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url: https://www.sciencedirect.com/science/article/pii/S0168169919306532
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saved date: Wed Jan 31 2024 13:45:01 GMT-0500 (hora estándar de Cuba)
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<meta name=citation_pii content=S0168169919306532>
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<meta name=citation_id content=104988>
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<meta name=citation_issn content=0168-1699>
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<meta name=citation_volume content=166>
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<meta name=citation_publisher content=Elsevier>
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<meta name=citation_firstpage content=104988>
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<meta name=citation_journal_title content="Computers and Electronics in Agriculture">
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<meta name=citation_type content=JOUR>
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<meta name=citation_doi content=10.1016/j.compag.2019.104988>
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<meta name=dc.identifier content=10.1016/j.compag.2019.104988>
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<meta name=citation_article_type content="Full-length article">
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<meta property=og:description content="Process-based models are valuable tools for simulating crop production, estimating agronomic efficiency and developing optimum management practices to…">
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<meta name=citation_title content="Exploring management strategies to improve maize yield and nitrogen use efficiency in northeast China using the DNDC and DSSAT models">
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<meta property=og:title content="Exploring management strategies to improve maize yield and nitrogen use efficiency in northeast China using the DNDC and DSSAT models">
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<meta name=citation_publication_date content=2019/11/01>
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<meta name=citation_online_date content=2019/09/10>
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<title>Exploring management strategies to improve maize yield and nitrogen use efficiency in northeast China using the DNDC and DSSAT models - ScienceDirect</title>
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sV5vfwU0Me+vGDp1wk18VUKtyM2d86Sp3b1H1x76cK70sS6kX8vFwPEhySkCJwtugEoMRomThRXtwhEXZW3ugYuczipjgNqjkG8Hz6t0Hzy3XlTSzmYsetrotRAEAJr5BBEU8HfdYJEZbNCx7vNFHB/mQ6nF1GU48C1X2KABHiCBRCvUICiOehNVEJ1VZAn0lXortgppnzOhTqnwrm/G7OhgGRUopRbwFdHHepq3QplWl3yRbi0r9btX+kFNZC01KLiiSUDnmgWpqwDyG46DScVGtxcGnJayud+zMqd6BCxcrA9dvl5avEuUydQt3xPO8CSUiGnHmAt02Z3ckfV1/4vIbyhRp3+nTM713b1VM7F4OKjlMlRxsUrrv4a0+gPtHv5tsmO7HM5U5nT2XJHb+YhGzLgoVl6zF3omIqjirnkJtRqNSv1zZa7UoDj7fKYmxc8rpouzYMaWsoUExs2dTIO6uVIvu5k3spEhqKj0iyaSpM1iJx13Kl0n3DWdnHJqVk78/WZW019VoxhxlGbzsUIqTZBgEulkUx77aWaWUYkFpSk+XY+RUPv1sPMvBny3DiFLeun5iSJ2SIiY8GZ8rPCqBl6kZ8Wa4K9mmlDamjHewSuPUkzRdkjQco3HMzMkBSTLIkTi4oNr9v6EoUGwJzEFlAOVCqVCVg09QDl+9KhuXAH/NqbApUkkGFwF0FvKA2RGMQLMFVSAbVINaUBqoBwvAGVAA1oNAQAT5YD6oBCVgFfgJ7ADDoBBsAYOQF8DsQi7IAvtADlgCusAaMA9sB22gFSwC+8Eu0AI6QR2oAOVgGdgGNoAasAfsBAfAQnAEHAx0g8PgKFgNesAvoLfwB/i10AT+BKfA8cBpcBJ0gHWgH5wtDIDdYCu4DC4EroJL4ApoBNfALXC9cA/cLrSD++AxeBB4Ah6BvWAzeApGCs9AM/gLrAWbwBD4G/Jyh0xQFGQbsBEsDiwFy4tspwsJQaJB3gFIt76/Aj9DNnmm/8lgqUS4GDKi1CRKRGOs1Z7aN3QS/Y4hYKx9jQ+EPPpiEjc73c0IvrlwyvRiKBmlrqyuCPFZfdEWtt3tUDvW7rUXXdSde3pv6UNDNFyGxyged7fALXbbtO3bxilLp/WUTMUMmimzfG6dbbN7Hj/oh/pYezQdE3QmPT4ujGcZOMZkfExH0m/phoWTRU1enZ9avLacYTnMrKoL6/JGYd1hHWmf20Q39n39Lts0HDiOnNnDOFYuCHdyL3A1S/5StaxYmpaONXItWEvXv62myFqfb427Zj91ILxbfCr/hD/3/HHRBA8FXoIrghFbgu0N26h7wb3kvm/70PY/0ISPkItdeCRyEjVzF/fwCb4kGhZVwycKaecYp7jOU57zju9i6epTjJKVq0qt0xWHO9YpznODc92RrclPcapzMZ0x1dg/e7kXe68f+23I2vbv5u7syV7OdIelDKmbtEV6UNonPbN5vHm/JW+nY7JjjGOTY4/jZ6dapzinbc5S52rnchc7l7+6fy5sZW4yD9lc2SrZWTmuF32S8+UyuZc8XZ4qXzH5XxjeZ2Sf0V9I+8KdPn8poPhH4VH82/W96xZvwfs/vRXv//b28e7nPcB7kPcQ72HeI7xHeY+p9a2dVetfu7Y2rHZ77Y7ayNrk2vzaqtpOn24+H/j4PXGP2CptMYzOqtdINUMCPXstXtjXCnuyvkyKawwuPvR0r/KwzqcbGSI/ewrZlApsvlmxjKdRlaS8pAoef1zNkvG3PZKxesWt4oueQPAMec4qw5PvZeQlVvB5Qh8xSSrkJRIirVbXCW5HLuKOrwhQiOGCQCDXW/zG1A1CkfBarOILkTlKLsYL8djczUPc/2OBfMVCXlB+biI19khWCYIkSkSxYHPVau+hc903lcC9vvUE721Gv2FkjROnMQudLUapUjxA7vcPJi3V6wvQqa2t0uXM1fTMuDhbHyjPjr0SqvOxKshhWKsbrRzj3KUVtpiUBx32dbLmHP3fBPJurf5RwN71aopjn38fc+51z4P/53CFGJJX4Gqn+TPEMCpGrfDH7ivXXlr+CGwgHtn39yXKtGzD1bOkKAxodYRuqDvyeQtRX02Au9Je38pffsO6Xwe07b0TZdsdr6tXUgLztJAJHDPG5DMXr9U8rFt7v9PyYMnpg3PrVS1SYmfS89nRAjpX6D91xtKu6qfk8iXT6qzKPmcXIph257e+zIlcmYIJElu+AbTTjZvm4iJ3plzavlLsO3JG3yiXE9VD47TbUfkJUmmbE1bzYMZ5EjB7SjqrmRTP+0HIC4Ra4OX+XeP31kPTheJs+cSDnus0bdLedvHYzwQJTDJYKLJCYN350lrWtiid04gD0VB986V5hl8YDEE8c+wfrMGLbbNduWPP1qR43lhe/cp47f8eeCX86qcsn8cc0RwiOy9qmhVdqm65fFC3N7ZnZW5hdnJJfIfg6sdKnoVIi0refLuDH3OaUGCQWBDxhV/xQTz/1+nnHpDVSbCzJd/9uO9R8ISPxaPE+/jlkpGwWk4VcO5JQR0KkQNxoCLerQ6q+ZvpSqSszsEJusc8IgghXSSr9DLG13HU6GRfi5Txb6Pwg6j6W7/PX903FSME/vrKmPlZjmX4OfGbIFKQFwV7E4vW0o5HREDom5tH3ztNT7cd+ikoFgrJqSZm3GjzZbIiSqluR6sduXUL3XXaeR+oBcy0+9mbpHZvMb+Iqt2apijNDgg8L2Asqn9B9bg2hlIVtFuTsohl6vxrb2lm6+HeDy5xuWrmSbAoFJIzgr4gyktzaKknUprqMvSaiOrv5bk8CDrf+93xLyK0tiN/Yp/xEukkUoSNkOLErHQef7/jxpWr/9vcUv7OfQusB96kGWQ3cMWExvHBnWIsEA+TI3VAqyrLZWOzqKG0+Wu9A0f2+dxmD+3pkcrqm0zmDqmYHNS/YONjiKEf+6A7csbXkTwRor3kh1DCANNuEz3Dqn0PfNmla28twI24Qq40bcDyCY2Vwe1yPJgMk4JaoD2oyGZiC6je3fWtZdFSnZoGb+m3nkAhQ6tF7eXbWtm3vpgdIJjPPB/OnKLxe8AmZuru03V/20Dz91OIbSQieB2oPJhM0n14xrsPHfOX1kOcLGLWRmHdPVBH7weTp0R3PYEDSDvu8gBGg1TMJjtWHBwPRmMVNiVu930xA9m/wAICjvMTpmhneCdWMTj1v0/+q0Jd1g5yALYChiDwWIn+/6UJ2Sfn/VsIZnXLP/bxcr9DvZpY0UQ3ly/W8P02U1CBFm3srX/1/XXgwvD3C3jYQrB9D+dfAwN/yljHEQRfbI3vMQo4l+VISK589Kosa1HESeFSoP20PSvmLKP6I5Wte77HBBbN7U0c2kXrHhGCCKi+/87jCHjjHhJt0mXF2RaavLF6b2HBP4baizs+QWT7pWOnlHP/XE0cbvvvwTXxI7nQ00AxJaLCElBckAze8cIykW553gvQithggBA76nf4u3vdj8CZWLB/tiRkC292Uo9jFrsfdP4LjdaFFey6jl+b6288s/ylvkwKch00kZgjSxc+XGI9FQLqc0244NOnai1k9tIP3lD9/BDWVu0B+XMKWxjZIb9MAsocfltAF2X9j+hKK9ylI5W966V9R4j2Mv2/7swvsOvl52Uz71/N31zaIY0ra/qj9qSezd8aUfRxNIqPeYXzD7sfQgh8EaTVcwzes10Spax7+olNW6MlE2HA1Ij9VnNwEC3ekKjD99eChbm/mXpiJ9YPA1AEqb7uQ77Euz0S9Ftni9dXaOKR6qjNKucAGhe28BeQi87tNRR0fTjZptKdCHihsRxDmqjPH/jbgnUbd+uWqu/NkpWDn2LjQyxVn/gubjNeW3WkDket1MqIyhqagzc5q/hiEHbLq1eWBuofJtV0nPszXg6qp1yk8W42PKQKHH4Frxo0l/654XrfhPQfzTgBPSuWjiePhhIkNT0UDkdW12vu3DtQyM6IDx2PraxwjwK+HVlozVl9OXAK+fm6NSfp7kRALuVSzQlwrr6U2EDXSqUvXLC2Kpro7A/sSz0uj5pNiPIatdxSqW5F9dQWPNVSCfz+pmF59K9uduJvJ/D/Ml0A/ik4eA6C2bjLI9gT++05DdaLC75ZQ0wvMifCmicH2abOOPZZElqIdObLbxdYZ7Q+2/Gbzc+/n71421YubfrGoG19vaZNyc610MU1nY9+t/77xKMLW+RU3V4Zj/PqvLZx9TKtWZU+fpgj8P38inBvPBIcSfiCpB5/ENYJ9oX2+fsQmQ0lLPkPqlxy79h8YWmGJmWE/QGSGO+12rnVF3Ub/DQIfjfNttI/TWw1rREiVUaHGYW5cos3su4cXKfH9pfzswH/cAipy6DpfV3NTHoXR9qxqrV7F2bATYknF4Rpf359h9Xs+0WNfdxWZ6p5pHdErHHTfxxqvP6D5cmixkH7rRmB/SMvIvrNF2K3BPH13I1rVPvz1+Ol3SaLxicD8Ir1XbPyoHUZlotpySA5N+lLoM7K9oMOkr+H/mWfrFr6hCPaO6ImOHRFqnY7TRl6ZEu65Tinhlr/+bXtvqkMwWY++pKVSUswLQKWLJH5U+fRt67b1DdJD/noct1TvQzzR7E4VM6RzR/p6nC3g39+B413Vvsp74FzRaJQjLm3pEW4EmnrRJ/qaq3rEX/nLwVr4G4epauwcJH95zkuO0OgARozX8ZVjBoQITalJbhIv1zwmY4cNFCOj8yrpT32Iu4nq9R9AKOiL2zHHzXQ+N1Z9bprOnmnFMLvfbzTzcCLjyt/8LDJvbxGT9YUb9+z8FE/Wb3n7h7Ji3tgoVsUlu8uq8uqRZYCcAE6pBdWMulMnD1eBNy0zMHkR4LMuIDVwu2MNRk1hOJoj1CG15/I6kG9PBdC4SgGLPu8GiMwBI3k7WKxLqkYPIDf8EZ4mg1arkwOnhxXt1vvamTja9xpy4xgz3N60kFfiLnzMO+U0gNG9ZEeXue53hNmIvT8+t6Z0XRglfV0QutoGAbYE6Ck/rVb/5mTfzGPwWqcbArB5g9ldTcZHzrejEutStCwQJriPyuZzX3vw3xreqDc9wJgVUxqKCXkXdLBXL8c29RfKBdmxroHQZDGM8KxaqnUJnBrXaf3MPpobPFb/WzxKQDB7j+Dj0oL6okIJuVNgZT8gqzgEGrNiozrYuYRN+sOLSpKSvH4R23Bb73+fHD0rWs2GuYlbLTuq6tHBxFfF0WrqQsq6BFmNlQ9CqXN2f6CojKwwoYfUS4NbMhvbT5hLrrCNb/kvnX3F8ur+S0D8q0Za/S+b3vRdV8ZXcNkZJ1g9sTeI2ErWVfPZTfwX9hnq5d/1ToX28WkmtHMT6o6vmIdcj/C3z5guF4/PUhXZ0WsIaWIVFLe48xzpJfCeW0EyNT5kXqmbr9jIEPPTgLl8PWMkm+5ePaWUJyfDl5BaoluaocWbcBzq66eice8mvEJKNwGsmL1mBw97STkXUJC1nAoxOWkxSJBUpQPimiflGhPaQuDYW3yMosQzycBaSLS+f/ZFfHxTI7NQxiPpHJ5vI7luZDW2EXZkUldWB1rYKHwTAmIFMHkDZnTo+nQCpu2YlQX6uUpnqU9y8dbQsl/7K2JFhMZNns3JkNc6pmxAYtFzAU8G/WlJzRG6ja42ti4Gk/aXhE4vKsn+Ygk8HTE9Lt1jkY27oQnZZ/8BDf0TMgcDoe47LRoON4DV9vdqirs5RhGEbRpfb1Rq9GzEZHAzmcXMc40iEt1h8Ncxp6gP0jARqaCKFNIixlJ4NjdDqeTOCRvtoOleFuOclxRtnwNOAkZck7tZUQzMTZvL1nY59iZQ9tBetxIEcbsdpAPyOdD+Ajx6R78PTwzJej3kwKGDjuTF5nT5xTlNlZNi8dYSL9xfS2rQd77/K87pqlQLZfvgW/Gb0YK66TCfxtR+Gi4MSyptMoJsYpy2Q8iZfxjFL4W0TDUN8orK/NuePiw3F+KCgc5lHs7ONdp4s18GqNlnEvbPFwBWBE8zIY1zRZUYlNAl4mvdCjW5Vk5UFINcrSMHm4aou+QTQpDP3MvXQ23/ojz3qLaWdUaCa8Z0JhbXdXwrRA2LyxathDEz2GGKKjHvyXKjEuDdZcoBkJixMfTw7i5lHkgPVpAj4/VCRwSrpj8lW27RgI6qhLH9RZD8BvaciuDRqvitbyM7ZdJ8+wLuL5sLb192VoqbcVa2x5sU6Ndf+K8J5E/OQN5J6ONFQS1xxoRSfoRgWLIv+1z0JrPlm7V0eESSi+CEskeBWExZ+KmxTAPgeTQLVOGbe2trvyEx5miaopuKsvEGkvgb/5EV6kxG34PsekJVGYebGSyc4GlJ4f6ue6UDc1kTYum7hlu7rM3aUKsqwZxoogJxRiHqYOQPwi1o9FwlKT5Q6MhlOUbNkZoxQXgToOuLDo5NbVG1X0wHWt/QRjU/oD8Mfgi/1W8DtstW6HqLkGCCk+RMmRWg9JjmIwC6U5iARYMtMYvo4kn+s42EbJbaQLGk2QmI1f0l9DFiaH2DN6th5IYtvYYA5/GJ5L5a50C+V22cKZ791rl1I3zXTOnT3XJzhKVBrsSFOxAeQGO2ncYCey8dOb0yf3Wudnu3avO+rLG/y+A8R6JKheC4DL0lMzA/ouK795vdK+sdjGjv2tRdy9DbJ4P4UeWDHlFIkemt2hJqOdY4qP2LaDo/1huPsdWZEGRsN5+KzqyO1w2pZw0Ix9BpYGkhumcD1V8KJRNXUF6Nfwnvsc0rPKRUw0SsoX9rbFz0c5qmhpprMPlctjFl+xLxp5F488N1l2knRKVfowJmpSemAqcxWiZh13dYEtizjFBMgqlwBi6Uqpty+AEa5+GOA3AzqiF4cDWSu2B+LNC6Iq3zpOC/pb4pQAzGemKv2dJjYemcGz+TFaLoek+VJixAn6jOIXkOVT2VCyFTiHGwWfkyVdzFOIOGZikbTiGjUS+60FhR3v0MiJxfyJSNKnbPgO18Vz55jlKcmmaS84cq+yxDcuEENa+9Sw/5nGmqUp6t2qfjjBTZ24MkcpLWcPGI2fO1/ed7CD/kqhIFn6Z9BrlopQaDo2EUV7MoY/SA71gezTsLqKrlQt7AcULcL3D9bdNNbz4V9NH5uGySkbXb7moAzRn1Z3Ak2R1jR1jYpRqjxIF9scKJhqdcGfmOZyBYwc5gval5HhV3yNXDpkGDIMWccBdOieNTO/J9JPA8pb6rtetK4Ubq1Q9DE0dBA73kawOPgFN/Aw/xSjCLsozgCybnCIoxm1y6YmvsDm6x6W1n2+6+SbMqy/xW3vGZ9yxELdXAe3MEQyRxyDAS4QbN+QuR/KpNJsdByqMPmJDU+0p+w1gr81vMHAutce2YSJzhXUxco/e2cbGr7qTjvEv4mtP8hX70odYu8foOMfGr3lSjsk6fk3P3fHhSITL9SejYbLfjanOwewbCpuDFr+F3ePwG4IKnuqJ6Am1+AIvMyZGUoEtNqV1VB+W/Yze9ISHCDaPyCj5k74AmxoPxsKRRKqmSAiV1ImtCrkas1knmXUW1FGrjXO5bQ4HidDdRGoqYsJTqrQh37vKEgcJ34HU0JEaSMTY3PgNCAI5VqpjxGb3wGBx2sjML834pRAXckcjXMZouCPC2WknSmO3wpo0xfVT9Syw5EdUcnf0ydpMJnZHLqfw8UVMRBrEBt0pzGSNRgNfNbUKmz6XVbIEKOkm9241zYHqERk24Jm5PeWzFmm8XxVVTIm3Fb3ZCocMqT5uqXOWROYogrbR0GgxkIiLj5RGJ9c5REqVSYlMgRSV5aPvpYpvSsRLXGXulIqXthhPvMhRpLTDqcuqf0yMyXjGKZ7KXIqL6EIOcdrvUA2c0miuNDF2YqOFrJ9fNfE8L5XOUJTjP7XOqj9MqhnVRgcj6r9rQRVjJ2pC8LGH3ZDmB0wJe0gcG3SmkzG4rrad15bWkLWzrTGBlyyezNyOL1hKPeJ8lVfbXbRwaVlUPt1L9pmKmPuobG6Fw2QSr5U62y5wlCGFeIe0SrK5W2V5wSWewoBXG5+dp6dXYrdbLZgr5UbSgQDJCO6t60aaWB2CYAcf66zirbjKxiyChXKlVoE2DUYyHfRQU1tciuaf6RTQefR+oNBwpJzMIfesTblJHevLthK6cmGWRy9SdQ4mSQ3kfZ0vIDXx9UYGu9BQ/syX4zoPtZKtcnuP5VnxYlEk/uCgcF4c/9/tCSavbdjBTnI59EoUHT+StNGQMyDPZfNr0Yr1suZLi73+6DcOby/Qqrr8m25ecBnfe/6Bz+kUPhTzgRPqM8KxEup2Ak1BWhAAwiMRZwGdrZxeg46L8nYnok4+j7Ggx3Y++owQDs00ZpsgN405ngunkcRRoDpwF/HgfQFBTctHAGQXgOGEYWzThi/UrRhNXAhL4qsZMiool6WkgWtgF0GMWkvHKn2sRLVlgu0lsjr6FJ+5tFoh3MOM16EQMH2VTkrpOsnJ0KgUdmmeRLUCdOIkPmeoGyBvCcO16lRDqwuBh8VfxZCdvzpwLOESBH4PULw/5H8vCn3jZ87FcfeHu0BrGaRr3kZpt0mQvjwvxDDeZJt0FRNJd7H7DtbfNZUhFFugMf8jz5U980Vy897k+zL1myD/HNEspLvrnmEZZN1ZP+v+tRffI86FUVscbHyE2v3yqR9llHzAtrcMpkdfH08UW/SkPmOem1KcU+UeRGQ9uwupyGx5A777kMeVoACwrCB8udN0NACvgKUtJD2snwxgSS1Wbm6Q/09ypNQpsjMdc8XEvQUUcWk9WV67cOTtAX1ef0zFBVGxb9JibxV8wjlJsrqELwbOdA7FjC8FzrNUVJGzKdSlDGPqnXHdKMfgOqBymk4a5N46d8Zvjn35FRO8wULkExwGxfxPDZpZgeRIgsiwNzDJ4aJUliSv5ujeBwooTOpDxwbgmEDRW0TunSAn1mvr4ejiQQhpobQlAaLSh0Mu2u6UklI7Q0rSRd3CC7ELvm3p7uCCl6OdB6Hk4eElA91DIXqJdKRB2JcR8ZZQCY2K2yGy7RCjJUxhltcYokfmgA4NXaJ14c1Daxkua96H+Qtm3WXhsopm14Oxb7mkY+qN7jE6PCCBWjtmTKKKrdFvb5qw/BmvCXPUzY20dunx/EEv0n7SiyJJ/+ZjqDuuHFi9Rlee2A1v93p+p/zW10OppAW4ZA32PISkk5Fks6BZltm9mGnrlAYHMO3YYFlNWwvv5GEoIBsJbaihJlQIWev/KRw+kD0DKu0graPqhSKYMHAFaz4qzx25dPzlI7L+8+fn+t+0feopZAlbh3U6zq9U+ai1dGwvbzjjyZKFXCt4iypx/ByP+suZVqfpYLKa28x4wIdY6wRfhhycfb+mCAtpXvrAIdY6uq1TupF1rbK+JS6VsvQZdbwHHE6cGYF79GsLSwQ02aYutqi2Vfy3h7CDv+mXCpDTH8Bn9pU03tPq6icJia5hB1lgHxjo5ez5+vGpycx0kSBhNohcrTjHUFvVEo/U2lIyZVmwcdBg7kUR20dGtZTobiEtRDmiRY6dHnMfzUp/rbq4FNhk9+zz5kpcPXwekg/40tnUbCJF8nIKoQnkznaZwvTELBARDWsD8Lo3vUQQfjgmuK6VBq+wtnzDMnHqtyV1IQ+ObZ6MSpiddlZdgbk9XoqZX4CejeoQCqtCsxvcqdhZxRK529H3X+PGgsJVnZFGtHStXdlOfTsGDOjwFTa32zsauKVZslIbGYOpDel4IDjmZq3r6PTxSHGRto8RRqGdTQ8499LzzdfuNp269m577bmjRE1zq/g59x7cw3mniVMbC5R8JhswGjTx2EiBlKPhNkqcju/D6R45SWUPBFDuDRZ/QHB/uYia7Ey0rtKtwaL2n4XdHTCJ+XbEIwIiUno3W5C4qL1J1zEWfgaI05i73Rug+Mv0pZbPN2GU3V9f/806K/EeCatKlmSFO34ot5iP4E0gpzSBKvTRT37ZvGZ27s47LLDJfvD0D/qNtb631FZgaNKbnb1DrL+s/9wxbszMuR/5W8EbJpxQRA19XKBa066x+OM2suq2oG1IWawBDVrQKb14dDcMbDg8aQQ0V558bo1uSaynYoFKlq2bGb+2ZyHFjOr1xq6BHrJ6DI9WP6WLmqJsZLYl6cyI43M88ThXsW9ubJJU27d6Uua4maUVNQS0YtgvhsnIqTMhDM43Xj7E6NXHN0xHbv9h+j9u3krJBMazUilpmeX8EyvrePi7ubXzeFLfmyPujaAYeDFpbvqkIO8/hvxJ8WISzG+fqcwuEXLmxydDWowaAF63dUcOqESFUuviKHettTabzWGwK9Wbmeb+JNQmJtwpEldoLmjj4v1besIdaNY3mqYu6pCSqhIr01m7JkJSXT7tALdtFxMgc1kHaeCOvv6z3JQz8u25tYlylKxLyWrWkTtvNJqnpIKA7c2e8vrRKCkh1u4yIseOVCEbCMeilCg0yLS2CkjqrkKMZTfYJh3Xekwsdeiv1agCzcSFIJqPY4Dhf9oCIsY9IaJq5bIY+yguR+FIM+j4WQ5HULpAhw3GZWAgZG5pa9IcMUQtCdaVY0ub86f+xT2UWoyk02K3OzW9wB1zVdQ5UhzaEH8ZUfIW0aFGeUdcF+elYK/TZCPOnWALH1HAOBNNOpjgwsyVivx3BObBYLGTH2OLN8ubiBNOYlLOSGS9IG8my9BC5FmnDQrwprG6YRT4+jmySgv42k4wH5jqn4H6wlxvWCXJz5Kra4rHFpeKAy2TIV6/pU1ZWiHq0CRQ0a4gFPOlfC663mSPKEqmMrT9booCe3pTd9TAUmvjZtzDX96b3v7kQBnQqvaFg7EJpLWlQBWxeuA2T4EzrRl9LQWDGOBrTZzC3dhVR93X91q1Noeulc3HcMYalnGTjz6iRsaVf+HtdRMeQ2HmZ3Ih1YoU33s1pVLyo1kosNF9cNW6fWn/7GLPUCrce7LmZZf/NQ9H/OCZXEB1HiabHH4XKDAHr9+0qYHQk0lVDn61s+oqUDP8yIZpZs69eFYUNVQYl0jzhGN/blYlX7DMv9A+Y4eSlFNdzlzGlqumE+C+pKuZ8/hVBsL0NwjXPCYfEshEbPYDDtibrSBbiRvHBsUGtLY5Nn/Slb62vBwAB+s0m/5mBevWh8uwpbz1WOX4BIQNe8TPOAvUB+AOFs8CRcuHInkB9Nh6sphd1jaMxUYfaZxYy9+GtSV2zPGJJMfY50Fwfqs2WGqP/zFA7C9np0cDuN9VDx7TPB9weAY8Sq0LYHggeQF5O/58/IoayOXDwT0vmJPCXrTvEnfgnizBuaNvhrD50nydMj1eDGaiVjO6DSPoHcWJt+l2vRfF3jchvoukLuEkFiYFDrvtRgICJMhmszY31viOHlINycTODnNLCzfszMzvbrZGVm85b3ldv5OSe82WkgzgslFa2QqWXFAYG9Cs4Nn36iPLpTV69c8DZ0b8Izl2z15OJkncacl/tfpG5TKeNqUCr9vU4ToYMB+iD9cXwdxHd+vI7y2fQEIfeRm86YCqt0x02PbWl8lX8jCmr5Hfl7X/IxlyPziIzuiL6Qew6gbucbdCiNIROrYm3cdK0tvvmDgZKjPu9uAb+pCnZ8nBqBdWzoPXrzwLo/2nnXo/ctTw9bSI9JtnPHS+QanWslsjgJ3PKWLsGRCd7ozGucw9YX+I/IxKAf4UDUrdbYM5MAhwRaArkhoq1Dr4hPrHXve7lyH4NTwIBapxU2NMdcwIQftTp58TX6c8PLp3/T/LJI2n6VT6EzEQsp2qQ2T//fohenjRSl7vo4fKF3Jw9wOy9vIjXetJHH6pjxwKL+bhAzLx1sja97vce9AD/vgDeSKMbx2i20azW330VLndi0dXX50IsOsjpw6JWCIgmw/NYl7WR9f6Uz88PpBVLvHQ0olFyzGFDxzeRSXLMJrfdH6Bulj8V578tE+qde+SCTBoLWN0zbvacOitj5DRzLlD/iotF4uPHiefdPD5H8uQ86w+cpx82QXxsbyby3rvjnQqD5chPLAdrubDtfYPZe3LQF71gTwULg7Oe7Lxn5HPSAl3KOE2+AkpaTq527VBcKOPVvcnQ+ABtzVPQf0lwlH5Ln/9oEStUFgJpVjP4qo8azvciO3M2WpV45llPixjuf/8cU53HKxOrJIkpLkDwwExR/k6drlt8pmLh0CJ//HkC4gEX33zxVzbqolDBz+1EO5wv/PTyMyhN/2FpIvfTSTrL7y57aEXBN+d/cNVW3XLwR/9ZAF9w48zmqzqYJ2KyZwL9hCt/Mh6kK/RqceJEt3eDu2CdriA52cX6Jpn0QscmEIko855oPeS75fWvXdf9tHVK6LiOITOh16uGBGsfJvXooRcIKUURn1yLHHZafsuTBWvp40Vsdrheme143HY/e53oUYt/Akg0WoNP2jZJQm2nZRNkIiEMtmfenG5JihrTo1xh8KpbJxkBqLBRGilUKOFv32sNH2dez7wlKAhXJ/gixfJskZj68csHqZe+pekgvdL19LLhQIpKM5Gd9Cto4MXuk1K2SDdoj3fajQZFWyk2jHQxy0oNq9LSZ45a84br8lqyjUhZ8gk1mqD1lGzkVvRrzrf0QPTh4GLEOPCdecOgt2966DY1P56I6aG4MwhAqZ8sDEDyGcI4mF95B3wsQOqD3TK5czeu30b8/Ah+PZaI5MEXlp7L5JmqI7HQEsu+fCFktDYCi+yb9Su39gGPODZI3QEia0x+S0HUkuTIe9pm9pjY5813IXyZ14zlhrljbR8bXPyNBo4dsPxi5/V/SdynzXOA3rnQv7KdbKk9vaWh16v2w1aiMOSK6jR+ZqyezRhZ69r9UDck1tQ+0uIU5TKViWYTGYStcvo1KAA+9QiqTwWb1WJU62fSuKEZJj9UGFTID3GwlTcGNBpOyU+Tn299Oour9CxVr8V79H3y+R25yuwI2zqaFiNNUP0OEiJNT56io6hbF/Zj7+PU3ScWGVQE9wgRsq09k8saSNGZk+DsbxmiB6T/HlNFZ3QX1oj9f68/zpAf5SH7ZAthWoBZANrA6mTX/ZcY33A/duw3VkVo8OSH88LeMV+2HF3w21rcPPGqqPrt6tqVnz79QyhaN6F0Oe9Qnz9otNnrYZJ6rdy/cRvG2sHJhsj59ncsJ5QCyJP7w8onIJffwoVgn4CDTU2Uk1hg2Uw+ODjeuWlCtK1Fki4pnnTLA6ZmCAn4tmQ37mkwzMfPb7IZL11Hy7fEQ+8pSaYxHhYA1e+BPkFQrT2z+Rr6Qj3WD4UODhcBLXRUUxIgo8Z33lLEo7zMJJ0ugNGlBymUU3jwugoVwuik8sFGGfJDp+ijyM3+7zn2wfTyway/S9WYp43m+XgXnS5vbJolKUIHfLQ2eIpr62se0NwLt/YedUL2t9M7yHy+9D2xefoqaszappOggOr6yNnEOk+PuVtM6DId+0SVYfqKQYM0TOHvAzw0Y1zQD45wAjvIx+WmeXdARUR1sAPoNbaP8qE1GHEPlWlG4G1yorars9lbRlmXfXRi5UajvvwReAKGCA298CARGVD5XfxS5FN7zwdxSX/tH4hIEL4gXxQ7+JMNnCR8LnUdR6Qe4zscmksOicbF6H2DKCRuCG+vSb8IH02SwBFKtbpnMlPUZ2A92JvLMl3T3nJD9KYRoBLITItkQb7+3R20tP6YY6UU3TZmndDDu6uXZoqa95L0SMqKIhG1eZdwpx0qrFN1mVTH40oQa+f0Ja3iAs/FEhNVCDiym8J98TaRwJ+B5WM4oU/cAix/2GQxbrXwghxSa1Ah4gqqk8RAYDX20T1gcp2vqlNe2AEcAKDxbSrfUfPOPnKIHGUA+1Mw6pV84cRIRNOCkyiXZ3x44OGtssHaEEQmLe43fWm1983GmBp81CjWNBaVceraH7w5FWqPbhUOb2zmLCf2iB4FsqWrOk516Pw3VlmDT0OKmsmpm55NRW11lP1mMy3Im03TPTMioLH5OM9eBmonjIxmb9w4aGuyzzpIr/8/IEzb7AB5oOfan9+Hc4zWVPYSnUL/8FTl399mW2939AzT1lI1DIcxB87rvz1u/UsoBh88CzIHv1nUhJmfFddb0Y/ne2n39NCpKwGHNiXD88goKfHCkFh3kGPghKznGn8WunlD4+MGBE6i84Kaeu+gCZt2ubR+7kPSm7bGksWJ64h9Snlws5ovmvidZOqaVBEP/ejpZgUULETm++5qN6ZrxHhkieHRAsWpj/mo2Eds/BwD9HbYXWyUeEmTwNSEc9xX5HigekhNBiFxoxGI41h9F23Ks/ZPUw2fkqA7zqL0Vf83U8+sjo+qKqo9oseFasP/PwIcW+0CE7gWcxjLU1PKzczuUC8RI4fAIek4DDYFHZH3rXgwS3p8Hfh+4BDobK9jKKjvX4h1Ja8Vu7tQf0DN39hnW13dFEPasormUWURrvEbzyn3P0kPsRp0PSLtfo89Z8OA578mJArFO34zwLngd/fDfjdsgsTxnpWtjNqiJL0KGVIjZavUuu0dEu4qsM2IHa86jkueFjIStLaBu0jxlzLmCSrQU1i4xTVX1kUHeMNOuiOPJDlsvh1yLHTbrHQyEi3wsLZsw1JC4nNKtrz6PDRYKFC9+dMTSU2xO72gvehsMBB0rru7Xi6e57srnMsTnD7klKRCMlIC48EUGaW1x6ieTuAKyvsSqMDh4KpGM3K8ufinDpIHloON+LhY5Ox6x8nRl0i5bxLjhk0XXa3sYklnqxrvJnv60kxdtER/SGNC5gmYpvxUAC/2y7WsS91mvg2fng23EP5Myh1JSbeQcfjemcVthpGRus9VKzwYLGCWwm0DIkMVpRGCltCfGQJ6i79cDZsh3f3J1BBjAhjyXW+wN6PEqhsPy7wyk6dxa3fVlR2VgZKJxMka2WD/zKihK8nkMQ2VgZIZXlWlZ9qfG2UT6/h6NAqqFhEqo91/OSseqI8FMhMhpM0Wg/2zqkP1COiDg3ya6ZOsatyyehOVgh3Wy3d3ze2liap2fGRNCrNSsHoEDUJl0M+00+P09W0fFWab2BqdJOxrtsY6NEbGISakpBkNtK7rcUetYE5pBvpI3TqN8gOnnNkMLDdzu3k8VCTWZUAKsWzVQEHiBup0hwHv8BZdfmtGU0kYLs2Oj19KGxzcWM5m1uYpn5iqP5w15OW2VWX3m6oXM8kiUqZ/P80nfvVcz7+lgdjoQfn+vk3fsIM3UnuZxFJGmhIhnCtdtd+v2HNOvoXE48Whnx1FmHgGlntIdrZ9yYLpu6H3xggIvD73Nw9fzyV2O10QW3be0Ik7BxH58snNmmW5xd2KqR8adDoMNjJlhXAzZhz1IP2JjR3TuaPoNxM/r3tz0uecD8+TtTOvVfiFGamCBILucAt6m/TtpL7J4j6qdxPx+ds2QF5iNSLmqAUuEFa+ta3lWI/S+bq+kAjmjLLpmumuf5HokcO4bumRTVK91gnD0rTFcLq5aAseNx1yKwEoxiz53f4L6EpS+zaepq3+Ejk6CHcQcDGqTnjaymSlhP3pUr2Fl3P3cns+gZT2Wk2xpFb/yJQtCpu+etiXQvZBrWqbX892HGg7KGrCfoufZC8EhNb/ax+KiN6kvtxmrvqrZtvcv8fSG5mpKHpXZrkdlQby6n/3XLJAdr1EeONmEhmHqMco1dIv6etd/FgN6aJTOMgGxv/6K7T3tgKeZQ2yDMhzTd2F7Qje/5xYDd9uzGZjZAqNyO0+20kUUhfnUrgz9bqvav100mP5CnyvVkPdPddR+e+0q/ubIh5baX8NOX2/vV5sdaZXRoFoKG3RfHyvZkPNNS6onPecm30qmulBWqdEKfdawbBoin3TMUK7v5A0VpG6Zq3R2WIEhRun9xYg9N17beAMKj9eiYfar8L/VfxOmxXtoL4f+/RpHHJ0adinhDm4BNv83rngVsPR/yEyjfaT3ywvtotJW22DvWbs7es3F0i52WCowzphrSzu8vehhYsG5/X013Fw9v3LPjz0NfmJC6KCs5BT4i0yNNn7SFxO5wmqzmprNH+fHkYIFGKyCyby+hGw+GdLRopBcxoK7lSKJ4jhbkTY15OdWXtn8riA/JFlJvEdMgIsmbc+UwmmzSfNsrajWq6yvymWV8KbJkyZHeioJSh1+DpX3i/KzGoUq/0AUVQCupsattm+63HfrNXbusGH/Jhf+efMZNYMpYVNjEtXCIikiI9qqI2GmJ97Im/4yNOkTySf9KmkJScstKedD89TmPpRXqfJnD/43H9gIMhwjHq0OEgxArXuc2WMzQivhgIhiZdd8dPfjNkh91q9Jq27Ix3vMioCP0SFZ4NzddWXwp3onP1pEjbSInKrbXdjZad49Nzmmzq4xx7ed+7fz74iS/ffOfdazbt3Lv/5OXnXnvrm8/ffvy+sp8sLe3bJvXJk+Ob3KlNlcsXevoaCKp67Kpdmaxs3k9c7L5hVet6X1dYL9mI5hSGJYQHk+ZNgbQYBGIi/6R5p7W9x9jDVx+tKwbmFCuRwqKAHwNn8qaFSyfpxVEBQx1Hx6XocIVbgRZuyi7YaPz+4dIVDmMSVQyVKVQa69OEekhGSFKR04q3jC+bdlDbRR0O+3BJcR54X9Z6/jZCdK3lQ3P++Z/pvPobqDSpjbcd7R2vr99GC+fPf2WdCSteUOrtxoaww9pA9ZCtEQ0LvMC7uGw40EFt7Wgz1wMihFnHr/7cyyVrZyJCKXsBXSopX3lzXIUgJr/P+pN2E5069paYuMs7JKZe1dxn70jZn5aPu9IgXrH5puNfXlB2Vj20qTjjqh74z0PQFz+Tpre/hVDwm0+htb2BFqyXDZaV6P02lm1K6YLmVDOLKM+63VawRKdW9KFwW2jSQCPHQJ7dGlChAHl3w+m17u/9eenEL0/Rg6vptfIDkDi+Z/ZsbCydicVrioQIO5NnAJk2PZ4sbHZ1urXOejZ+w5UxrTzNr+wZn3THIly2AkoKrhmpn8gkK2ogQswJuuE80HFChj+Sl2PCplxITXZRNEYAPRXIQ5qOoZXggQv9ZYUhdFTJkpANLc0jbVkbUPkMaNhFsYNQ+TypEn3Jr56uCcyZkuPcsfH8xsKEoWuZlAyCIt5SjPGBzBNtwSZEomEzBBilNf5qoa157dGhiCNsYtOtfrORW96jfrW5B6YNG1fDgSJMWlFvzNBaPkmq9zA9rQMrhRux9OQYO6YkabPszEEdSHf5izDWbSNkRk1ERpOwO+QJBrlMfzgYIAdTQJnfKTt6iBtQce5fodaOgBurcg0JBZupD2i13OQW6cElytCYnOyKfqxDvGWjZqPgIaYMS9o3Ns2rJlsTg6ne9JNnavwntHFzRuxKujI57mrVzFv1Bcu5G9aaSemCIkACQeJf+K12UwNCcXbSN+NS+LCNcK6oIMLr76PagkxCYIm9T1g5kYlNZSQDkeBJgXW6yKHz+Sw0fFQR06c65msCRScEPwG/iVF/xS1N130fsywIOfw7WIB2+GsUSnMXHfRXEtfHBRwWVf60cWhwF0qulGwrPC24WRVRYp5LLL14KCPlw69iZRqOM/p/a29ZiLp+3XUVwfXJdZBtilgP1NGUhSD4JFZmYTSCpKoCPoC6mMoeJxhY+Yb/bza/Xq77aSXdE2idA8sv1eteQOPq09ys6hi3N6IvdEwodtF4qFR4smopt1431GwghfURH3ixd3tlz6IM0O0SDh7UPaakisggcElVQpaE0FEUcX/Taj5/6YLKsKSryO/ngDx40wzAv73QspIrfGieber/YyYsSEiBpCUOSa0cLCtIoNk33wYi6Gyq0G8nWW+FD+VCTxP5Voouvr43HgslA2xaKj8ygeAk0nfPE2EE75CO81phjWjsczabIQLDEPmJ+Ma7BEU6+4SqwCqgjqJQxUhZYXY4dnGi0cBcgkEI/Vja8sMF/4o/6qtR2IRFsLBHrVWiEGUk00UPtXbG5Wjx+U4BXUTbA4WGsmIyiw5O25Rr1LGyYCuiCzuzPFpHFd4AGxYimnTRYrc6zW5NCP6eLvSViCbuIbR71GRVxNSvgwOFlH/X65FIezzewuT9aElZjCMlt8RFWNmLDb+MCVgR+geuxsoa3L5x4pIkES/ENiWFQmB0CdsgChuw2SxOyzAbaxs2IiKJ7XWT+XwqX2BPrM4KXLt5Me+0gP1ynuyyLKFezEnY+61Tp9gLLfuwk1+pSUFQ0bYvVdzmCWks/9t4VDEjVh5gu+Klr20zAH27kwUV3/aQ2zkl28k+M2BLwKzigiSKXn43ycBC8W+ELbOL5nUEyQsQEQ3/8X8kqp8kkXU9C7TH4/XTImV42U4PlRywM+gNTXEKZHcWVfGQMCcdcuwiqHJHRoVTcl4FfX2jZJTupZA3NCqm96zKVOeobbDOtEua0ugJe3Qq+QIFQBsvUBTYqdcrzBo3m2jRDauQsj692EbsJWAXWBcdCFqbAsFxieSHlWDvfew1V9AVfp2Hp/0iUfBG4G5ujVyo5SjeowXC4IuHf+GwOTdD2qAyotsRJnluTCsVTf8iiRwng2Fc6nWLcroDR2fuilEUPvojMjqrlOeF+Mxb63XlB48vW/Cy9KNDV5e2xyKZUo4gWTyZMcioMhJxdmaj1250kBATUJ6szeQkwY/bJZxQkGSI64+c8laBEmqFkdFgThkdqJSJJyo9pSeqdBGDhegxNg7RmcjCSy3Nm5UxWtgAlAp6qIqy4fbnS4uhsi0B+q85/e3+Fr/5DskvLqBeU8LEiW4dCgNPotWgQ1WWQIfkKaXtgvLN5Y26VxrBtqVJWP8r815/f/Xyi5ezL/X7cs6ZJ6wXWthXtfuwpSEv5jSHdBSYlcpIX9OJrkNfGU19D4NRGJIU6l4LFeGvBAtFMx2VWw2FrVCYE8IWarPiMZeih3RuFRq69iY4ikvtYON0areEYYrzjCp83Kr9qg7FdAAqU4Xce+CA/uBbrLquGBVMie8s0VQH8PTlq/4l384vEXAU8QGhFKfMkg10XeLWJwcqOqJ0QaNIm2S6KYchhmQVIvLcYSgph+wTJKZiKGkyYmUfjvGEdHrOT4pUvICVyBGssmY3VnIharPYKVk3EmVIM/0SI5UVSeLCDgmto9xHxnCnSSED5bcYkzdNiqmM12BGqmx4AKAHotp7UhSlLuYvqAEbTQIqxcF3L42d0BxVtFNOCeVCrOQrQACYE3CBXH9eWQxEWO+PTE3trSHXXpdvH4Q1uF9FnwWi2u1Ijb2IIYtRq7VPtzSmCZSMWpurpZynzKXeyZhQL/SmONGo2LjoTfSkTMb0LtPzn/lFKS+7qri6VP2wVZ9bFw1U0fh5w3iomr4ltfLW2JpaS5d3717cK/ut4Tr0o2V07b3jzEf62nji/2qMkybIWLJ4m6B0bMVb1Je1dju1PVm37PZte8dc6t1vMIOyRkYFQGcFQ8QOhf2ML4MTFxbkXLEoeeLy5osvkJaQ7tklQZgIEa44m06TVDAa0z9p6fisn/5GSX935Mc5dW7PDQ3v3ywdcp8Oe0xH6OxRhqGYF+8EVmMrt9/anAWCjshj+oPJsRtunnm6zEUf9VGGaqfsRmFLNWoSMG3ETMPDc4bIje6q0/f49cTB5PrU+rHpjZm/ZpfmNifqiWo3ytLGekGkimCv3Y8mzZT206xxc5ahqNdg6X7qi/kYDSM97S6efxGHRptjb+Pld8NpVZza0fy6baPPFlNdGn+SaQoXp8yGw3geHkwReCyHG4Kt6uQPlaJ3mGP2kmO3d7e83zxo8RwMsBA0WclUywa51/yb+FLMSQqXUoe12wNt+EHlddU38N5pBNoiwOgKMzxbltRZBumDnbUuRo+hOgcNsCEvIRg62GDnG4NES/IyL71TQwNu6EflgJzSaSa/+8Q+n/Z6Jh7RQ/M8h4AZEp+G3ni5EAPuh2+H+uGWkNVS1WxTGpRTtVzjDi3tShWi+tObM+gleET/tRboRDZnDLjAHvE74m3HGNIoviDksvDitdRri9olvbIyu7g1utNnAHaotFMOOCfOL90xj3yiWNEHzDW7L34XORCXS6xkX53D3/6t+WUd/JiY4dFKk2E0hXXwNlGN04LZKMhqsU+RYqVS/Vb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Introduction"><span class=anchor-text>1. Introduction</span></a><li class=toc-list-entry-outline-padding><a class="anchor u-text-truncate anchor-default" href=#s0010 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title="2. Materials and methods"><span class=anchor-text>2. Materials and methods</span></a><li class=toc-list-entry-outline-padding><a class="anchor u-text-truncate anchor-default" href=#s0055 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title="3. Results and discussion"><span class=anchor-text>3. Results and discussion</span></a><li class=toc-list-entry-outline-padding><a class="anchor u-text-truncate anchor-default" href=#s0110 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title="4. Conclusions"><span class=anchor-text>4. Conclusions</span></a><li class=toc-list-entry-outline-padding><a class="anchor u-text-truncate anchor-default" href=#ak005 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title=Acknowledgements><span class=anchor-text>Acknowledgements</span></a><li class=toc-list-entry-outline-padding><a class="anchor u-text-truncate anchor-default" href=#s0120 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title="Appendix A. Supplementary material"><span class=anchor-text>Appendix A. Supplementary material</span></a><li class=toc-list-entry-outline-padding><a class="anchor u-text-truncate anchor-default" href=#bi005 data-aa-button="sd:product:journal:article:type=anchor:name=outlinelink" title=References><span class=anchor-text>References</span></a></ol><button class="button-link button-link-secondary u-margin-xs-top u-margin-s-bottom button-link-icon-right" type=button aria-expanded=false data-aa-button="sd:product:journal:article:type=menu:name=show-full-outline"><span class=button-link-text>Show full outline</span><svg focusable=false viewBox="0 0 92 128" height=20 width=17.25 class="icon icon-navigate-down"><path d="m1 51l7-7 38 38 38-38 7 7-45 45z"></path></svg></button><div class=PageDivider></div></div><div class=CitedBy id=toc-cited-by><h2 class=u-h4><a class="anchor anchor-default" href=#section-cited-by><span class=anchor-text>Cited by (53)</span></a></h2><div class=PageDivider></div></div><div class=Figures id=toc-figures><h2 class=u-h4>Figures (5)</h2><ol class=u-margin-s-bottom><li><a class="anchor u-display-block anchor-default" href=#f0005 data-aa-button="sd:product:journal:article:type=anchor:name=figure"><span class=anchor-text><div><img alt="Fig. 1. Measured and simulated maize yield from 2009 to 2015 for (a) ecological…" class=u-display-block height=164px 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 width=214px></div></span></a><li><a class="anchor u-display-block anchor-default" href=#f0010 data-aa-button="sd:product:journal:article:type=anchor:name=figure"><span class=anchor-text><div><img alt="Fig. 2. Measured and simulated plant nitrogen uptake from 2009 to 2015 for (a)…" class=u-display-block height=163px 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 width=208px></div></span></a><li><a class="anchor u-display-block anchor-default" href=#f0015 data-aa-button="sd:product:journal:article:type=anchor:name=figure"><span class=anchor-text><div><img alt="Fig. 3. Measured and simulated soil organic carbon content (0–0" class=u-display-block height=154px 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" width=219px></div></span></a><li><a class="anchor u-display-block anchor-default" href=#f0020 data-aa-button="sd:product:journal:article:type=anchor:name=figure"><span class=anchor-text><div><img alt="Fig. 4. Measured and simulated soil mineral N content (0–0" class=u-display-block height=160px 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 width=219px></div></span></a><li><a class="anchor u-display-block anchor-default" href=#f0025 data-aa-button="sd:product:journal:article:type=anchor:name=figure"><span class=anchor-text><div><img alt="Fig. 5. Sensitivity of maize yields to (a) fertilizer application rate, (b) planting…" class=u-display-block height=164px 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" width=174px></div></span></a></ol><div class=PageDivider></div></div><div class=Tables id=toc-tables><h2 class=u-h4>Tables (5)</h2><ol class=u-padding-s-bottom><li class=toc-list-entry-outline-padding><a class="anchor u-text-truncate anchor-default anchor-has-colored-icon anchor-icon-left" href=#t0005 data-aa-button="sd:product:journal:article:type=anchor:name=table" title="The basic management information for spring maize from 2009 to 2015 at experimental site."><svg focusable=false viewBox="0 0 98 128" width=20 height=20 class="icon icon-table"><path d="m54 68h32v32h-32v-32zm-42 0h32v32h-32v-32zm0-42h32v32h-32v-32zm42 0h32v32h-32v-32zm-52 84h94v-94h-94v94z"></path></svg><span class=anchor-text>Table 1</span></a><li class=toc-list-entry-outline-padding><a class="anchor u-text-truncate anchor-default anchor-has-colored-icon anchor-icon-left" href=#t0010 data-aa-button="sd:product:journal:article:type=anchor:name=table" title="The parameter levels for sensitivity analysis at Liufangzi, Jilin, China."><svg focusable=false viewBox="0 0 98 128" width=20 height=20 class="icon icon-table"><path d="m54 68h32v32h-32v-32zm-42 0h32v32h-32v-32zm0-42h32v32h-32v-32zm42 0h32v32h-32v-32zm-52 84h94v-94h-94v94z"></path></svg><span class=anchor-text>Table 2</span></a><li class=toc-list-entry-outline-padding><a class="anchor u-text-truncate anchor-default anchor-has-colored-icon anchor-icon-left" href=#t0015 data-aa-button="sd:product:journal:article:type=anchor:name=table" title="Statistics of model calibration and evaluation between the measured and simulated maize yield and nitrogen uptake at Liufangzi, Jilin, China."><svg focusable=false viewBox="0 0 98 128" width=20 height=20 class="icon icon-table"><path d="m54 68h32v32h-32v-32zm-42 0h32v32h-32v-32zm0-42h32v32h-32v-32zm42 0h32v32h-32v-32zm-52 84h94v-94h-94v94z"></path></svg><span class=anchor-text>Table 3</span></a><li class=toc-list-entry-outline-padding><a class="anchor u-text-truncate anchor-default anchor-has-colored-icon anchor-icon-left" href=#t0020 data-aa-button="sd:product:journal:article:type=anchor:name=table" title="Statistical of model calibration and evaluations between the measured and simulated agronomic efficiency of N (AEN), partial factor productivity of N (PFPN) and recover efficiency of N (REN) at Liufan..."><svg focusable=false viewBox="0 0 98 128" width=20 height=20 class="icon icon-table"><path d="m54 68h32v32h-32v-32zm-42 0h32v32h-32v-32zm0-42h32v32h-32v-32zm42 0h32v32h-32v-32zm-52 84h94v-94h-94v94z"></path></svg><span class=anchor-text>Table 4</span></a><li class=toc-list-entry-outline-padding><a class="anchor u-text-truncate anchor-default anchor-has-colored-icon anchor-icon-left" href=#t0025 data-aa-button="sd:product:journal:article:type=anchor:name=table" title="Recommendations of field management practices based on the sensitivity analyses of the DNDC and DSSAT models at Liufangzi, Jilin, China."><svg focusable=false viewBox="0 0 98 128" width=20 height=20 class="icon icon-table"><path d="m54 68h32v32h-32v-32zm-42 0h32v32h-32v-32zm0-42h32v32h-32v-32zm42 0h32v32h-32v-32zm-52 84h94v-94h-94v94z"></path></svg><span class=anchor-text>Table 5</span></a></ol><div class=PageDivider></div></div><div class=Extras id=toc-extras><h2 class=u-h4>Extras (1)</h2><ol class=u-padding-s-bottom><li class=toc-list-entry-outline-padding><a class="anchor u-text-truncate anchor-default anchor-has-colored-icon anchor-icon-left" href=#m0005 data-aa-button="sd:product:journal:article:type=anchor:region=outline:name=sdf" title="Supplementary data 1"><svg focusable=false viewBox="0 0 94 128" width=20 height=20 class="icon icon-text-document"><path d="m35.6 1e1c-5.38 0-10.62 1.92-14.76 5.4-9.1 7.68-18.84 20.14-18.84 32.1v70.5h9e1v-15.99-2.01-4e1 -17.64-32.36h-56.4zm0 1e1h46.4v22.36 17.64 4e1 2.01 5.99h-7e1v-49c0-6.08 4.92-11 11-11h17v-2e1h-6c-2.2 0-4 1.8-4 4v6h-7c-3.32 0-6.44 0.78-9.22 2.16 2.46-5.62 7.28-11.86 13.5-17.1 2.34-1.98 5.3-3.06 8.32-3.06zm-13.6 38v1e1h5e1v-1e1h-5e1zm0 2e1v1e1h5e1v-1e1h-5e1z"></path></svg><span class=anchor-text>Supplementary data 1</span></a></ol><div class=PageDivider></div></div></div></div><article class="col-lg-12 col-md-16 pad-left pad-right u-padding-s-top" lang=en><div class=Publication id=publication><div class="publication-brand u-show-from-sm"><a class="anchor anchor-default" href=https://www.sciencedirect.com/journal/computers-and-electronics-in-agriculture title="Go to Computers and Electronics in Agriculture on ScienceDirect"><span class=anchor-text><img class=publication-brand-image 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" alt=Elsevier></span></a></div><div class="publication-volume u-text-center"><h2 class="publication-title u-h3" id=publication-title><a class="anchor publication-title-link anchor-navigation" href=https://www.sciencedirect.com/journal/computers-and-electronics-in-agriculture title="Go to Computers and Electronics in Agriculture on ScienceDirect"><span class=anchor-text>Computers and Electronics in Agriculture</span></a></h2><div class=text-xs><a class="anchor anchor-default" href=https://www.sciencedirect.com/journal/computers-and-electronics-in-agriculture/vol/166/suppl/C title="Go to table of contents for this volume/issue"><span class=anchor-text>Volume 166</span></a>, November 2019, 104988</div></div><div class="publication-cover u-show-from-sm"><a class="anchor anchor-default" href=https://www.sciencedirect.com/journal/computers-and-electronics-in-agriculture/vol/166/suppl/C><span class=anchor-text><img class=publication-cover-image 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" alt="Computers and Electronics in Agriculture"></span></a></div></div><h1 id=screen-reader-main-title class="Head u-font-serif u-h2 u-margin-s-ver"><span class=title-text>Exploring management strategies to improve maize yield and nitrogen use efficiency in northeast China using the DNDC and DSSAT models</span></h1><div class=Banner id=banner><div class="wrapper truncated"><div class="AuthorGroups text-s"><div class=author-group id=author-group><span class=sr-only>Author links open overlay panel</span><button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true data-xocs-content-type=author data-xocs-content-id=au005><span class=button-link-text><span class=react-xocs-alternative-link><span class=given-name>Rong</span> <span class="text surname">Jiang</span> </span><span class=author-ref id=baf005><sup>a</sup></span></span></button>, <button class="button-link button-link-primary" type=button data-sd-ui-side-panel-opener=true 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id=article-identifier-links><a class="anchor doi anchor-default" href=https://doi.org/10.1016/j.compag.2019.104988 target=_blank rel="noreferrer noopener" aria-label="Persistent link using digital object identifier" title="Persistent link using digital object identifier"><span class=anchor-text>https://doi.org/10.1016/j.compag.2019.104988</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right arrow-external-link"><path d="m4 36h57.07l-59.5 59.5 7.07 7.08 59.36-59.36v56.78h1e1v-74h-74z"></path></svg></a><a class="anchor rights-and-content anchor-default" href="https://s100.copyright.com/AppDispatchServlet?publisherName=ELS&contentID=S0168169919306532&orderBeanReset=true" target=_blank rel="noreferrer noopener"><span class=anchor-text>Get rights and content</span><svg focusable=false viewBox="0 0 78 128" aria-label="Opens in new window" width=1em height=1em class="icon icon-arrow-up-right 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u-margin-xs-bottom">Highlights</h2><div id=as005><p id=sp0005><ul class=list><li class=react-xocs-list-item><span class=list-label>•</span><span><p id=p0005>Both DNDC and <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/agricultural-engineering title="Learn more about DSSAT from ScienceDirect's AI-generated Topic Pages" class=topic-link>DSSAT</a> simulated maize yield, above-ground biomass and N uptake well.</p></span><li class=react-xocs-list-item><span class=list-label>•</span><span><p id=p0010>DNDC showed lower bias than DSSAT in <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/soil-organic-carbon title="Learn more about SOC from ScienceDirect's AI-generated Topic Pages" class=topic-link>SOC</a> and mineral N under N-fertilized treatments.</p></span><li class=react-xocs-list-item><span class=list-label>•</span><span><p id=p0015><span>Modelled optimum <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/nitrogen-fertilizer title="Learn more about fertilizer nitrogen from ScienceDirect's AI-generated Topic Pages" class=topic-link>fertilizer nitrogen</a> rate was at 180–210 kg N ha</span><sup>−1</sup> with two splits.</p></span><li class=react-xocs-list-item><span class=list-label>•</span><span><p id=p0020>Suggested planting date was from late April to early May and density was 7 seeds m<sup>−2</sup>.</p></span></ul><p></p></div></div><div class="abstract author" id=ab010 lang=en><h2 class="section-title u-h4 u-margin-l-top u-margin-xs-bottom">Abstract</h2><div id=as010><p id=sp0010>Process-based models are valuable tools for simulating crop production, estimating agronomic efficiency and developing optimum management practices to achieve sustainable agriculture. However, a comparison of the DeNitrification-DeComposition (DNDC) and Decision Support System for Agrotechnology Transfer (DSSAT) models has not been previously used to optimize management practices for spring maize in northeast China. The objectives of this study were to evaluate the performance of the DSSAT and DNDC models in simulating maize growth and soil C & N dynamics and analyse their weaknesses and strengths based on a 7-year spring maize study in northeast China; and to explore the optimal management practices for improving maize production and nitrogen use efficiency under 20-year climate variability. Both DNDC and DSSAT exhibited “good” to “excellent” performance in simulating maize yield, above-ground biomass and plant N uptake for ecological intensification with N fertilizer (EI-N) and farmers’ practice with N fertilizer (FP-N) treatments based on percent bias (PBIAS) of −10.5–4.2%, a normalized root mean squared error (nRMSE) of 7.5–17.2%, a Nash-Sutcliffe efficiency (NSE) of 0.17–0.77 and a d index of agreement (d) of 0.81–0.94. Both models showed “fair” to “good” performance in the same simulation for EI without N fertilizer (EI-N0) and FP without N fertilizer (FP-N0) treatments, but the maize yield simulation was better for the DSSAT model. In addition, the two models provided “fair” performance for N-fertilized treatments to “poor” performance for N-unfertilized treatments in simulations of soil organic carbon (0–0.20 m) and mineral N (0–0.30 m), but the simulations were better for the DNDC model. Sensitivity analyses indicated that the optimum yield and agronomic efficiency were achieved at a planting date of late April to early May, a fertilizer N application rate of 180–210 kg N ha<sup>−1</sup> with two timing splits in the DNDC and DSSAT model and a planting density of 7 seeds m<sup>−2</sup> in the DSSAT model. This study suggests that comparing the management scenarios of multiple dynamic models is more beneficial to develop best management practices for improving crop production and fertilizer use efficiency.</p></div></div></div><ul id=issue-navigation class="issue-navigation u-margin-s-bottom u-bg-grey1"><li class="previous move-left u-padding-s-ver u-padding-s-left"><a class="button-alternative button-alternative-tertiary button-alternative-icon-left" href=https://www.sciencedirect.com/science/article/pii/S0168169919313110><span class=button-alternative-icon><svg focusable=false viewBox="0 0 54 128" width=10.125 height=24 class="icon icon-navigate-left u-fill-grey8"><path d="m1 61l45-45 7 7-38 38 38 38-7 7z"></path></svg></span><span class=button-alternative-text>Previous <span class=extra-detail-1>article</span><span class=extra-detail-2> in issue</span></span></a><li class="next move-right u-padding-s-ver u-padding-s-right"><a class="button-alternative button-alternative-tertiary button-alternative-icon-right" href=https://www.sciencedirect.com/science/article/pii/S016816991930691X><span class=button-alternative-text>Next <span class=extra-detail-1>article</span><span class=extra-detail-2> in issue</span></span><span class=button-alternative-icon><svg focusable=false viewBox="0 0 54 128" width=10.125 height=24 class="icon icon-navigate-right u-fill-grey8"><path d="m1 99l38-38-38-38 7-7 45 45-45 45z"></path></svg></span></a></ul><div class="Keywords u-font-serif text-s"><div id=kg005 class=keywords-section><h2 class="section-title u-h4 u-margin-l-top u-margin-xs-bottom">Keywords</h2><div id=k0005 class=keyword><span>DNDC model</span></div><div id=k0010 class=keyword><span>DSSAT model</span></div><div id=k0015 class=keyword><span>Maize growth</span></div><div id=k0020 class=keyword><span>Nitrogen use efficiency</span></div><div id=k0025 class=keyword><span>Sensitivity analysis</span></div></div></div><div class="Body u-font-serif text-s" id=body><div><section id=s0005><h2 id=st020 class="u-h4 u-margin-l-top u-margin-xs-bottom">1. Introduction</h2><p id=p0025>The demand for global agricultural production has greatly increased with the rising population worldwide and is likely to continue increasing in the future (<a class="anchor u-display-inline anchor-paragraph" href=#b0065 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0065><span class=anchor-text>FAO, 2009</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0085 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0085><span class=anchor-text>Godfray et al., 2010</span></a><span>). More intensified cropping with large amounts of fertilizer input has been conducted to obtain higher yields. However, the excessive and imbalanced application of fertilizers has resulted in low <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/nutrient-use-efficiency title="Learn more about nutrient use efficiency from ScienceDirect's AI-generated Topic Pages" class=topic-link>nutrient use efficiency</a> and high environmental risks (e.g., greenhouse gas emissions, water contamination) (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0090 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0090><span class=anchor-text>Gu et al., 2015</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0350 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0350><span class=anchor-text>Xu et al., 2016</span></a><span>). Thus, modern <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/sustainable-agriculture title="Learn more about sustainable agriculture from ScienceDirect's AI-generated Topic Pages" class=topic-link>sustainable agriculture</a> is facing the challenge of balancing crop production and environmental effects. Ecological intensification (EI) is an effective approach for sustainable agriculture that is essential to meet these challenges.</span><p id=p0030>The principle of EI is to satisfy the anticipated increase in food demand while minimizing the negative effects on the environment by integrating ecological management practices (<a class="anchor u-display-inline anchor-paragraph" href=#b0030 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0030><span class=anchor-text>Cassman, 1999</span></a><span>). EI technology emphasizes efficiently using inputs (e.g., fertilizer, pesticide), optimizing agronomic management practices (e.g., <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/sowing-date title="Learn more about sowing dates from ScienceDirect's AI-generated Topic Pages" class=topic-link>sowing dates</a>, planting density, irrigation, tillage, rotation) and minimizing the effects on the environment (e.g., greenhouse gas emissions, nitrate leaching) (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0110 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0110><span class=anchor-text>Hochman et al., 2013</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0245 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0245><span class=anchor-text>Petersen and Snapp, 2015</span></a>). In recent decades, many field experiments have demonstrated the co-benefit of EI practices under different climate conditions and crop systems. For example, optimized planting density and fertilizer N input improved maize yield and N use efficiency in Mozambique (<a class="anchor u-display-inline anchor-paragraph" href=#b0285 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0285><span class=anchor-text>Roxburgh and Rodriguez, 2016</span></a>), and adopted heterotic hybrids plus the use of herbicide and fertilizer increased maize yield by 32% from the 1990s to the 2000s in the United States (<a class="anchor u-display-inline anchor-paragraph" href=#b0070 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0070><span class=anchor-text>Flavell, 2010</span></a>).<p id=p0035>In northeast China, excessive fertilization and inappropriate management practices in maize fields have resulted in low agronomic nutrient efficiency and high environmental risks (<a class="anchor u-display-inline anchor-paragraph" href=#b0340 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0340><span class=anchor-text>Xu et al., 2014a</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0350 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0350><span class=anchor-text>Xu et al., 2016</span></a>). It has been reported that changes in planting date could lead to 15–35% variations in maize yields (<a class="anchor u-display-inline anchor-paragraph" href=#b0370 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0370><span class=anchor-text>Yao et al., 2015</span></a>). Increasing plant density can contribute to high yield, but blind increases often reduced maize yield due to the dense canopy and weak stems, which may cause plant lodging (<a class="anchor u-display-inline anchor-paragraph" href=#b0385 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0385><span class=anchor-text>Zhang et al., 2014</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0400 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0400><span class=anchor-text>Zhang et al., 2017a</span></a><span>). Tillage is an essential management practice in crop production, but its influence on crop growth is complex, which is related to soil quality (e.g., soil moisture, <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/soil-temperature title="Learn more about soil temperature from ScienceDirect's AI-generated Topic Pages" class=topic-link>soil temperature</a> and soil nutrient availability) (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0390 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0390><span class=anchor-text>Zhang et al., 2015a</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0185 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0185><span class=anchor-text>Liu et al., 2018</span></a>). Therefore, optimizing agronomic managements based on the EI concept is essential to exploring best management practices for obtaining high maize production and agronomic efficiency in northeast China.<p id=p0040>Process-based models such as the Decision Support System for Agrotechnology Transfer (DSSAT) (<a class="anchor u-display-inline anchor-paragraph" href=#b0120 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0120><span class=anchor-text>Jones et al., 2003</span></a>), Simulateur mulTIdisciplinaire pour lesCultures Standard (STICS) (<a class="anchor u-display-inline anchor-paragraph" href=#b0025 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0025><span class=anchor-text>Brisson et al., 2003</span></a>), Agricultural Production Systems Simulator (APSIM) (<a class="anchor u-display-inline anchor-paragraph" href=#b0125 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0125><span class=anchor-text>Keating et al., 2003</span></a><span>), Root Zone Water <a href=https://www.sciencedirect.com/topics/computer-science/quality-model title="Learn more about Quality Model from ScienceDirect's AI-generated Topic Pages" class=topic-link>Quality Model</a> (RZWQM2) (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0195 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0195><span class=anchor-text>Ma et al., 2012</span></a>), DeNitrification-DeComposition (DNDC) (<a class="anchor u-display-inline anchor-paragraph" href=#b0145 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0145><span class=anchor-text>Li et al., 2012</span></a>), and daily time step versions of the CENTURY ecosystem model (DayCent) (<a class="anchor u-display-inline anchor-paragraph" href=#b0240 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0240><span class=anchor-text>Parton et al., 1998</span></a><span>) are widely used in agroecosystems to evaluate the effects of <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/agricultural-management title="Learn more about agricultural management from ScienceDirect's AI-generated Topic Pages" class=topic-link>agricultural management</a> practices on crop growth and nutrient dynamics (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0270 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0270><span class=anchor-text>Qi et al., 2011a</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0280 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0280><span class=anchor-text>Qi et al., 2013</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0055 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0055><span class=anchor-text>Dutta et al., 2017</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0255 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0255><span class=anchor-text>Plaza-Bonilla et al., 2018</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0155 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0155><span class=anchor-text>Li et al., 2018</span></a>); develop useful tools for farmers or policy applications (<a class="anchor u-display-inline anchor-paragraph" href=#b0150 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0150><span class=anchor-text>Li et al., 2015</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0395 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0395><span class=anchor-text>Zhang et al., 2015b</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0095 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0095><span class=anchor-text>He et al., 2016</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0200 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0200><span class=anchor-text>Malik et al., 2019</span></a>); and assess climate change impacts on crop production and environment risks as well as explore potential adaptation measures (<a class="anchor u-display-inline anchor-paragraph" href=#b0225 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0225><span class=anchor-text>Ngwira et al., 2014</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0100 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0100><span class=anchor-text>He et al., 2018a</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0105 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0105><span class=anchor-text>He et al., 2018b</span></a><span>). Sensitivity analysis is an effective approach for the applications of crop and soil models to explore optimal management strategies (e.g., <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/fertilizer-application title="Learn more about fertilizer application from ScienceDirect's AI-generated Topic Pages" class=topic-link>fertilizer application</a> rate, planting date, planting density and irrigation) for improving crop growth and minimizing environmental risk under current and future climate change (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0280 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0280><span class=anchor-text>Qi et al., 2013</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0395 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0395><span class=anchor-text>Zhang et al., 2015b</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0095 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0095><span class=anchor-text>He et al., 2016</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0100 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0100><span class=anchor-text>He et al., 2018a</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0105 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0105><span class=anchor-text>He et al., 2018b</span></a>). For example, <a class="anchor u-display-inline anchor-paragraph" href=#b0095 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0095><span class=anchor-text>He et al. (2016)</span></a> conducted sensitivity analysis of the DSSAT model to simulate the response of maize/wheat yield and N leaching to different input parameters (e.g., fertilizer application rate, planting date, planting density) under various Canadian climate; <a class="anchor u-display-inline anchor-paragraph" href=#b0395 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0395><span class=anchor-text>Zhang et al. (2015b)</span></a><span> employed the <a href=https://www.sciencedirect.com/topics/computer-science/model-decomposition title="Learn more about DNDC model from ScienceDirect's AI-generated Topic Pages" class=topic-link>DNDC model</a> to optimize management practices in North China for maintaining maize yield and reducing N leaching including the improvements of fertilizer application rate and timing, tillage depth and irrigation; and </span><a class="anchor u-display-inline anchor-paragraph" href=#b0280 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0280><span class=anchor-text>Qi et al. (2013)</span></a> verified that the CERES-Wheat module incorporated in RZWQM2 model well simulated crop yield under various planting dates and planting densities for spring wheat in the Northern Great Plain of the United States.<p id=p0045>The DNDC model is a well-established tool used to predict C and N cycling in agroecosystems, and it has the capacity to simulate crop growth by specifying N requirements for C biomass accumulation (<a class="anchor u-display-inline anchor-paragraph" href=#b0145 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0145><span class=anchor-text>Li et al., 2012</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0080 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0080><span class=anchor-text>Gilhespy et al., 2014</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0055 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0055><span class=anchor-text>Dutta et al., 2017</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0305 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0305><span class=anchor-text>Smith et al., 2019</span></a><span>). The DSSAT model includes widely used <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/crop-simulation-model title="Learn more about crop simulation models from ScienceDirect's AI-generated Topic Pages" class=topic-link>crop simulation models</a> (CSMs), which can systematically simulate the crop growth stages with genotypic differences represented by cultivar-specific genetic coefficients (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0120 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0120><span class=anchor-text>Jones et al., 2003</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0115 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0115><span class=anchor-text>Hoogenboom et al., 2012</span></a><span>). The DNDC and DSSAT models have been well applied separately to simulate maize growth, <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/soil-water-movement title="Learn more about soil water movement from ScienceDirect's AI-generated Topic Pages" class=topic-link>soil water movement</a> and nutrient cycling under different field management practices in northeast China (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0355 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0355><span class=anchor-text>Yang et al., 2011a</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0170 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0170><span class=anchor-text>Liu et al., 2013</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0040 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0040><span class=anchor-text>Chen et al., 2015</span></a><span>). However, a comparison of the DNDC and DSSAT models has not been previously used to simulate spring maize growth and soil processes and optimize management practices in order to improve maize production and agronomic efficiency in northeast China. This information gap has limited any further applications of the DNDC and DSSAT models for regional management improvement and forecasting. The objectives of this study were: (1) to calibrate and evaluate the DNDC and DSSAT models using measured crop yield, above-ground biomass, N uptake, <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/soil-organic-carbon title="Learn more about soil organic carbon from ScienceDirect's AI-generated Topic Pages" class=topic-link>soil organic carbon</a> (SOC) and mineral N based on a 7-year spring maize study conducted in northeast China; (2) to identify the performances and report weaknesses of the two models for simulating spring maize growth and soil C & N dynamics, and most importantly (3) to optimize management practices including the planting date, planting density, tillage depth, and fertilizer N rate and timing to improve maize yield and N use efficiency in northeast China.</span></p></section><section id=s0010><h2 id=st025 class="u-h4 u-margin-l-top u-margin-xs-bottom">2. Materials and methods</h2><section id=s0015><h3 id=st030 class="u-h4 u-margin-m-top u-margin-xs-bottom">2.1. Field experiment</h3><div><p id=p0050><span>A field experiment was conducted from 2009 to 2015 in Liufangzi County, Jilin Province (43°35′N and 124°54′E), in the <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/maize title="Learn more about Corn from ScienceDirect's AI-generated Topic Pages" class=topic-link>Corn</a> Belt of China. The average air temperature of the growing season (May – September) was 18.9 °C, with an average seasonal precipitation of 423 mm (</span><a class="anchor u-display-inline anchor-paragraph" href=#s0120 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=s0120><span class=anchor-text>Fig. A. 1</span></a>). The basic soil physical and chemical properties at the beginning of the field experiment are listed in <a class="anchor u-display-inline anchor-paragraph" href=#s0120 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=s0120><span class=anchor-text>Table A. 1</span></a>. A split plot experimental design with four replications was used. The main plots consisted of ecological intensification and farmers’ practice, and the split plots consisted of different nitrogen fertilizer rates. The four treatments consisted of ecological intensification with nitrogen fertilizer (EI-N), farmers’ practice with nitrogen fertilizer (FP-N), ecological intensification without nitrogen fertilizer (EI-N0) and farmers’ practice without nitrogen fertilizer (FP-N0). The fertilizer application rates were 180 kg N ha<sup>−1</sup>, 75 kg P<sub>2</sub>O<sub>5</sub> ha<sup>−1</sup> and 90 kg K<sub>2</sub>O ha<sup>−1</sup> in the EI-N treatment, which was recommended based on expected yield response to fertilizer and target agronomy efficiencies of applied N and the concept of ecological intensification management; additionally, the N rate was increased to 200 kg N ha<sup>−1</sup> in 2015. Half of the N rate was applied as basal fertilizer and half was applied at the jointing stage from 2009 to 2011. Then, the N rate was applied at the sowing, jointing and tasselling stages at ratios of 1/4:2/4:1/4, respectively, from 2012 to 2015, and all P<sub>2</sub>O<sub>5</sub> and K<sub>2</sub>O were applied as basal fertilizers. Meanwhile, the FP-N treatment received a fertilizer supply of 251 kg N ha<sup>−1</sup>, 145 kg P<sub>2</sub>O<sub>5</sub> ha<sup>−1</sup> and 100 kg K<sub>2</sub>O ha<sup>−1</sup><span> as basal fertilizer from 2009 to 2015, representing an average <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/fertilizer-rates title="Learn more about fertilizer rates from ScienceDirect's AI-generated Topic Pages" class=topic-link>fertilizer rate</a> applied based on farmers’ practice in northeast China (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0340 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0340><span class=anchor-text>Xu et al., 2014a</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0345 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0345><span class=anchor-text>Xu et al., 2014b</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0350 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0350><span class=anchor-text>Xu et al., 2016</span></a><span>). All other practices of the EI-N0 and FP-N0 treatments were the same as those in the EI-N and FP-N treatments. In this study, the <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/conventional-tillage title="Learn more about conventional tillage from ScienceDirect's AI-generated Topic Pages" class=topic-link>conventional tillage</a> with fall mouldboard ploughing (0.20 m depth) was conducted before snowing. Maize was planted at seeding rate range from 5.0 to 6.5 seeds m</span><sup>−2</sup> with 60 cm row space. The SOC contents (0–0.20 m) were measured at harvest from 2009 to 2015 using dichromate oxidation method (<a class="anchor u-display-inline anchor-paragraph" href=#bib412 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=bib412><span class=anchor-text>Kalembasa and Jenkinson, 1973</span></a><span>). Detailed measurements of maize yield, N uptake and <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/soil-minerals title="Learn more about soil mineral from ScienceDirect's AI-generated Topic Pages" class=topic-link>soil mineral</a> N can be referred to </span><a class="anchor u-display-inline anchor-paragraph" href=#bib411 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=bib411><span class=anchor-text>Zhao et al. (2016)</span></a>. Management practice, planting/harvest date and cultivar information is shown in <a class="anchor u-display-inline anchor-paragraph" href=#t0005 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0005><span class=anchor-text>Table 1</span></a>.<div class="tables frame-topbot rowsep-0 colsep-0" id=t0005><span class="captions text-s"><span id=cn0030><p id=sp0040><span class=label>Table 1</span>. The basic management information for spring maize from 2009 to 2015 at experimental site.</p></span></span><div class=groups><table><thead><tr class=valign-top><th scope=col class=align-left>Treatment<th scope=col class=align-left>Year<th scope=col class=align-left>Cultivar information<th scope=col class=align-left>Planting date<th scope=col class=align-left>Harvest date<th scope=col class=align-left>Planting density<th scope=col class=align-left>Tillage depth<th scope=col class="align-left rowsep-1" colspan=3>N fertilizer application (kg N ha<sup>−1</sup>)<tr class="rowsep-1 valign-top"><td scope=col class=align-left><span class=screen-reader-only>Empty Cell</span><td scope=col class=align-left><span class=screen-reader-only>Empty Cell</span><td scope=col class=align-left><span class=screen-reader-only>Empty Cell</span><th scope=col class=align-left>(day of year)<th scope=col class=align-left>(day of year)<th scope=col class=align-left>(seed m<sup>−2</sup>)<th scope=col class=align-left>(m)<th scope=col class=align-left>Basal<th scope=col class=align-left>Jointing<th scope=col class=align-left>Tasseling<tbody><tr class=valign-top><td class=align-left>EI (FP)<a class="anchor u-display-inline anchor-paragraph" href=#tblfn1 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=tblfn1><span class=anchor-text><sup>a</sup></span></a><td class=align-left>2009<td class=align-left>Pioneer335 (Pioneer335)<td class=align-left>124<td class=align-left>271<td class=align-left>6.0 (6.0)<td class=align-left>0.20<td class=align-left>90 (251)<td class=align-left>90<td class=align-left>-<tr class=valign-top><td class=align-left><td class=align-left>2010<td class=align-left>Pioneer335 (Pioneer335)<td class=align-left>129<td class=align-left>273<td class=align-left>6.5 (5.0)<td class=align-left>0.20<td class=align-left>90 (251)<td class=align-left>90<td class=align-left>-<tr class=valign-top><td class=align-left><td class=align-left>2011<td class=align-left>Pioneer335 (Jidong33)<td class=align-left>117<td class=align-left>267<td class=align-left>6.5 (5.0)<td class=align-left>0.20<td class=align-left>90 (251)<td class=align-left>90<td class=align-left>-<tr class=valign-top><td class=align-left><td class=align-left>2012<td class=align-left>Pioneer335 (Lvyu4117)<td class=align-left>122<td class=align-left>268<td class=align-left>6.5 (5.0)<td class=align-left>0.20<td class=align-left>45 (251)<td class=align-left>90<td class=align-left>45<tr class=valign-top><td class=align-left><td class=align-left>2013<td class=align-left>Pioneer335 (Lvyu4117)<td class=align-left>121<td class=align-left>273<td class=align-left>6.5 (5.0)<td class=align-left>0.20<td class=align-left>45 (251)<td class=align-left>90<td class=align-left>45<tr class=valign-top><td class=align-left><td class=align-left>2014<td class=align-left>Pioneer335 (Lvyu4119)<td class=align-left>120<td class=align-left>273<td class=align-left>6.5 (5.0)<td class=align-left>0.20<td class=align-left>45 (251)<td class=align-left>90<td class=align-left>45<tr class=valign-top><td class=align-left><td class=align-left>2015<td class=align-left>Nonghua101(Lvyu4119)<td class=align-left>119<td class=align-left>273<td class=align-left>6.5 (5.0)<td class=align-left>0.20<td class=align-left>50 (251)<td class=align-left>100<td class=align-left>50</table></div><dl class=footnotes><dt id=tblfn1>a<dd><p id=np005>The content within the brackets represents the detail information for the FP treatment. EI: ecological intensification; FP-N: farmers’ practice.</p></dl></div></div></section><section id=s0020><h3 id=st035 class="u-h4 u-margin-m-top u-margin-xs-bottom">2.2. DNDC and DSSAT model descriptions</h3><section id=s0025><h4 id=st040 class="u-margin-m-top u-margin-xs-bottom">2.2.1. DNDC model</h4><p id=p0055><span>The DNDC model was initially developed to simulate the <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/biogeochemical-cycle title="Learn more about biogeochemical cycles from ScienceDirect's AI-generated Topic Pages" class=topic-link>biogeochemical cycles</a> of carbon and nitrogen in agroecosystems (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0135 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0135><span class=anchor-text>Li et al., 1992</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0140 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0140><span class=anchor-text>Li et al., 1994</span></a><span>). It has been widely used to estimate crop growth, soil C and N dynamics, greenhouse gas emissions and the <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/soil-water title="Learn more about soil water from ScienceDirect's AI-generated Topic Pages" class=topic-link>soil water</a> cycle under various management practices and climatic conditions (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0080 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0080><span class=anchor-text>Gilhespy et al., 2014</span></a><span><span>). The first component of the DNDC model consists of soil, climate, crop growth and decomposition sub-models, which are related to four ecological factors (climate, soil, vegetation and anthropogenic activity) that can predict daily crop growth (e.g., water demand, root respiration, N uptake and growth of grain, stem and root), soil environmental conditions (e.g., temperature, moisture, <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/potential-evapotranspiration title="Learn more about potential evapotranspiration from ScienceDirect's AI-generated Topic Pages" class=topic-link>potential evapotranspiration</a>, pH, oxidation-reduction potential) and the </span><a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/soil-carbon title="Learn more about soil carbon from ScienceDirect's AI-generated Topic Pages" class=topic-link>soil carbon</a><span> pool dynamic (e.g., deposition rate, dissolved organic C concentration). The second component consists of denitrification, nitrification and fermentation sub-models, which are used to simulate the impacts of soil and environmental conditions on <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/soil-microbial-activity title="Learn more about soil microbial activity from ScienceDirect's AI-generated Topic Pages" class=topic-link>soil microbial activity</a> to predict the emissions of trace gases, including dinitrogen (N</span></span><sub>2</sub>), nitrous oxide (N<sub>2</sub>O), nitric oxide (NO), carbon dioxide (CO<sub>2</sub>), ammonia (NH<sub>3</sub>), and methane (CH<sub>4</sub>), from the plant-soil system (<a class="anchor u-display-inline anchor-paragraph" href=#b0145 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0145><span class=anchor-text>Li et al., 2012</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0320 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0320><span class=anchor-text>Uzoma et al., 2015</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0100 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0100><span class=anchor-text>He et al., 2018a</span></a>).</p></section><section id=s0030><h4 id=st045 class="u-margin-m-top u-margin-xs-bottom">2.2.2. DSSAT model</h4><p id=p0060><span>The DSSAT model is a software application program, and the current version of DSSAT v4.6 is integrated with widely used Cropping System Models, a <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/soil-water-balance title="Learn more about soil water balance from ScienceDirect's AI-generated Topic Pages" class=topic-link>soil water balance</a> module, and two soil nitrogen and organic matter modules (the CERES- and CENTURY-based soil models) (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0115 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0115><span class=anchor-text>Hoogenboom et al., 2012</span></a><span>). The <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/crop-simulation-model title="Learn more about CSMs from ScienceDirect's AI-generated Topic Pages" class=topic-link>CSMs</a> can simulate the growth of over 40 different crops for field and fallow fields. The CENTURY-based soil model was selected to simulate the soil N and C dynamics in our research due to its more realistic simulations under a long-term field study (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0075 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0075><span class=anchor-text>Gijsman et al., 2002</span></a>). The soil water balance module is based on the Ritchie equation to calculate daily soil water changes (<a class="anchor u-display-inline anchor-paragraph" href=#b0315 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0315><span class=anchor-text>Tsuji et al., 1998</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0115 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0115><span class=anchor-text>Hoogenboom et al., 2012</span></a>). The DSSAT model has been widely used to simulate crop growth, soil water balance, soil carbon and soil nitrogen dynamics under different crop systems, management practices and climatic conditions (<a class="anchor u-display-inline anchor-paragraph" href=#b0225 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0225><span class=anchor-text>Ngwira et al., 2014</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0150 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0150><span class=anchor-text>Li et al., 2015</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0095 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0095><span class=anchor-text>He et al., 2016</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0105 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0105><span class=anchor-text>He et al., 2018b</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0200 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0200><span class=anchor-text>Malik et al., 2019</span></a>).</p></section></section><section id=s0035><h3 id=st050 class="u-h4 u-margin-m-top u-margin-xs-bottom">2.3. Model initialization and cultivar calibration</h3><p id=p0065><span>The DNDC and DSSAT models must be initialized and parameterized before the models can be used to simulate crop growth, soil C and N dynamics. The required input information for the DNDC and DSSAT models includes daily <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/meteorological-data title="Learn more about meteorological data from ScienceDirect's AI-generated Topic Pages" class=topic-link>meteorological data</a> (e.g., maximum, minimum temperature (°C), precipitation (mm) and solar radiation (MJ m</span><sup>−2</sup><span>)); <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/soil-properties title="Learn more about soil property from ScienceDirect's AI-generated Topic Pages" class=topic-link>soil property</a><span><span> data (e.g., initial bulk density, texture, field water capacity, pH, organic carbon, nitrate and ammonium N content); and <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/crop-management title="Learn more about crop management from ScienceDirect's AI-generated Topic Pages" class=topic-link>crop management</a> information (e.g., cropping system, planting time, tillage, fertilization and irrigation). In this study, local meteorological data were obtained from the Gongzhuling </span><a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/weather-stations title="Learn more about weather station from ScienceDirect's AI-generated Topic Pages" class=topic-link>weather station</a> during the field experiment. The annual and seasonal temperature, precipitation and solar radiation based on daily data from 2009 to 2015 are shown in </span></span><a class="anchor u-display-inline anchor-paragraph" href=#s0120 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=s0120><span class=anchor-text>Fig. A. 1</span></a>. The initial soil properties for each soil layer at the experimental site and the crop management practices (e.g., planting and harvest dates, fertilizer application rates and timing) used for our modelling study are shown in <a class="anchor u-display-inline anchor-paragraph" href=#t0005 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0005><span class=anchor-text>Table 1</span></a> and <a class="anchor u-display-inline anchor-paragraph" href=#s0120 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=s0120><span class=anchor-text>Table A. 1</span></a>, respectively.<p id=p0070>The DNDC model includes a crop growth sub-model (<a class="anchor u-display-inline anchor-paragraph" href=#b0140 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0140><span class=anchor-text>Li et al., 1994</span></a>), which has been successfully studied to simulate crop growth worldwide and reported its ability in capturing the water and N stresses (<a class="anchor u-display-inline anchor-paragraph" href=#b0395 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0395><span class=anchor-text>Zhang et al., 2015b</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0055 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0055><span class=anchor-text>Dutta et al., 2017</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0100 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0100><span class=anchor-text>He et al., 2018a</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0305 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0305><span class=anchor-text>Smith et al., 2019</span></a><span>). Plant growth for a specific cultivar is characterized by empirical growth curves specifying N requirements for C biomass accumulation and is driven by the accumulation of <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/growing-degree-day title="Learn more about growing degree days from ScienceDirect's AI-generated Topic Pages" class=topic-link>growing degree days</a><span>. The model calculates water and N demand for crop growth based on several physiological parameters (e.g., maximum <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/biomass-production title="Learn more about biomass production from ScienceDirect's AI-generated Topic Pages" class=topic-link>biomass production</a> and its portioning fractions to shoot and roots, the C/N ratio of plants, the accumulative temperature for maturity and water requirement). Crop parameters include the maximum biomass production (kg C ha</span></span><sup>−1</sup> yr<sup>−1</sup>), biomass fraction (ratio of grain, leaf, stem and root), biomass C/N ratio, thermal degree days for maturity (°C) and water demand (g water g<sup>−1</sup> dry matter) (<a class="anchor u-display-inline anchor-paragraph" href=#b0100 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0100><span class=anchor-text>He et al., 2018a</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0305 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0305><span class=anchor-text>Smith et al., 2019</span></a>).<p id=p0075>In the CSM-CERES-Maize model, maize growth is controlled by physiological growth stages, which are governed by thermal time (growing degree days), depending on the stages (<a class="anchor u-display-inline anchor-paragraph" href=#b0120 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0120><span class=anchor-text>Jones et al., 2003</span></a><span>). Three cultivar coefficients (P1, P2, P5) determine the critical <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/phenology title="Learn more about phenology from ScienceDirect's AI-generated Topic Pages" class=topic-link>phenology</a><span> stages, such as the <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/anthesis title="Learn more about anthesis from ScienceDirect's AI-generated Topic Pages" class=topic-link>anthesis</a> date and maturity date. Two cultivar coefficients (G2, G3) determine grain filling, and one cultivar coefficient determines leaf phenology (PHINT) (</span></span><a class="anchor u-display-inline anchor-paragraph" href=#b0120 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0120><span class=anchor-text>Jones et al., 2003</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0115 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0115><span class=anchor-text>Hoogenboom et al., 2012</span></a><span>). Five cultivars were selected, including Pioneer335, Nonghua101, Jidong33, Lvyu4117 and Lvyu4119, which were calibrated for the EI and FP treatments in the DNDC and DSSAT models (Table A. 2, 3). In this study, the cultivar parameters were calibrated using measured dry maize yield, above-ground biomass and plant N uptake based on a ‘Trial and Error’ method and the statistics of root mean square error (RMSE) to find the best agreement between the simulated and measured values. The fertilized treatments (EI and FP) were used for model calibration and the unfertilized treatments (EI-N0 and FP-N0) were used for validation across all the experiment years (2009–2015) including both the humid and dry years. The calibrated DNDC and DSSAT models were then used to simulate the <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/organic-soils title="Learn more about soil organic from ScienceDirect's AI-generated Topic Pages" class=topic-link>soil organic</a> carbon and mineral nitrogen dynamics.</span></p></section><section id=s0040><h3 id=st055 class="u-h4 u-margin-m-top u-margin-xs-bottom">2.4. Model performance statistics</h3><p id=p0080>Model simulation performance was estimated by comparing the simulated and measured maize yield, above-ground biomass, plant N uptake, nitrogen use efficiency, soil organic C and soil mineral N distribution. Four deviation statistics were employed to provide an integrated evaluation: percent bias (PBIAS), the normalized root mean squared error (nRMSE), Nash-Sutcliffe efficiency (NSE) and the index of agreement (d) (<a class="anchor u-display-inline anchor-paragraph" href=#b0220 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0220><span class=anchor-text>Nash and Sutcliffe, 1970</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0335 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0335><span class=anchor-text>Willmott, 1982</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0215 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0215><span class=anchor-text>Moriasi et al., 2007</span></a>). The deviation statistics were calculated using Eqs. <a class="anchor u-display-inline anchor-paragraph" href=#e0005 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=e0005><span class=anchor-text>(1)</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#e0010 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=e0010><span class=anchor-text>(2)</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#e0015 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=e0015><span class=anchor-text>(3)</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#e0020 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=e0020><span class=anchor-text>(4)</span></a>:<span class=display><span id=e0005 class=formula><span class=label>(1)</span><span class=math><span class=MathJax_Preview></span><span 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is=true>n</mi></msubsup><msup is=true><mrow is=true><mo stretchy=false is=true>(</mo><msub is=true><mi mathvariant=normal is=true>S</mi><mi mathvariant=normal is=true>i</mi></msub><mo is=true>-</mo><msub is=true><mi mathvariant=normal is=true>M</mi><mi mathvariant=normal is=true>i</mi></msub><mo stretchy=false is=true>)</mo></mrow><mn is=true>2</mn></msup><mo stretchy=false is=true>/</mo><mi mathvariant=normal is=true>n</mi></mrow></msqrt><mover accent=true is=true><mrow is=true><mi mathvariant=normal is=true>M</mi></mrow><mrow is=true><mo stretchy=false is=true>¯</mo></mrow></mover></mfrac><mo is=true>×</mo><mn is=true>100</mn></mrow></math></span></span></span></span></span><span class=display><span id=e0015 class=formula><span class=label>(3)</span><span class=math><span class=MathJax_Preview></span><span style=font-size:90%;display:inline-block;position:relative class=MathJax_SVG id=MathJax-Element-11-Frame tabindex=0 data-mathml='<math xmlns="http://www.w3.org/1998/Math/MathML"><mrow is="true"><mi mathvariant="normal" is="true">N</mi><mi mathvariant="normal" is="true">S</mi><mi mathvariant="normal" is="true">E</mi><mo linebreak="goodbreak" is="true">=</mo><mn is="true">1</mn><mo linebreak="badbreak" is="true">-</mo><mfrac is="true"><mrow is="true"><msubsup is="true"><mo is="true">&#x2211;</mo><mrow is="true"><mi mathvariant="normal" is="true">i</mi><mo is="true">=</mo><mn is="true">1</mn></mrow><mi mathvariant="normal" is="true">n</mi></msubsup><msup is="true"><mrow is="true"><mo stretchy="false" is="true">(</mo><msub is="true"><mi mathvariant="normal" is="true">S</mi><mi mathvariant="normal" is="true">i</mi></msub><mo is="true">-</mo><msub is="true"><mi mathvariant="normal" is="true">M</mi><mi mathvariant="normal" is="true">i</mi></msub><mo stretchy="false" is="true">)</mo></mrow><mn is="true">2</mn></msup></mrow><mrow is="true"><msubsup is="true"><mo is="true">&#x2211;</mo><mrow is="true"><mi mathvariant="normal" is="true">i</mi><mo is="true">=</mo><mn is="true">1</mn></mrow><mi mathvariant="normal" is="true">n</mi></msubsup><msup is="true"><mrow is="true"><mo stretchy="false" is="true">(</mo><msub is="true"><mi mathvariant="normal" is="true">M</mi><mi mathvariant="normal" is="true">i</mi></msub><mo is="true">-</mo><mover accent="true" is="true"><mrow is="true"><mi mathvariant="normal" is="true">M</mi></mrow><mrow is="true"><mo stretchy="false" is="true">&#xAF;</mo></mrow></mover><mo stretchy="false" is="true">)</mo></mrow><mn is="true">2</mn></msup></mrow></mfrac></mrow></math>' role=presentation><svg xmlns:xlink=http://www.w3.org/1999/xlink width=23.456ex height=5.202ex style=vertical-align:-2.082ex viewBox="0 -1343.3 10099 2239.6" role=img focusable=false aria-hidden=true><g stroke=currentColor fill=currentColor stroke-width=0 transform="matrix(1 0 0 -1 0 0)"><g is=true><g is=true><use href=#MJMAIN-4E></use></g><g is=true transform=translate(750,0)><use href=#MJMAIN-53></use></g><g is=true transform=translate(1307,0)><use href=#MJMAIN-45></use></g><g is=true transform=translate(2266,0)><use href=#MJMAIN-3D></use></g><g is=true transform=translate(3322,0)><use href=#MJMAIN-31></use></g><g is=true transform=translate(4045,0)><use href=#MJMAIN-2212></use></g><g is=true transform=translate(4823,0)><g transform=translate(342,0)><rect stroke=none width=4812 height=60 x=0 y=220></rect><g is=true transform=translate(82,606)><g is=true><g is=true><use transform=scale(0.707) href=#MJSZ1-2211></use></g><g is=true transform=translate(747,337)><use transform=scale(0.5) href=#MJMAIN-6E></use></g><g is=true transform=translate(747,-203)><g is=true><use transform=scale(0.5) href=#MJMAIN-69></use></g><g is=true transform=translate(139,0)><use transform=scale(0.5) href=#MJMAIN-3D></use></g><g is=true transform=translate(528,0)><use transform=scale(0.5) href=#MJMAIN-31></use></g></g></g><g is=true transform=translate(1763,0)><g is=true><g is=true><use transform=scale(0.707) href=#MJMAIN-28></use></g><g is=true transform=translate(275,0)><g is=true><use transform=scale(0.707) href=#MJMAIN-53></use></g><g is=true transform=translate(393,-107)><use transform=scale(0.5) href=#MJMAIN-69></use></g></g><g is=true transform=translate(878,0)><use transform=scale(0.707) href=#MJMAIN-2212></use></g><g is=true transform=translate(1429,0)><g is=true><use transform=scale(0.707) href=#MJMAIN-4D></use></g><g is=true transform=translate(648,-107)><use transform=scale(0.5) href=#MJMAIN-69></use></g></g><g is=true transform=translate(2288,0)><use transform=scale(0.707) href=#MJMAIN-29></use></g></g><g is=true transform=translate(2563,337)><use transform=scale(0.5) href=#MJMAIN-32></use></g></g></g><g is=true transform=translate(60,-636)><g is=true><g is=true><use transform=scale(0.707) href=#MJSZ1-2211></use></g><g is=true transform=translate(747,337)><use transform=scale(0.5) href=#MJMAIN-6E></use></g><g is=true transform=translate(747,-203)><g is=true><use transform=scale(0.5) href=#MJMAIN-69></use></g><g is=true transform=translate(139,0)><use transform=scale(0.5) href=#MJMAIN-3D></use></g><g is=true transform=translate(528,0)><use transform=scale(0.5) href=#MJMAIN-31></use></g></g></g><g is=true transform=translate(1763,0)><g is=true><g is=true><use transform=scale(0.707) href=#MJMAIN-28></use></g><g is=true transform=translate(275,0)><g is=true><use transform=scale(0.707) href=#MJMAIN-4D></use></g><g is=true transform=translate(648,-107)><use transform=scale(0.5) href=#MJMAIN-69></use></g></g><g is=true transform=translate(1134,0)><use transform=scale(0.707) href=#MJMAIN-2212></use></g><g is=true transform=translate(1684,0)><g is=true><g is=true><use transform=scale(0.707) href=#MJMAIN-4D></use></g></g><g is=true transform=translate(147,174)><g is=true><use transform=scale(0.707) href=#MJMAIN-AF></use></g></g></g><g is=true transform=translate(2333,0)><use transform=scale(0.707) href=#MJMAIN-29></use></g></g><g is=true transform=translate(2608,399)><use transform=scale(0.5) href=#MJMAIN-32></use></g></g></g></g></g></g></g></svg><span class=MJX_Assistive_MathML role=presentation><math xmlns=http://www.w3.org/1998/Math/MathML><mrow is=true><mi mathvariant=normal is=true>N</mi><mi mathvariant=normal is=true>S</mi><mi mathvariant=normal is=true>E</mi><mo linebreak=goodbreak is=true>=</mo><mn is=true>1</mn><mo linebreak=badbreak is=true>-</mo><mfrac is=true><mrow is=true><msubsup is=true><mo is=true>∑</mo><mrow is=true><mi mathvariant=normal is=true>i</mi><mo is=true>=</mo><mn is=true>1</mn></mrow><mi mathvariant=normal is=true>n</mi></msubsup><msup is=true><mrow is=true><mo stretchy=false is=true>(</mo><msub is=true><mi mathvariant=normal is=true>S</mi><mi mathvariant=normal is=true>i</mi></msub><mo is=true>-</mo><msub is=true><mi mathvariant=normal is=true>M</mi><mi mathvariant=normal is=true>i</mi></msub><mo stretchy=false is=true>)</mo></mrow><mn is=true>2</mn></msup></mrow><mrow is=true><msubsup is=true><mo is=true>∑</mo><mrow is=true><mi mathvariant=normal is=true>i</mi><mo is=true>=</mo><mn is=true>1</mn></mrow><mi mathvariant=normal is=true>n</mi></msubsup><msup is=true><mrow is=true><mo stretchy=false is=true>(</mo><msub is=true><mi mathvariant=normal is=true>M</mi><mi mathvariant=normal is=true>i</mi></msub><mo is=true>-</mo><mover accent=true is=true><mrow is=true><mi mathvariant=normal is=true>M</mi></mrow><mrow is=true><mo stretchy=false is=true>¯</mo></mrow></mover><mo stretchy=false is=true>)</mo></mrow><mn is=true>2</mn></msup></mrow></mfrac></mrow></math></span></span></span></span></span><span class=display><span id=e0020 class=formula><span class=label>(4)</span><span class=math><span class=MathJax_Preview></span><span style=font-size:90%;display:inline-block;position:relative class=MathJax_SVG id=MathJax-Element-12-Frame tabindex=0 data-mathml='<math xmlns="http://www.w3.org/1998/Math/MathML"><mrow is="true"><mi mathvariant="normal" is="true">d</mi><mo linebreak="goodbreak" is="true">=</mo><mn is="true">1</mn><mo linebreak="badbreak" is="true">-</mo><mfrac is="true"><mrow is="true"><msubsup is="true"><mo is="true">&#x2211;</mo><mrow is="true"><mi mathvariant="normal" is="true">i</mi><mo is="true">=</mo><mn is="true">1</mn></mrow><mi mathvariant="normal" is="true">n</mi></msubsup><msup is="true"><mrow is="true"><mo stretchy="false" is="true">(</mo><msub is="true"><mi mathvariant="normal" is="true">S</mi><mi mathvariant="normal" is="true">i</mi></msub><mo is="true">-</mo><msub is="true"><mi mathvariant="normal" is="true">M</mi><mi mathvariant="normal" is="true">i</mi></msub><mo stretchy="false" is="true">)</mo></mrow><mn is="true">2</mn></msup></mrow><mrow is="true"><msubsup is="true"><mo is="true">&#x2211;</mo><mrow is="true"><mi mathvariant="normal" is="true">i</mi><mo is="true">=</mo><mn is="true">1</mn></mrow><mi mathvariant="normal" is="true">n</mi></msubsup><msup is="true"><mrow is="true"><mo stretchy="false" is="true">(</mo><mfenced close="|" open="|" is="true"><mrow is="true"><msub is="true"><mi mathvariant="normal" is="true">S</mi><mi mathvariant="normal" is="true">i</mi></msub><mo is="true">-</mo><mover accent="true" is="true"><mrow is="true"><mi mathvariant="normal" is="true">M</mi></mrow><mrow is="true"><mo stretchy="false" is="true">&#xAF;</mo></mrow></mover></mrow></mfenced><mo is="true">+</mo><mfenced close="|" open="|" is="true"><mrow is="true"><msub is="true"><mi mathvariant="normal" is="true">M</mi><mi mathvariant="normal" is="true">i</mi></msub><mo is="true">-</mo><mover accent="true" is="true"><mrow is="true"><mi mathvariant="normal" is="true">M</mi></mrow><mrow is="true"><mo stretchy="false" is="true">&#xAF;</mo></mrow></mover></mrow></mfenced><mo stretchy="false" is="true">)</mo></mrow><mn is="true">2</mn></msup></mrow></mfrac></mrow></math>' role=presentation><svg xmlns:xlink=http://www.w3.org/1999/xlink width=27.425ex height=5.317ex style=vertical-align:-2.197ex viewBox="0 -1343.3 11807.9 2289.2" role=img focusable=false aria-hidden=true><g stroke=currentColor fill=currentColor stroke-width=0 transform="matrix(1 0 0 -1 0 0)"><g is=true><g is=true><use href=#MJMAIN-64></use></g><g is=true transform=translate(834,0)><use href=#MJMAIN-3D></use></g><g is=true transform=translate(1890,0)><use href=#MJMAIN-31></use></g><g is=true transform=translate(2613,0)><use href=#MJMAIN-2212></use></g><g is=true transform=translate(3391,0)><g transform=translate(342,0)><rect stroke=none width=7953 height=60 x=0 y=220></rect><g is=true transform=translate(1653,606)><g is=true><g is=true><use transform=scale(0.707) href=#MJSZ1-2211></use></g><g is=true transform=translate(747,337)><use transform=scale(0.5) href=#MJMAIN-6E></use></g><g is=true transform=translate(747,-203)><g is=true><use transform=scale(0.5) href=#MJMAIN-69></use></g><g is=true transform=translate(139,0)><use transform=scale(0.5) href=#MJMAIN-3D></use></g><g is=true transform=translate(528,0)><use transform=scale(0.5) href=#MJMAIN-31></use></g></g></g><g is=true transform=translate(1763,0)><g is=true><g is=true><use transform=scale(0.707) href=#MJMAIN-28></use></g><g is=true transform=translate(275,0)><g is=true><use transform=scale(0.707) href=#MJMAIN-53></use></g><g is=true transform=translate(393,-107)><use transform=scale(0.5) href=#MJMAIN-69></use></g></g><g is=true transform=translate(878,0)><use transform=scale(0.707) href=#MJMAIN-2212></use></g><g is=true transform=translate(1429,0)><g is=true><use transform=scale(0.707) href=#MJMAIN-4D></use></g><g is=true transform=translate(648,-107)><use transform=scale(0.5) href=#MJMAIN-69></use></g></g><g is=true transform=translate(2288,0)><use transform=scale(0.707) href=#MJMAIN-29></use></g></g><g is=true transform=translate(2563,337)><use transform=scale(0.5) href=#MJMAIN-32></use></g></g></g><g is=true transform=translate(60,-636)><g is=true><g is=true><use transform=scale(0.707) href=#MJSZ1-2211></use></g><g is=true transform=translate(747,337)><use transform=scale(0.5) href=#MJMAIN-6E></use></g><g is=true transform=translate(747,-203)><g is=true><use transform=scale(0.5) href=#MJMAIN-69></use></g><g is=true transform=translate(139,0)><use transform=scale(0.5) href=#MJMAIN-3D></use></g><g is=true transform=translate(528,0)><use transform=scale(0.5) href=#MJMAIN-31></use></g></g></g><g is=true transform=translate(1763,0)><g is=true><g is=true><use transform=scale(0.707) href=#MJMAIN-28></use></g><g is=true transform=translate(275,0)><g transform=translate(0,592)><use transform=scale(0.707) href=#MJMAIN-2223 x=0 y=-752></use><use transform=scale(0.707) href=#MJMAIN-2223 x=0 y=-925></use></g><g is=true transform=translate(196,0)><g is=true><g is=true><use transform=scale(0.707) href=#MJMAIN-53></use></g><g is=true transform=translate(393,-107)><use transform=scale(0.5) href=#MJMAIN-69></use></g></g><g is=true transform=translate(603,0)><use transform=scale(0.707) href=#MJMAIN-2212></use></g><g is=true transform=translate(1153,0)><g is=true><g is=true><use transform=scale(0.707) href=#MJMAIN-4D></use></g></g><g is=true transform=translate(147,174)><g is=true><use transform=scale(0.707) href=#MJMAIN-AF></use></g></g></g></g><g transform=translate(1999,592)><use transform=scale(0.707) href=#MJMAIN-2223 x=0 y=-752></use><use transform=scale(0.707) href=#MJMAIN-2223 x=0 y=-925></use></g></g><g is=true transform=translate(2471,0)><use transform=scale(0.707) href=#MJMAIN-2B></use></g><g is=true transform=translate(3022,0)><g transform=translate(0,592)><use transform=scale(0.707) href=#MJMAIN-2223 x=0 y=-752></use><use transform=scale(0.707) href=#MJMAIN-2223 x=0 y=-925></use></g><g is=true transform=translate(196,0)><g is=true><g is=true><use transform=scale(0.707) href=#MJMAIN-4D></use></g><g is=true transform=translate(648,-107)><use transform=scale(0.5) href=#MJMAIN-69></use></g></g><g is=true transform=translate(858,0)><use transform=scale(0.707) href=#MJMAIN-2212></use></g><g is=true transform=translate(1409,0)><g is=true><g is=true><use transform=scale(0.707) href=#MJMAIN-4D></use></g></g><g is=true transform=translate(147,174)><g is=true><use transform=scale(0.707) href=#MJMAIN-AF></use></g></g></g></g><g transform=translate(2254,592)><use transform=scale(0.707) href=#MJMAIN-2223 x=0 y=-752></use><use transform=scale(0.707) href=#MJMAIN-2223 x=0 y=-925></use></g></g><g is=true transform=translate(5474,0)><use transform=scale(0.707) href=#MJMAIN-29></use></g></g><g is=true transform=translate(5749,399)><use transform=scale(0.5) href=#MJMAIN-32></use></g></g></g></g></g></g></g></svg><span class=MJX_Assistive_MathML role=presentation><math xmlns=http://www.w3.org/1998/Math/MathML><mrow is=true><mi mathvariant=normal is=true>d</mi><mo linebreak=goodbreak is=true>=</mo><mn is=true>1</mn><mo linebreak=badbreak is=true>-</mo><mfrac is=true><mrow is=true><msubsup is=true><mo is=true>∑</mo><mrow is=true><mi mathvariant=normal is=true>i</mi><mo is=true>=</mo><mn is=true>1</mn></mrow><mi mathvariant=normal is=true>n</mi></msubsup><msup is=true><mrow is=true><mo stretchy=false is=true>(</mo><msub is=true><mi mathvariant=normal is=true>S</mi><mi mathvariant=normal is=true>i</mi></msub><mo is=true>-</mo><msub is=true><mi mathvariant=normal is=true>M</mi><mi mathvariant=normal is=true>i</mi></msub><mo stretchy=false is=true>)</mo></mrow><mn is=true>2</mn></msup></mrow><mrow is=true><msubsup is=true><mo is=true>∑</mo><mrow is=true><mi mathvariant=normal is=true>i</mi><mo is=true>=</mo><mn is=true>1</mn></mrow><mi mathvariant=normal is=true>n</mi></msubsup><msup is=true><mrow is=true><mo stretchy=false is=true>(</mo><mfenced close=| open is=true><mrow is=true><msub is=true><mi mathvariant=normal is=true>S</mi><mi mathvariant=normal is=true>i</mi></msub><mo is=true>-</mo><mover accent=true is=true><mrow is=true><mi mathvariant=normal is=true>M</mi></mrow><mrow is=true><mo stretchy=false is=true>¯</mo></mrow></mover></mrow></mfenced><mo is=true>+</mo><mfenced close=| open is=true><mrow is=true><msub is=true><mi mathvariant=normal is=true>M</mi><mi mathvariant=normal is=true>i</mi></msub><mo is=true>-</mo><mover accent=true is=true><mrow is=true><mi mathvariant=normal is=true>M</mi></mrow><mrow is=true><mo stretchy=false is=true>¯</mo></mrow></mover></mrow></mfenced><mo stretchy=false is=true>)</mo></mrow><mn is=true>2</mn></msup></mrow></mfrac></mrow></math></span></span></span></span></span>where S<sub>i</sub> is the simulated value, M<sub>i</sub> is the measured value, i = 1, …, n is the number of measured values and <span class=math><span class=MathJax_Preview></span><span style=font-size:90%;display:inline-block;position:relative class=MathJax_SVG id=MathJax-Element-13-Frame tabindex=0 data-mathml='<math xmlns="http://www.w3.org/1998/Math/MathML"><mover accent="true" is="true"><mrow is="true"><mi mathvariant="normal" is="true">M</mi></mrow><mrow is="true"><mo stretchy="false" is="true">&#xAF;</mo></mrow></mover></math>' role=presentation><svg xmlns:xlink=http://www.w3.org/1999/xlink width=2.131ex height=2.317ex style=vertical-align:-0.235ex viewBox="0 -896.2 917.5 997.6" role=img focusable=false aria-hidden=true><g stroke=currentColor fill=currentColor stroke-width=0 transform="matrix(1 0 0 -1 0 0)"><g is=true><g is=true><g is=true><use href=#MJMAIN-4D></use></g></g><g is=true transform=translate(208,215)><g is=true><use href=#MJMAIN-AF></use></g></g></g></g></svg><span class=MJX_Assistive_MathML role=presentation><math xmlns=http://www.w3.org/1998/Math/MathML><mover accent=true is=true><mrow is=true><mi mathvariant=normal is=true>M</mi></mrow><mrow is=true><mo stretchy=false is=true>¯</mo></mrow></mover></math></span></span></span> is the mean of the measured values.<p id=p0085>The PBIAS indicates the average tendency of the simulated data to underestimate (negative value) or overestimate (positive value) the measured data (<a class="anchor u-display-inline anchor-paragraph" href=#b0215 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0215><span class=anchor-text>Moriasi et al., 2007</span></a>). The nRMSE shows the relative size of the average difference without units, and this statistic is unbounded (<a class="anchor u-display-inline anchor-paragraph" href=#b0265 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0265><span class=anchor-text>Priesack et al., 2006</span></a>). The NSE (−∞ to 1) is a normalized measure that determines the relative magnitude of model residuals compared to the measured variance (<a class="anchor u-display-inline anchor-paragraph" href=#b0215 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0215><span class=anchor-text>Moriasi et al., 2007</span></a>). The index of agreement (d) (0 ≤ d ≤ 1) is intended to be a descriptive measure, and it is both a relative and bounded measure (<a class="anchor u-display-inline anchor-paragraph" href=#b0335 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0335><span class=anchor-text>Willmott, 1982</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0130 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0130><span class=anchor-text>Krause et al., 2005</span></a>). Therefore, using different indicators to estimate the model performance can ensure the accuracy of model simulation based on previous studies. In this study, we consider a “fair” agreement between the simulated and measured data when PBIAS was within < ±25% and < ±70% for N (<a class="anchor u-display-inline anchor-paragraph" href=#b0215 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0215><span class=anchor-text>Moriasi et al., 2007</span></a>). An agreement is said to be “excellent” when the nRMSE is ≤10%, “good” when 10 < nRMSE ≤ 20%, “fair” when 20% < nRMSE ≤ 30%, and “poor” when nRMSE > 30% (<a class="anchor u-display-inline anchor-paragraph" href=#b0170 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0170><span class=anchor-text>Liu et al., 2013</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0100 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0100><span class=anchor-text>He et al., 2018a</span></a>). The NSE criteria should be project-specific so as to increase the efficiency of evaluation based on previous studies (<a class="anchor u-display-inline anchor-paragraph" href=#b0210 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0210><span class=anchor-text>Motovilov et al., 1999</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0215 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0215><span class=anchor-text>Moriasi et al., 2007</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0365 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0365><span class=anchor-text>Yang et al., 2014</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0330 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0330><span class=anchor-text>Waseem et al., 2017</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0005 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0005><span class=anchor-text>Abebe and Gebremariam, 2019</span></a>). In our study, we consider “perfect” model performance when NSE value = 1.0, “good” performance when 0.5 < NSE < 1, “fair” performance when 0.0 ≤ NSE ≤ 0.5, and “poor” performance when NSE < 0.0. A value of d ≥ 0.9 is considered “excellent” agreement, 0.8 ≤ d < 0.9 is considered “good” agreement, 0.7 ≤ d < 0.8 is considered “fair” agreement, and d < 0.7 is considered “poor” agreement when comparing the simulated and measured values (<a class="anchor u-display-inline anchor-paragraph" href=#b0170 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0170><span class=anchor-text>Liu et al., 2013</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0100 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0100><span class=anchor-text>He et al., 2018a</span></a>).<p id=p0090>Additionally, data were compared using the paired-<em>t</em> test from IBM SPSS 19.0 software to determine whether the differences between measured and simulated values were statistically significant at <em>p</em> < 0.05.</p></section><section id=s0045><h3 id=st060 class="u-h4 u-margin-m-top u-margin-xs-bottom">2.5. Sensitivity analysis</h3><div><p id=p0095>Sensitivity analysis is a fundamental tool in the use and understanding of simulation models as it can be used to indicate the variation range of the output variable after changing one input parameter value within the specific boundaries while keeping all other inputs at their default values (<a class="anchor u-display-inline anchor-paragraph" href=#b0205 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0205><span class=anchor-text>Misra and Rose, 1996</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0015 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0015><span class=anchor-text>Bert et al., 2007</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0150 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0150><span class=anchor-text>Li et al., 2015</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0095 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0095><span class=anchor-text>He et al., 2016</span></a>). In this study, the sensitivity analyses of the DNDC and DSSAT models were used to explore best management practices including the planting date, planting density, tillage depth and fertilizer rate and times (<a class="anchor u-display-inline anchor-paragraph" href=#t0010 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0010><span class=anchor-text>Table 2</span></a><span>). A 20-year historical weather data (1996–2015) was used to explore the best management practices for improving maize yield and <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/nutrient-use-efficiency title="Learn more about nutrient use efficiency from ScienceDirect's AI-generated Topic Pages" class=topic-link>nutrient use efficiency</a> (Fig. A. 1). The planting date in the sensitivity analysis ranged from 3 April (93) to 29 May (149) with a 7-day interval based on farmers’ practices in northeast China (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0340 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0340><span class=anchor-text>Xu et al., 2014a</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0345 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0345><span class=anchor-text>Xu et al., 2014b</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0410 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0410><span class=anchor-text>Zhao et al., 2015</span></a>). The sensitivity level of the planting density ranged from 3 to 10 seeds m<sup>−2</sup> (<a class="anchor u-display-inline anchor-paragraph" href=#b0385 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0385><span class=anchor-text>Zhang et al., 2014</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0400 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0400><span class=anchor-text>Zhang et al., 2017a</span></a>), with a 1 seed m<sup>−2</sup> interval conducted only by the DSSAT model as the DNDC model is incapable of adjusting planting density. The tillage depth was conducted to encompass 0, 0.05, 0.10, 0.20 and 0.30 m (<a class="anchor u-display-inline anchor-paragraph" href=#b0395 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0395><span class=anchor-text>Zhang et al., 2015b</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0250 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0250><span class=anchor-text>Piao et al., 2016</span></a>). The fertilizer N application rate was set to vary from 0 to 300 kg N ha<sup>−1</sup> with a 30 kg N ha<sup>−1</sup> interval (<a class="anchor u-display-inline anchor-paragraph" href=#b0230 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0230><span class=anchor-text>Niu et al., 2013</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0340 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0340><span class=anchor-text>Xu et al., 2014a</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0345 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0345><span class=anchor-text>Xu et al., 2014b</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0350 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0350><span class=anchor-text>Xu et al., 2016</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0325 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0325><span class=anchor-text>Wang et al., 2016</span></a>). The fertilization time was considered according to three main growth stages: basal, jointing and tasselling. The ratios of two-splitting fertilizer were 1/2:1/2, 1/3:2/3 and 2/3:1/3 and at basal and 1/3:1/3:1/3, 1/4:2/4:1/4, 2/4:1/4:1/4 and 1/4:1/4:2/4 for three-splitting, respectively. More detailed information is listed in <a class="anchor u-display-inline anchor-paragraph" href=#t0010 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0010><span class=anchor-text>Table 2</span></a>. All input parameters of sensitivity analysis are based on the default values from current farmers’ practice in northeast China, the default cultivar, planting date, planting density, tillage depth and fertilizer rate are Pioneer335, 1 May, 6.0 seeds m<sup>−2</sup>, 0.20 m and 251 kg N ha<sup>−1</sup>, respectively.<div class="tables frame-topbot rowsep-0 colsep-0" id=t0010><span class="captions text-s"><span id=cn0035><p id=sp0045><span class=label>Table 2</span>. The parameter levels for sensitivity analysis at Liufangzi, Jilin, China.</p></span></span><div class=groups><table><thead><tr class=valign-top><th scope=col class=align-left>Levels<th scope=col class=align-left>Planting date<th scope=col class=align-left>Planting density<th scope=col class=align-left>Tillage depth<th scope=col class=align-left>N application rate<th scope=col class="align-left rowsep-1" colspan=3>N application ratio<tr class="rowsep-1 valign-top"><td scope=col class=align-left><span class=screen-reader-only>Empty Cell</span><th scope=col class=align-left>(day of year)<th scope=col class=align-left>(seed m<sup>−2</sup>)<th scope=col class=align-left>(m)<th scope=col class=align-left>(kg N ha9<sup>−1</sup>)<th scope=col class=align-left>Basal<th scope=col class=align-left>Jointing<th scope=col class=align-left>Tasseling<tbody><tr class=valign-top><td class=align-left>1<td class=align-left>03-Apr (93)<a class="anchor u-display-inline anchor-paragraph" href=#tblfn2 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=tblfn2><span class=anchor-text><sup>a</sup></span></a><td class=align-left>3<td class=align-left>0<td class=align-left>0<td class=align-left>1<td class=align-left>–<td class=align-left>–<tr class=valign-top><td class=align-left>2<td class=align-left>10-Apr (100)<td class=align-left>4<td class=align-left>0.05<td class=align-left>30<td class=align-left>1/2<td class=align-left>1/2<td class=align-left>–<tr class=valign-top><td class=align-left>3<td class=align-left>17-Apr (107)<td class=align-left>5<td class=align-left>0.10<td class=align-left>60<td class=align-left>1/3<td class=align-left>2/3<td class=align-left>–<tr class=valign-top><td class=align-left>4<td class=align-left>24-Apr (114)<td class=align-left>6<td class=align-left>0.20<td class=align-left>90<td class=align-left>2/3<td class=align-left>1/3<td class=align-left>–<tr class=valign-top><td class=align-left>5<td class=align-left>01-May (121)<td class=align-left>7<td class=align-left>0.30<td class=align-left>120<td class=align-left>1/3<td class=align-left>1/3<td class=align-left>1/3<tr class=valign-top><td class=align-left>6<td class=align-left>08-May (128)<td class=align-left>8<td class=align-left><td class=align-left>150<td class=align-left>1/4<td class=align-left>2/4<td class=align-left>2/4<tr class=valign-top><td class=align-left>7<td class=align-left>15-May (135)<td class=align-left>9<td class=align-left><td class=align-left>180<td class=align-left>1/4<td class=align-left>1/4<td class=align-left>2/4<tr class=valign-top><td class=align-left>8<td class=align-left>22-May (142)<td class=align-left>10<td class=align-left><td class=align-left>210<td class=align-left>2/4<td class=align-left>1/4<td class=align-left>1/4<tr class=valign-top><td class=align-left>9<td class=align-left>29-May (149)<td class=align-left><td class=align-left><td class=align-left>240<td class=align-left><td class=align-left><td class=align-left><tr class=valign-top><td class=align-left>10<td class=align-left><td class=align-left><td class=align-left><td class=align-left>270<td class=align-left><td class=align-left><td class=align-left><tr class=valign-top><td class=align-left>11<td class=align-left><td class=align-left><td class=align-left><td class=align-left>300<td class=align-left><td class=align-left><td class=align-left></table></div><dl class=footnotes><dt id=tblfn2>a<dd><p id=np010>The content within the brackets represents the day of year.</p></dl></div></div></section><section id=s0050><h3 id=st065 class="u-h4 u-margin-m-top u-margin-xs-bottom">2.6. Nitrogen use efficiency</h3><p id=p0100>The nitrogen use efficiency parameters included the agronomic efficiency of N (AEN, kg kg<sup>−1</sup>), the partial factor productivity of N (PFPN, kg kg<sup>−1</sup>) and the recovery efficiency of N (REN, %), which were calculated using Eqs. <a class="anchor u-display-inline anchor-paragraph" href=#e0025 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=e0025><span class=anchor-text>(5)</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#e0030 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=e0030><span class=anchor-text>(6)</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#e0035 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=e0035><span class=anchor-text>(7)</span></a>:<span class=display><span id=e0025 class=formula><span class=label>(5)</span><span class=math><span class=MathJax_Preview></span><span style=font-size:90%;display:inline-block;position:relative class=MathJax_SVG id=MathJax-Element-14-Frame tabindex=0 data-mathml='<math xmlns="http://www.w3.org/1998/Math/MathML"><mrow is="true"><mi mathvariant="normal" is="true">A</mi><mi mathvariant="normal" is="true">E</mi><mi mathvariant="normal" is="true">N</mi><mo linebreak="goodbreak" is="true">=</mo><mfrac is="true"><mrow is="true"><msub is="true"><mrow is="true"><mi mathvariant="normal" is="true">G</mi><mi mathvariant="normal" is="true">Y</mi></mrow><mi is="true">N</mi></msub><mo is="true">-</mo><msub is="true"><mrow is="true"><mi mathvariant="normal" is="true">G</mi><mi mathvariant="normal" is="true">Y</mi></mrow><mrow is="true"><mi is="true">N</mi><mn is="true">0</mn></mrow></msub></mrow><msub is="true"><mi mathvariant="normal" is="true">N</mi><mrow is="true"><mi mathvariant="italic" is="true">rate</mi></mrow></msub></mfrac></mrow></math>' role=presentation><svg 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xmlns=http://www.w3.org/1998/Math/MathML><mrow is=true><mi mathvariant=normal is=true>A</mi><mi mathvariant=normal is=true>E</mi><mi mathvariant=normal is=true>N</mi><mo linebreak=goodbreak is=true>=</mo><mfrac is=true><mrow is=true><msub is=true><mrow is=true><mi mathvariant=normal is=true>G</mi><mi mathvariant=normal is=true>Y</mi></mrow><mi is=true>N</mi></msub><mo is=true>-</mo><msub is=true><mrow is=true><mi mathvariant=normal is=true>G</mi><mi mathvariant=normal is=true>Y</mi></mrow><mrow is=true><mi is=true>N</mi><mn is=true>0</mn></mrow></msub></mrow><msub is=true><mi mathvariant=normal is=true>N</mi><mrow is=true><mi mathvariant=italic is=true>rate</mi></mrow></msub></mfrac></mrow></math></span></span></span></span></span><span class=display><span id=e0030 class=formula><span class=label>(6)</span><span class=math><span class=MathJax_Preview></span><span style=font-size:90%;display:inline-block;position:relative class=MathJax_SVG id=MathJax-Element-15-Frame tabindex=0 data-mathml='<math xmlns="http://www.w3.org/1998/Math/MathML"><mrow is="true"><mi mathvariant="normal" is="true">P</mi><mi mathvariant="normal" is="true">F</mi><mi mathvariant="normal" is="true">P</mi><mi mathvariant="normal" is="true">N</mi><mo linebreak="goodbreak" is="true">=</mo><mfrac is="true"><msub is="true"><mrow is="true"><mi mathvariant="normal" is="true">G</mi><mi mathvariant="normal" is="true">Y</mi></mrow><mi is="true">N</mi></msub><msub is="true"><mi mathvariant="normal" is="true">N</mi><mrow is="true"><mi mathvariant="italic" is="true">rate</mi></mrow></msub></mfrac></mrow></math>' role=presentation><svg xmlns:xlink=http://www.w3.org/1999/xlink width=14.08ex height=3.932ex style=vertical-align:-1.389ex viewBox="0 -1094.9 6062.1 1693.1" role=img focusable=false aria-hidden=true><g stroke=currentColor fill=currentColor stroke-width=0 transform="matrix(1 0 0 -1 0 0)"><g is=true><g is=true><use href=#MJMAIN-50></use></g><g is=true transform=translate(681,0)><use href=#MJMAIN-46></use></g><g is=true transform=translate(1335,0)><use href=#MJMAIN-50></use></g><g is=true transform=translate(2016,0)><use href=#MJMAIN-4E></use></g><g is=true transform=translate(3044,0)><use href=#MJMAIN-3D></use></g><g is=true transform=translate(3823,0)><g transform=translate(397,0)><rect stroke=none width=1721 height=60 x=0 y=220></rect><g is=true transform=translate(60,509)><g is=true><g is=true><use transform=scale(0.707) href=#MJMAIN-47></use></g><g is=true transform=translate(555,0)><use transform=scale(0.707) href=#MJMAIN-59></use></g></g><g is=true transform=translate(1086,-107)><use transform=scale(0.5) href=#MJMATHI-4E></use></g></g><g is=true transform=translate(107,-387)><g is=true><use transform=scale(0.707) href=#MJMAIN-4E></use></g><g is=true transform=translate(530,-107)><g is=true><use transform=scale(0.5) href=#MJMATHI-72></use><use transform=scale(0.5) href=#MJMATHI-61 x=451 y=0></use><use transform=scale(0.5) href=#MJMATHI-74 x=981 y=0></use><use transform=scale(0.5) href=#MJMATHI-65 x=1342 y=0></use></g></g></g></g></g></g></g></svg><span class=MJX_Assistive_MathML role=presentation><math xmlns=http://www.w3.org/1998/Math/MathML><mrow is=true><mi mathvariant=normal is=true>P</mi><mi mathvariant=normal is=true>F</mi><mi mathvariant=normal is=true>P</mi><mi mathvariant=normal is=true>N</mi><mo linebreak=goodbreak is=true>=</mo><mfrac is=true><msub is=true><mrow is=true><mi mathvariant=normal is=true>G</mi><mi mathvariant=normal is=true>Y</mi></mrow><mi is=true>N</mi></msub><msub is=true><mi mathvariant=normal is=true>N</mi><mrow is=true><mi mathvariant=italic is=true>rate</mi></mrow></msub></mfrac></mrow></math></span></span></span></span></span><span class=display><span id=e0035 class=formula><span class=label>(7)</span><span class=math><span class=MathJax_Preview></span><span style=font-size:90%;display:inline-block;position:relative class=MathJax_SVG id=MathJax-Element-16-Frame tabindex=0 data-mathml='<math xmlns="http://www.w3.org/1998/Math/MathML"><mrow is="true"><mi mathvariant="normal" is="true">R</mi><mi mathvariant="normal" is="true">E</mi><mi mathvariant="normal" is="true">N</mi><mo linebreak="goodbreak" is="true">=</mo><mfrac is="true"><mrow is="true"><msub is="true"><mi mathvariant="normal" is="true">U</mi><mi is="true">N</mi></msub><mo is="true">-</mo><msub is="true"><mi mathvariant="normal" is="true">U</mi><mrow is="true"><mi is="true">N</mi><mn is="true">0</mn></mrow></msub></mrow><msub is="true"><mi mathvariant="normal" is="true">N</mi><mrow is="true"><mi mathvariant="italic" is="true">rate</mi></mrow></msub></mfrac><mo is="true">&#xD7;</mo><mn is="true">100</mn></mrow></math>' role=presentation><svg xmlns:xlink=http://www.w3.org/1999/xlink width=22.016ex height=3.932ex style=vertical-align:-1.389ex viewBox="0 -1094.9 9479 1693.1" role=img focusable=false aria-hidden=true><g stroke=currentColor fill=currentColor stroke-width=0 transform="matrix(1 0 0 -1 0 0)"><g is=true><g is=true><use href=#MJMAIN-52></use></g><g is=true transform=translate(736,0)><use href=#MJMAIN-45></use></g><g is=true transform=translate(1418,0)><use href=#MJMAIN-4E></use></g><g is=true transform=translate(2446,0)><use href=#MJMAIN-3D></use></g><g is=true transform=translate(3224,0)><g transform=translate(397,0)><rect stroke=none width=3012 height=60 x=0 y=220></rect><g is=true transform=translate(60,520)><g is=true><g is=true><use transform=scale(0.707) href=#MJMAIN-55></use></g><g is=true transform=translate(530,-107)><use transform=scale(0.5) href=#MJMATHI-4E></use></g></g><g is=true transform=translate(1045,0)><use transform=scale(0.707) href=#MJMAIN-2212></use></g><g is=true transform=translate(1596,0)><g is=true><use transform=scale(0.707) href=#MJMAIN-55></use></g><g is=true transform=translate(530,-107)><g is=true><use transform=scale(0.5) href=#MJMATHI-4E></use></g><g is=true transform=translate(444,0)><use transform=scale(0.5) href=#MJMAIN-30></use></g></g></g></g><g is=true transform=translate(753,-387)><g is=true><use transform=scale(0.707) href=#MJMAIN-4E></use></g><g is=true transform=translate(530,-107)><g is=true><use transform=scale(0.5) href=#MJMATHI-72></use><use transform=scale(0.5) href=#MJMATHI-61 x=451 y=0></use><use transform=scale(0.5) href=#MJMATHI-74 x=981 y=0></use><use transform=scale(0.5) href=#MJMATHI-65 x=1342 y=0></use></g></g></g></g></g><g is=true transform=translate(6976,0)><use href=#MJMAIN-D7></use></g><g is=true transform=translate(7977,0)><use href=#MJMAIN-31></use><use href=#MJMAIN-30 x=500 y=0></use><use href=#MJMAIN-30 x=1001 y=0></use></g></g></g></svg><span class=MJX_Assistive_MathML role=presentation><math xmlns=http://www.w3.org/1998/Math/MathML><mrow is=true><mi mathvariant=normal is=true>R</mi><mi mathvariant=normal is=true>E</mi><mi mathvariant=normal is=true>N</mi><mo linebreak=goodbreak is=true>=</mo><mfrac is=true><mrow is=true><msub is=true><mi mathvariant=normal is=true>U</mi><mi is=true>N</mi></msub><mo is=true>-</mo><msub is=true><mi mathvariant=normal is=true>U</mi><mrow is=true><mi is=true>N</mi><mn is=true>0</mn></mrow></msub></mrow><msub is=true><mi mathvariant=normal is=true>N</mi><mrow is=true><mi mathvariant=italic is=true>rate</mi></mrow></msub></mfrac><mo is=true>×</mo><mn is=true>100</mn></mrow></math></span></span></span></span></span>where GY<em><sub>N</sub></em> is the maize yield (kg ha<sup>−1</sup>) for the EI-N and FP-N treatments, and GY<em><sub>N0</sub></em> is the maize yield (kg ha<sup>−1</sup>) for the EI-N0 and FP-N0 treatments; U<em><sub>N</sub></em> is the plant N uptake at maturity (kg N ha<sup>−1</sup>) for the EI-N and FP-N treatments and U<em><sub>N0</sub></em> is the plant N uptake at maturity (kg N ha<sup>−1</sup>) for the EI-N0 and FP-N0 treatments. N<em>rate</em> is the fertilizer N application rate (<a class="anchor u-display-inline anchor-paragraph" href=#b0350 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0350><span class=anchor-text>Xu et al., 2016</span></a>).</p></section></section><section id=s0055><h2 id=st070 class="u-h4 u-margin-l-top u-margin-xs-bottom">3. Results and discussion</h2><section id=s0060><h3 id=st075 class="u-h4 u-margin-m-top u-margin-xs-bottom">3.1. Model calibration and evaluation</h3><section id=s0065><h4 id=st080 class="u-margin-m-top u-margin-xs-bottom">3.1.1. Crop growth</h4><div><p id=p0105>Calibration of the DNDC and DSSAT model indicated that “good” to “excellent” agreement between the simulated and measured maize yields and above-ground biomass for the EI-N and FP-N treatment based on the values of −7.0% ≤ PBIAS ≤ 1.9%, 11.3% ≤ nRMSE ≤ 7.5%, 0.32 ≤ NSE ≤ 0.77, and 0.88 ≤ d ≤ 0.94 (<a class="anchor u-display-inline anchor-paragraph" href=#f0005 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0005><span class=anchor-text>Fig. 1</span></a> and A. 2, 3, <a class="anchor u-display-inline anchor-paragraph" href=#t0015 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0015><span class=anchor-text>Table 3</span></a>). Both models underestimated maize yields for the EI-N treatment and overestimated maize yields for the FP-N treatment, but there was no significant difference between the simulated and measured data. These results demonstrated that the DNDC and DSSAT models matched well between the simulated and measured maize yields and above-ground biomass of the N-fertilized (EI-N and FP-N) treatments. For plant N uptake, both the models showed “good” agreement, with the −10.5% ≤ PBIAS ≤ 4.2%, 11.4% ≤ nRMSE ≤ 17.2%, 0.17 ≤ NSE ≤ 0.63 and 0.81 ≤ d ≤ 0.89 between the simulated and measured data under N-fertilized treatments (<a class="anchor u-display-inline anchor-paragraph" href=#f0010 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0010><span class=anchor-text>Fig. 2</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#t0015 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0015><span class=anchor-text>Table 3</span></a>). Overall, the N use efficiency was calculated based on the successfully calibrated N uptake in the DNDC and DSSAT models (<a class="anchor u-display-inline anchor-paragraph" href=#t0020 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0020><span class=anchor-text>Table 4</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#s0120 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=s0120><span class=anchor-text>Fig. A. 4</span></a>). Compared to the measured plant N uptake, the DNDC model underestimated the N uptake for the FP-N treatments (<em>p</em> > 0.05); in contrast, the DSSAT model significantly (<em>p</em> < 0.05) overestimated the N uptake. A similar finding reported by <a class="anchor u-display-inline anchor-paragraph" href=#b0165 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0165><span class=anchor-text>Liu et al. (2012)</span></a> indicated that the over-prediction of the N uptake of maize in the DSSAT model might be due to an overestimation of N mineralization rate. In addition, the plant N uptake would decrease when the value of the N stress coefficient for changes in concentration with growth stage (CTCNP2) increased by 20–60% in the DSSAT model. However, the influence of a high CTCNP2 coefficient could reduce the sensitivity of crop growth to the fertilizer N rate. The long-term simulation of plant N uptake and soil N mineralization rate under excessive fertilization should be considered in further development of the DSSAT model.<figure class="figure text-xs" id=f0005><span><img 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" height=501 alt aria-describedby=cn0005><ol class=u-margin-s-bottom><li><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0168169919306532-gr1_lrg.jpg target=_blank download title="Download high-res image (315KB)"><span class=anchor-text>Download : <span class=download-link-title>Download high-res image (315KB)</span></span></a><li><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0168169919306532-gr1.jpg target=_blank download title="Download full-size image"><span class=anchor-text>Download : <span class=download-link-title>Download full-size image</span></span></a></ol></span><span class="captions text-s"><span id=cn0005><p id=sp0015><span class=label>Fig. 1</span>. Measured and simulated maize yield from 2009 to 2015 for (a) ecological intensification with <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/nitrogen-fertilizer title="Learn more about nitrogen fertilizer from ScienceDirect's AI-generated Topic Pages" class=topic-link>nitrogen fertilizer</a> (EI-N), (b) EI without nitrogen fertilizer (EI-N0), (c) farmers’ practice with nitrogen fertilizer (FP-N) and (d) FP without nitrogen fertilizer (FP-N0). Bars are standard deviations (n = 4).</p></span></span></figure><div class="tables frame-topbot rowsep-0 colsep-0" id=t0015><span class="captions text-s"><span id=cn0040><p id=sp0050><span class=label>Table 3</span>. Statistics of model calibration and evaluation between the measured and simulated maize yield and nitrogen uptake at Liufangzi, Jilin, China.</p></span></span><div class=groups><table><thead><tr class=valign-top><th scope=col class=align-left>Variable<th scope=col class=align-left>Treatment<th scope=col class=align-left>Measured<th scope=col class="align-left rowsep-1" colspan=2>Simulated<th scope=col class="align-left rowsep-1" colspan=2>PBIAS<a class="anchor u-display-inline anchor-paragraph" href=#tblfn3 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=tblfn3><span class=anchor-text><sup>a</sup></span></a> (%)<th scope=col class="align-left rowsep-1" colspan=2>nRMSE (%)<th scope=col class="align-left rowsep-1" colspan=2>NSE<th scope=col class="align-left rowsep-1" colspan=2>d<th scope=col class="align-left rowsep-1" colspan=2>Paired <em>t</em> test (<em>p</em>)<tr class="rowsep-1 valign-top"><td scope=col class=align-left><span class=screen-reader-only>Empty Cell</span><td scope=col class=align-left><span class=screen-reader-only>Empty Cell</span><td scope=col class=align-left><span class=screen-reader-only>Empty Cell</span><th scope=col class=align-left>DNDC<th scope=col class=align-left>DSSAT<th scope=col class=align-left>DNDC<th scope=col class=align-left>DSSAT<th scope=col class=align-left>DNDC<th scope=col class=align-left>DSSAT<th scope=col class=align-left>DNDC<th scope=col class=align-left>DSSAT<th scope=col class=align-left>DNDC<th scope=col class=align-left>DSSAT<th scope=col class=align-left>DNDC<th scope=col class=align-left>DSSAT<tbody><tr class=valign-top><td class=align-left>Maize yield<td class=align-left>EI-N<a class="anchor u-display-inline anchor-paragraph" href=#tblfn4 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=tblfn4><span class=anchor-text><sup>b</sup></span></a><td class=align-left>9582<td class=align-left>9400<td class=align-left>9552<td class=align-left>1.9<td class=align-left>0.3<td class=align-left>9.1<td class=align-left>7.5<td class=align-left>0.32<td class=align-left>0.53<td class=align-left>0.88<td class=align-left>0.92<td class=align-left>0.28<td class=align-left>0.82<tr class=valign-top><td class=align-left>(kg ha<sup>−1</sup>)<td class=align-left>EI-N0<td class=align-left>2938<td class=align-left>2279<td class=align-left>2696<td class=align-left>22.4<td class=align-left>8.3<td class=align-left>38.4<td class=align-left>26.0<td class=align-left>0.02<td class=align-left>0.55<td class=align-left>0.71<td class=align-left>0.83<td class=align-left>0.01<td class=align-left>0.09<tr class=valign-top><td class=align-left><td class=align-left>FP-N<td class=align-left>9043<td class=align-left>9328<td class=align-left>9145<td class=align-left>−3.2<td class=align-left>−1.1<td class=align-left>9.5<td class=align-left>7.7<td class=align-left>0.59<td class=align-left>0.73<td class=align-left>0.91<td class=align-left>0.94<td class=align-left>0.07<td class=align-left>0.12<tr class=valign-top><td class=align-left><td class=align-left>FP-N0<td class=align-left>2916<td class=align-left>2256<td class=align-left>2775<td class=align-left>22.6<td class=align-left>4.8<td class=align-left>39.1<td class=align-left>25.1<td class=align-left>0.10<td class=align-left>0.63<td class=align-left>0.72<td class=align-left>0.86<td class=align-left>0.01<td class=align-left>0.32<tr class=valign-top><td class=align-left colspan=15><br><tr class=valign-top><td class=align-left>Nitrogen uptake<td class=align-left>EI-N<td class=align-left>171<td class=align-left>164<td class=align-left>179<td class=align-left>4.2<td class=align-left>−5.0<td class=align-left>11.7<td class=align-left>12.5<td class=align-left>0.55<td class=align-left>0.48<td class=align-left>0.86<td class=align-left>0.87<td class=align-left>0.06<td class=align-left>0.06<tr class=valign-top><td class=align-left>(kg N ha<sup>−1</sup>)<td class=align-left>EI-N0<td class=align-left>42<td class=align-left>48<td class=align-left>47<td class=align-left>−15.1<td class=align-left>−12.2<td class=align-left>42.7<td class=align-left>32.3<td class=align-left>0.22<td class=align-left>0.55<td class=align-left>0.70<td class=align-left>0.82<td class=align-left>0.06<td class=align-left>0.05<tr class=valign-top><td class=align-left><td class=align-left>FP-N<td class=align-left>175<td class=align-left>171<td class=align-left>193<td class=align-left>2.4<td class=align-left>−10.5<td class=align-left>11.4<td class=align-left>17.2<td class=align-left>0.63<td class=align-left>0.17<td class=align-left>0.89<td class=align-left>0.81<td class=align-left>0.31<td class=align-left>0.09<tr class=valign-top><td class=align-left><td class=align-left>FP-N0<td class=align-left>44<td class=align-left>48<td class=align-left>53<td class=align-left>−9.5<td class=align-left>−20.4<td class=align-left>45.2<td class=align-left>30.3<td class=align-left>0.32<td class=align-left>0.70<td class=align-left>0.70<td class=align-left>0.90<td class=align-left>0.27<td class=align-left>0.00</table></div><dl class=footnotes><dt id=tblfn3>a<dd><p id=np015>PBIAS: percent bias; nRMSE: normalized root mean square error; NSE: Nash-Sutcliffe efficiency; d: index of agreement.</p><dt id=tblfn4>b<dd><p id=np020>EI-N: ecological intensification with nitrogen fertilizer; EI-N0: EI without nitrogen fertilizer; FP-N: farmers’ practice with nitrogen fertilizer; FP-N0: FP without nitrogen fertilizer.</p></dl></div><figure class="figure text-xs" id=f0010><span><img 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" height=526 alt aria-describedby=cn0010><ol class=u-margin-s-bottom><li><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0168169919306532-gr2_lrg.jpg target=_blank download title="Download high-res image (338KB)"><span class=anchor-text>Download : <span class=download-link-title>Download high-res image (338KB)</span></span></a><li><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0168169919306532-gr2.jpg target=_blank download title="Download full-size image"><span class=anchor-text>Download : <span class=download-link-title>Download full-size image</span></span></a></ol></span><span class="captions text-s"><span id=cn0010><p id=sp0020><span class=label>Fig. 2</span>. Measured and simulated plant nitrogen uptake from 2009 to 2015 for (a) ecological intensification with <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/nitrogen-fertilizer title="Learn more about nitrogen fertilizer from ScienceDirect's AI-generated Topic Pages" class=topic-link>nitrogen fertilizer</a> (EI-N), (b) EI without nitrogen fertilizer (EI-N0), (c) farmers’ practice with nitrogen fertilizer (FP-N) and (d) FP without nitrogen fertilizer (FP-N0). Bars are standard deviations (n = 4).</p></span></span></figure><div class="tables frame-topbot rowsep-0 colsep-0" id=t0020><span class="captions text-s"><span id=cn0045><p id=sp0055><span class=label>Table 4</span>. Statistical of model calibration and evaluations between the measured and simulated agronomic efficiency of N (AEN), partial factor productivity of N (PFPN) and recover efficiency of N (REN) at Liufangzi, Jilin, China.</p></span></span><div class=groups><table><thead><tr class=valign-top><th scope=col class=align-left>Treatment<th scope=col class=align-left>Variable<th scope=col class=align-left>Measured<th scope=col class="align-left rowsep-1" colspan=2>Simulated<th scope=col class="align-left rowsep-1" colspan=2>PBIAS<a class="anchor u-display-inline anchor-paragraph" href=#tblfn5 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=tblfn5><span class=anchor-text><sup>a</sup></span></a> (%)<th scope=col class="align-left rowsep-1" colspan=2>nRMSE (%)<th scope=col class="align-left rowsep-1" colspan=2>NSE<th scope=col class="align-left rowsep-1" colspan=2>d<th scope=col class="align-left rowsep-1" colspan=2>Paired <em>t</em> test (<em>p</em>)<tr class="rowsep-1 valign-top"><td scope=col class=align-left><span class=screen-reader-only>Empty Cell</span><td scope=col class=align-left><span class=screen-reader-only>Empty Cell</span><td scope=col class=align-left><span class=screen-reader-only>Empty Cell</span><th scope=col class=align-left>DNDC<th scope=col class=align-left>DSSAT<th scope=col class=align-left>DNDC<th scope=col class=align-left>DSSAT<th scope=col class=align-left>DNDC<th scope=col class=align-left>DSSAT<th scope=col class=align-left>DNDC<th scope=col class=align-left>DSSAT<th scope=col class=align-left>DNDC<th scope=col class=align-left>DSSAT<th scope=col class=align-left>DNDC<th scope=col class=align-left>DSSAT<tbody><tr class=valign-top><td class=align-left>EI-N<a class="anchor u-display-inline anchor-paragraph" href=#tblfn6 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=tblfn6><span class=anchor-text><sup>b</sup></span></a><td class=align-left>AEN (kg kg<sup>−1</sup>)<td class=align-left>43.0<td class=align-left>43.0<td class=align-left>36.4<td class=align-left>7.9<td class=align-left>15.3<td class=align-left>22.8<td class=align-left>21.4<td class=align-left>0.33<td class=align-left>0.42<td class=align-left>0.81<td class=align-left>0.85<td class=align-left>0.06<td class=align-left>0.05<tr class=valign-top><td class=align-left><td class=align-left>PFPN (kg kg<sup>−1</sup>)<td class=align-left>52.5<td class=align-left>51.5<td class=align-left>50.9<td class=align-left>1.9<td class=align-left>3.0<td class=align-left>9.1<td class=align-left>7.1<td class=align-left>0.40<td class=align-left>0.64<td class=align-left>0.89<td class=align-left>0.93<td class=align-left>0.28<td class=align-left>0.82<tr class=valign-top><td class=align-left><td class=align-left>REN (%)<td class=align-left>70.3<td class=align-left>63.4<td class=align-left>71.5<td class=align-left>9.7<td class=align-left>−1.8<td class=align-left>19.7<td class=align-left>15.5<td class=align-left>0.53<td class=align-left>0.71<td class=align-left>0.88<td class=align-left>0.91<td class=align-left>0.01<td class=align-left>0.62<tr class=valign-top><td class=align-left colspan=15><br><tr class=valign-top><td class=align-left>FP-N<td class=align-left>AEN (kg kg<sup>−1</sup>)<td class=align-left>28.9<td class=align-left>28.5<td class=align-left>25.1<td class=align-left>1.4<td class=align-left>16.6<td class=align-left>17.4<td class=align-left>23.1<td class=align-left>0.75<td class=align-left>0.55<td class=align-left>0.92<td class=align-left>0.86<td class=align-left>0.69<td class=align-left>0.05<tr class=valign-top><td class=align-left><td class=align-left>PFPN (kg kg<sup>−1</sup>)<td class=align-left>36.0<td class=align-left>37.5<td class=align-left>35.0<td class=align-left>−4.0<td class=align-left>2.8<td class=align-left>9.1<td class=align-left>6.8<td class=align-left>0.60<td class=align-left>0.78<td class=align-left>0.92<td class=align-left>0.94<td class=align-left>0.08<td class=align-left>0.47<tr class=valign-top><td class=align-left><td class=align-left>REN (%)<td class=align-left>52.1<td class=align-left>49.4<td class=align-left>55.5<td class=align-left>5.1<td class=align-left>−6.7<td class=align-left>16.7<td class=align-left>18.5<td class=align-left>0.70<td class=align-left>0.64<td class=align-left>0.93<td class=align-left>0.90<td class=align-left>0.10<td class=align-left>0.05</table></div><dl class=footnotes><dt id=tblfn5>a<dd><p id=np025>PBIAS: percent bias; nRMSE: normalized root mean square error; NSE: Nash-Sutcliffe efficiency; d: index of agreement.</p><dt id=tblfn6>b<dd><p id=np030>EI-N: ecological intensification with nitrogen fertilizer; EI-N0: EI without nitrogen fertilizer; FP-N: farmers’ practice with nitrogen fertilizer; FP-N0: FP without nitrogen fertilizer.</p></dl></div></div><p id=p0110>The DNDC and DSSAT model produced “fair” to “good” agreement between the simulated and measured maize yields, above-ground biomass and plant N uptake under the EI-N0 and FP-N0 treatments with −20.4% ≤ PBIAS ≤ 22.6%, 0.02 ≤ NSE ≤ 0.70 and 0.70 ≤ d ≤ 0.90; however, nRMSE > 30% in all cases except for maize yields in the DSSAT model. Nevertheless, the DSSAT model showed better performance than the DNDC model in simulating maize yields (<a class="anchor u-display-inline anchor-paragraph" href=#f0005 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0005><span class=anchor-text>Fig. 1</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#f0010 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0010><span class=anchor-text>Fig. 2</span></a> and <a class="anchor u-display-inline anchor-paragraph" href=#s0120 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=s0120><span class=anchor-text>A. 2, 3</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#t0015 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0015><span class=anchor-text>Table 3</span></a>). The DNDC model did not capture the changes in inter-annual yields under N-unfertilized treatments partially due to the underestimated N mineralization rate and N availability from the dissolved inorganic N pools under N stress conditions, which resulted in low yield and N uptake, particularly under continuous N deficiencies (<a class="anchor u-display-inline anchor-paragraph" href=#b0295 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0295><span class=anchor-text>Sansoulet et al., 2014</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0405 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0405><span class=anchor-text>Zhang et al., 2017b</span></a>). Another possible factor was the incomplete consideration of the soil organic carbon decomposition and mineralization when sudden cessation of fertilization occurred in the model simulation (<a class="anchor u-display-inline anchor-paragraph" href=#b0405 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0405><span class=anchor-text>Zhang et al., 2017b</span></a><span>). Long-term field experiment with high quality datasets should be conducted in the model development to understand the impact of <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/nutrient-deficiencies title="Learn more about nutrient deficiency from ScienceDirect's AI-generated Topic Pages" class=topic-link>nutrient deficiency</a><span> on crop growth and <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/soil-nutrient-dynamics title="Learn more about soil nutrient dynamic from ScienceDirect's AI-generated Topic Pages" class=topic-link>soil nutrient dynamic</a> cycling.</span></span><p id=p0115>In addition, the two models significantly underestimated the maize yield in 2009 (a dry year) compared to the measured data (<a class="anchor u-display-inline anchor-paragraph" href=#f0005 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0005><span class=anchor-text>Fig. 1</span></a><span>). This might be due to the overestimation of the actual <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/evapotranspiration title="Learn more about evapotranspiration from ScienceDirect's AI-generated Topic Pages" class=topic-link>evapotranspiration</a> of the crop in the DSSAT model (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0060 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0060><span class=anchor-text>Eitzinger et al., 2004</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0290 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0290><span class=anchor-text>Sau et al., 2004</span></a><span>) which resulted in water stress later in the growing season. The increased gap between the potential and actual evapotranspiration rate resulted in an increased water stress levels which limited <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/crop-biomass title="Learn more about crop biomass from ScienceDirect's AI-generated Topic Pages" class=topic-link>crop biomass</a> accumulation (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0060 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0060><span class=anchor-text>Eitzinger et al., 2004</span></a>). <a class="anchor u-display-inline anchor-paragraph" href=#b0190 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0190><span class=anchor-text>López-Cedrón et al. (2008)</span></a><span> indicated that the DSSAT model underestimated the maize yield partially because that the simulated water extraction was earlier than field experiment under water deficit condition. In the DNDC model, the inaccurate simulation of root distribution and <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/soil-water-content title="Learn more about soil water content from ScienceDirect's AI-generated Topic Pages" class=topic-link>soil water content</a> under dry conditions could affect the crop growth mainly due to its inability to simulate a heterogeneous soil profile and no water table (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0320 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0320><span class=anchor-text>Uzoma et al., 2015</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0305 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0305><span class=anchor-text>Smith et al., 2019</span></a>).</p></section><section id=s0070><h4 id=st085 class="u-margin-m-top u-margin-xs-bottom">3.1.2. Soil organic carbon</h4><div><p id=p0120>The SOC contents at soil depths of 0–0.20 m from 2009 to 2015 were simulated using the DNDC and DSSAT models (<a class="anchor u-display-inline anchor-paragraph" href=#f0015 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0015><span class=anchor-text>Fig. 3</span></a>). The statistical values were PBIAS ≤ 1.2%, nRMSE < 10% and NSE > 0, although d < 0.7 for N-unfertilized treatments, overall provided “fair” agreement between the simulated and measured data in the DNDC model for all treatments. The results were consistent with other studies which reported that the DNDC model simulated well the SOC contents (<a class="anchor u-display-inline anchor-paragraph" href=#b0380 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0380><span class=anchor-text>Zhang et al., 2006</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0040 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0040><span class=anchor-text>Chen et al., 2015</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0405 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0405><span class=anchor-text>Zhang et al., 2017b</span></a><span>). The model slightly overestimated the SOC content under N-fertilized treatment, which was probably due to the stimulation in C mineralization associated with <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/microbial-activity title="Learn more about microbial activity from ScienceDirect's AI-generated Topic Pages" class=topic-link>microbial activity</a> under N sufficient conditions. The DSSAT model provided a “fair” simulation for SOC under the EI-N and FP-N treatments, with the PBIAS ≤ 2.1%, nRMSE ≤ 7.6%, NSE > 0 and d > 0.7. However, the model underestimated (</span><em>p</em> > 0.05) the SOC content for the EI-N0 and FP-N0 treatments, with the PBIAS ≤ 2.8%, nRMSE < 10%, NSE < 0 and d < 0.7. The underestimation of SOC might be associated with the flows of carbon from one pool to another accompanied by the amount of N which was proportional to the C:N ratio of the decomposing material. Additional N needed for adding C to the recipient pool was obtained by immobilizing some inorganic N from the surrounding soil under N deficiency condition (<a class="anchor u-display-inline anchor-paragraph" href=#b0260 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0260><span class=anchor-text>Porter et al., 2010</span></a>). Similar studies indicated that the DSSAT model underestimated the soil organic carbon content under N deficiency conditions (<a class="anchor u-display-inline anchor-paragraph" href=#b0050 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0050><span class=anchor-text>De Sanctis et al., 2012</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0180 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0180><span class=anchor-text>Liu et al., 2017</span></a>). <a class="anchor u-display-inline anchor-paragraph" href=#b0150 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0150><span class=anchor-text>Li et al., 2015</span></a><span> demonstrated that the low initial <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/topsoil title="Learn more about topsoil from ScienceDirect's AI-generated Topic Pages" class=topic-link>topsoil</a> C/N ratio contributed to rapidly increase SOC using the DSSAT model.</span><figure class="figure text-xs" id=f0015><span><img 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" height=499 alt aria-describedby=cn0015><ol class=u-margin-s-bottom><li><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0168169919306532-gr3_lrg.jpg target=_blank download title="Download high-res image (448KB)"><span class=anchor-text>Download : <span class=download-link-title>Download high-res image (448KB)</span></span></a><li><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0168169919306532-gr3.jpg target=_blank download title="Download full-size image"><span class=anchor-text>Download : <span class=download-link-title>Download full-size image</span></span></a></ol></span><span class="captions text-s"><span id=cn0015><p id=sp0025><span class=label>Fig. 3</span>. Measured and simulated <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/soil-organic-carbon title="Learn more about soil organic carbon from ScienceDirect's AI-generated Topic Pages" class=topic-link>soil organic carbon</a> content (0–0.20 m) from 2009 to 2015 for (a) ecological intensification (EI-N), (b) EI without nitrogen fertilizer with nitrogen fertilizer (EI-N0), (c) farmers’ practice with nitrogen fertilizer (FP-N) and (d) FP without nitrogen fertilizer (FP-N0). Bars are standard deviations (n = 4).</p></span></span></figure></div></section><section id=s0075><h4 id=st090 class="u-margin-m-top u-margin-xs-bottom">3.1.3. Soil mineral nitrogen</h4><div><p id=p0125>The soil mineral nitrogen contents at soil depths of 0–0.30 m from 2009 to 2015 were simulated in the DNDC and DSSAT models (<a class="anchor u-display-inline anchor-paragraph" href=#f0020 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0020><span class=anchor-text>Fig. 4</span></a>). For the EI-N0 and FP-N0 treatments, the results from both models showed “poor” agreement between the simulated and measured soil mineral nitrogen contents based on statistical values PBIAS > 50%, nRMSE > 30%, NSE < 0 and d < 0.7. For the EI-N and FP-N treatment, the DNDC and DSSAT models showed “fair” performance in simulating soil mineral N content compared to the measured values, with 3.2% ≤ PBIAS ≤ 45.0%, 0.27 ≤ NSE ≤ 0.60 and 0.71 ≤ d ≤ 0.88, although nRMSE > 30%. Both models underestimated soil mineral nitrogen content for all treatments, but there was no significant difference between the simulated and measured data under the EI-N and FP-N treatments in the DNDC model. The changes in soil mineral nitrogen content were related to the actual N uptake by crop, which was affected by soil N supply and water availability during the growing season in the model simulation (<a class="anchor u-display-inline anchor-paragraph" href=#b0295 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0295><span class=anchor-text>Sansoulet et al., 2014</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0375 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0375><span class=anchor-text>Yakoub et al., 2017</span></a>). The DSSAT model underestimated the soil mineral nitrogen content for the FP-N treatment partially due to the overestimation of crop N demand under N-sufficient conditions. Moreover, both the models underestimated soil mineral N content for N-unfertilized treatments, which might be due to the underestimation of the amount of the mineralized nitrogen under N deficiency; similar results were reported by <a class="anchor u-display-inline anchor-paragraph" href=#b0160 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0160><span class=anchor-text>Liu et al., 2011</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0175 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0175><span class=anchor-text>Liu et al., 2014</span></a>. <a class="anchor u-display-inline anchor-paragraph" href=#b0175 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0175><span class=anchor-text>Liu et al. (2014)</span></a> showed that the DSSAT model underestimated the simulated soil inorganic nitrogen content, mainly because the model could not accurately account for the soil mineral N content below a depth of 0.30 m, whereas the model reasonably captured the soil mineral N trajectory during the whole growth period (<a class="anchor u-display-inline anchor-paragraph" href=#b0160 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0160><span class=anchor-text>Liu et al., 2011</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0355 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0355><span class=anchor-text>Yang et al., 2011a</span></a><span>). It was also found that the DNDC model poorly estimated the soil mineral nitrogen content (0–0.30 m) using field experiment data, which was associated with the poor structure in the <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/soil-water-balance title="Learn more about soil water balance from ScienceDirect's AI-generated Topic Pages" class=topic-link>soil water balance</a> module (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0100 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0100><span class=anchor-text>He et al., 2018a</span></a>). The complexity of soil N dynamics led to a large uncertainty in simulating the soil mineral N content. The imprecise simulations of soil mineral N fluctuations for different soil layers are mainly due to the flaws in the soil water balance module, particularly under extreme weather conditions or nutrient stress, which needs to be better characterized in the model. Additionally, the inaccurate estimation of the immobilization-mineralization processes also could result in the model error (<a class="anchor u-display-inline anchor-paragraph" href=#b0020 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0020><span class=anchor-text>Brilli et al., 2017</span></a>).<figure class="figure text-xs" id=f0020><span><img 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" height=520 alt aria-describedby=cn0020><ol class=u-margin-s-bottom><li><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0168169919306532-gr4_lrg.jpg target=_blank download title="Download high-res image (453KB)"><span class=anchor-text>Download : <span class=download-link-title>Download high-res image (453KB)</span></span></a><li><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0168169919306532-gr4.jpg target=_blank download title="Download full-size image"><span class=anchor-text>Download : <span class=download-link-title>Download full-size image</span></span></a></ol></span><span class="captions text-s"><span id=cn0020><p id=sp0030><span class=label>Fig. 4</span>. Measured and simulated <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/soil-minerals title="Learn more about soil mineral from ScienceDirect's AI-generated Topic Pages" class=topic-link>soil mineral</a> N content (0–0.30 m) from 2009 to 2015 for (a) ecological intensification with nitrogen fertilizer (EI-N), (b) EI without nitrogen fertilizer (EI-N0), (c) farmers’ practice with nitrogen fertilizer (FP-N) and (d) FP without nitrogen fertilizer (FP-N0). Bars are standard deviations (n = 4).</p></span></span></figure></div></section></section><section id=s0080><h3 id=st095 class="u-h4 u-margin-m-top u-margin-xs-bottom">3.2. Sensitivity analysis for maize yield and agronomic efficiency</h3><div><p id=p0130>Sensitivity analyses of the DNDC and DSSAT models were conducted to explore best management practices, including the planting date, planting density, tillage depth, and fertilizer N application rate and time (<a class="anchor u-display-inline anchor-paragraph" href=#t0010 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0010><span class=anchor-text>Table 2</span></a>). The simulation results were observed with the response curves of the maize yield to the values of different management practices (<a class="anchor u-display-inline anchor-paragraph" href=#f0025 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0025><span class=anchor-text>Fig. 5</span></a> and <a class="anchor u-display-inline anchor-paragraph" href=#s0120 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=s0120><span class=anchor-text>A. 5</span></a>). The simulated average maize yield was 9434 and 9252 kg ha<sup>−1</sup> for the DNDC and DSSAT models based on default value, respectively. Based on the sensitivity analysis, the planting date, planting density, fertilizer N application rate and fertilizer split had a greater influence on maize yield than did the tillage depth.<figure class="figure text-xs" id=f0025><span><img 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height=545 alt aria-describedby=cn0025><ol class=u-margin-s-bottom><li><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0168169919306532-gr5_lrg.jpg target=_blank download title="Download high-res image (313KB)"><span class=anchor-text>Download : <span class=download-link-title>Download high-res image (313KB)</span></span></a><li><a class="anchor download-link u-font-sans u-display-inline anchor-default" href=https://ars.els-cdn.com/content/image/1-s2.0-S0168169919306532-gr5.jpg target=_blank download title="Download full-size image"><span class=anchor-text>Download : <span class=download-link-title>Download full-size image</span></span></a></ol></span><span class="captions text-s"><span id=cn0025><p id=sp0035><span class=label>Fig. 5</span>. <span>Sensitivity of maize yields to (a) <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/fertilizer-application title="Learn more about fertilizer application from ScienceDirect's AI-generated Topic Pages" class=topic-link>fertilizer application</a> rate, (b) planting date, (c) planting density and (d) tillage depth in the DNDC and </span><a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/agricultural-engineering title="Learn more about DSSAT from ScienceDirect's AI-generated Topic Pages" class=topic-link>DSSAT</a> model at Liufangzi, Jilin, China.</p></span></span></figure></div><section id=s0085><h4 id=st100 class="u-margin-m-top u-margin-xs-bottom">3.2.1. Fertilizer N application rate and time</h4><p id=p0135>There was a curvilinear increase in the maize yield when the fertilizer N rates ranged from 0 to 300 kg N ha<sup>−1</sup>, and the average maximum yields (9434 and 9236 kg ha<sup>−1</sup>) were observed at 240 kg N ha<sup>−1</sup> as basal fertilizer in both the DNDC and DSSAT models (<a class="anchor u-display-inline anchor-paragraph" href=#f0025 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0025><span class=anchor-text>Fig. 5</span></a>a). Although the simulated maize yield showed a slight increase when the fertilizer N rate exceeded 240 kg N ha<sup>−1</sup> in the DSSAT model, there was no significant difference. The increased crop yield and N uptake values decreased when nitrogen fertilizer application exceeded crop N demand based on patterns of diminishing returns, leading to low nitrogen use efficiency (<a class="anchor u-display-inline anchor-paragraph" href=#b0035 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0035><span class=anchor-text>Chen et al., 2014</span></a>). The difference in simulated maize yield between the DNDC and DSSAT models was found at a low fertilizer N rate, which may be due to the different responses of yield to nitrogen supply in maize growth for the two models. The yields were more sensitive to N stress in the DNDC model compared to the DSSAT model, which was consistent with the response of simulated yields under N-unfertilized conditions.<div><p id=p0140>Previous studies showed that the temporal synchronicity between crop N demand and soil N availability could be improved via fertilizer N application times at the crop growth stages (<a class="anchor u-display-inline anchor-paragraph" href=#b0350 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0350><span class=anchor-text>Xu et al., 2016</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0325 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0325><span class=anchor-text>Wang et al., 2016</span></a>). The sensitivity of the DNDC and DSSAT model showed that the simulated maximum maize yield with higher N use efficiency was obtained at 210 kg N ha<sup>−1</sup> when the fertilizer N applied with two- or three-time splitting compared to the default fertilizer application as basal (251 kg N ha<sup>−1</sup>), except for three-time splitting at 240 kg N ha<sup>−1</sup> in the DSSAT model (<a class="anchor u-display-inline anchor-paragraph" href=#t0025 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0025><span class=anchor-text>Table 5</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#s0120 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=s0120><span class=anchor-text>Fig. A. 5</span></a>). For different splitting, the N split ratio of 1/3:2/3 and 1/4:2/4:1/4 for both models performed slightly better than other split ratio. In both models simulation, the high maize yield would achieve at 210 kg N ha<sup>−1</sup> (<a class="anchor u-display-inline anchor-paragraph" href=#s0120 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=s0120><span class=anchor-text>Fig. A. 5</span></a>), but there were no statistical differences at 180–210 kg N ha<sup>−1</sup> range with splitting especially in the DSSAT model. These results indicated that split application of fertilizer N with lower N application rates could obtain equal or higher maize yields than single fertilization. The excessive application of fertilizer N as basal fertilizer had a negative impact on nitrogen uptake and transport and posed an environmental risk (e.g., NO<sub>3</sub><sup>–</sup> leaching and greenhouse gas emissions) (<a class="anchor u-display-inline anchor-paragraph" href=#b0350 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0350><span class=anchor-text>Xu et al., 2016</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0095 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0095><span class=anchor-text>He et al., 2016</span></a>). The potential yields did not increase when fertilizer N application was delayed until the silking stage, even though it had a response between N application and crop N demand (<a class="anchor u-display-inline anchor-paragraph" href=#b0300 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0300><span class=anchor-text>Scharf et al., 2002</span></a>). Therefore, based on our model sensitivity analysis, the optimal fertilizer N application for maize was 180–210 kg N ha<sup>−1</sup> with two-time splitting (1/3 as basal fertilizer; 2/3 at jointing stage). Previous studies have also indicated that fertilizer N application with two or three-time splitting based on the nutrient demand of maize growth would be recommended versus a high fertilizer N application rate as basal fertilizer with low nitrogen use efficiency common in current farmers’ practice in northeast China (<a class="anchor u-display-inline anchor-paragraph" href=#b0355 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0355><span class=anchor-text>Yang et al., 2011a</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0230 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0230><span class=anchor-text>Niu et al., 2013</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0340 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0340><span class=anchor-text>Xu et al., 2014a</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0345 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0345><span class=anchor-text>Xu et al., 2014b</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0350 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0350><span class=anchor-text>Xu et al., 2016</span></a>). <a class="anchor u-display-inline anchor-paragraph" href=#b0230 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0230><span class=anchor-text>Niu et al. (2013)</span></a> reported that the nitrogen application was 225 kg N ha<sup>−1</sup> with a two timing splits for spring maize based on field experimental data at current cultivation levels or 300 kg N ha<sup>−1</sup> with three-time splitting at high-yielding cultivation levels in northeast China. <a class="anchor u-display-inline anchor-paragraph" href=#b0355 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0355><span class=anchor-text>Yang et al., 2011</span></a> reported that the fertilizer N application rate ranged from 200 to 240 kg N ha<sup>−1</sup><span> with two-time splitting in the DSSAT model in northeast China. These results indicate that reasonable nitrogen fertilizer management is conducive to reducing the amount of nitrogen fertilizer and increasing crop productivity and <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/nutrient-uptake title="Learn more about nutrient uptake from ScienceDirect's AI-generated Topic Pages" class=topic-link>nutrient uptake</a>.</span><div class="tables frame-topbot rowsep-0 colsep-0" id=t0025><span class="captions text-s"><span id=cn0050><p id=sp0060><span class=label>Table 5</span>. Recommendations of field management practices based on the sensitivity analyses of the DNDC and DSSAT models at Liufangzi, Jilin, China.</p></span></span><div class=groups><table><thead><tr class=valign-top><th scope=col class=align-left>Model<th scope=col class=align-left>Index<th scope=col class=align-left>Default<th scope=col class="align-left rowsep-1" colspan=3>Nitrogen fertilizer application and times (kg N ha<sup>−1</sup>)<th scope=col class=align-left>Planting date<th scope=col class=align-left>Planting density<th scope=col class=align-left>Combined<tr class=valign-top><td scope=col class=align-left><span class=screen-reader-only>Empty Cell</span><td scope=col class=align-left><span class=screen-reader-only>Empty Cell</span><th scope=col class=align-left>Values<th scope=col class=align-left>(Basal)<th scope=col class=align-left>(Basal: jointing)<th scope=col class=align-left>(Basal: jointing: tasselling)<th scope=col class=align-left>(day of year)<th scope=col class=align-left>(seed m<sup>−2</sup>)<th scope=col class=align-left>optimization<tr class="rowsep-1 valign-top"><td scope=col class=align-left><span class=screen-reader-only>Empty Cell</span><td scope=col class=align-left><span class=screen-reader-only>Empty Cell</span><td scope=col class=align-left><span class=screen-reader-only>Empty Cell</span><th scope=col class=align-left>240<th scope=col class=align-left>210 (1/3:2/3)<th scope=col class=align-left>210/240 (1/4:2/4:1/4)<th scope=col class=align-left>121<th scope=col class=align-left>7<td scope=col class=align-left><span class=screen-reader-only>Empty Cell</span><tbody><tr class=valign-top><td class=align-left>DNDC<td class=align-left>Grain yields (kg ha<sup>−1</sup>)<td class=align-left>9434<td class=align-left>9434<td class=align-left>9449<td class=align-left>9435<td class=align-left>9434<td class=align-left>–<td class=align-left>9449<tr class=valign-top><td class=align-left><td class=align-left>AEN (kg kg<sup>−1</sup>)<a class="anchor u-display-inline anchor-paragraph" href=#tblfn7 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=tblfn7><span class=anchor-text><sup>a</sup></span></a><td class=align-left>30.5<td class=align-left>31.9<td class=align-left>36.6<td class=align-left>36.5<td class=align-left>30.5<td class=align-left>–<td class=align-left>36.6<tr class=valign-top><td class=align-left><td class=align-left>PFPN (kg kg<sup>−1</sup>)<td class=align-left>37.6<td class=align-left>39.3<td class=align-left>45.0<td class=align-left>44.9<td class=align-left>37.6<td class=align-left>–<td class=align-left>45.0<tr class=valign-top><td class=align-left><td class=align-left>REN (%)<td class=align-left>52.1<td class=align-left>52.8<td class=align-left>56.6<td class=align-left>56.9<td class=align-left>52.1<td class=align-left>–<td class=align-left>56.6<tr class=valign-top><td class=align-left colspan=9><br><tr class=valign-top><td class=align-left>DSSAT<td class=align-left>Grain yields (kg ha<sup>−1</sup>)<td class=align-left>9252<td class=align-left>9236<td class=align-left>9243<td class=align-left>9217<td class=align-left>9252<td class=align-left>9420<td class=align-left>9466<tr class=valign-top><td class=align-left><td class=align-left>AEN (kg kg<sup>−1</sup>)<td class=align-left>28.0<td class=align-left>29.2<td class=align-left>33.4<td class=align-left>29.1<td class=align-left>28.0<td class=align-left>28.6<td class=align-left>34.4<tr class=valign-top><td class=align-left><td class=align-left>PFPN (kg kg<sup>−1</sup>)<td class=align-left>36.9<td class=align-left>38.5<td class=align-left>44.0<td class=align-left>38.4<td class=align-left>36.9<td class=align-left>37.5<td class=align-left>45.1<tr class=valign-top><td class=align-left><td class=align-left>REN (%)<td class=align-left>55.7<td class=align-left>56.0<td class=align-left>59.2<td class=align-left>56.7<td class=align-left>55.7<td class=align-left>57.4<td class=align-left>61.2</table></div><dl class=footnotes><dt id=tblfn7>a<dd><p id=np035>AEN: agronomic efficiency of N; PFPN: partial factor productivity of N; REN: recover efficiency of N.</p></dl></div></div></section><section id=s0090><h4 id=st105 class="u-margin-m-top u-margin-xs-bottom">3.2.2. Planting date</h4><p id=p0145>The planting date showed a non-linear effect on maize yield (<a class="anchor u-display-inline anchor-paragraph" href=#f0025 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0025><span class=anchor-text>Fig. 5</span></a><span>b). Both models indicated the maximum yield would obtain on day 121 (1 May), but there was no significant difference between day 114 and 121. If the planting date was advanced or delayed 14–28 days, the maize yield decreased by approximately 6.2–19.6% in the DNDC model and 7.3–25.3% in the DSSAT model. The different effects of planting dates on maize yield between the DNDC and DSSAT models were mainly due to the differences in mechanisms of simulating <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/phenology title="Learn more about crop phenology from ScienceDirect's AI-generated Topic Pages" class=topic-link>crop phenology</a>. In the DNDC model, crop growth was characterized by empirical growth curves specifying N requirements for C biomass accumulation and driven by the accumulation of thermal degree days (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0080 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0080><span class=anchor-text>Gilhespy et al., 2014</span></a><span>), whereas in the DSSAT model, the crop growth was controlled by phenologically defined growth stages based on <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/growing-degree-day title="Learn more about growing degree days from ScienceDirect's AI-generated Topic Pages" class=topic-link>growing degree days</a> and daily intercepted photosynthetically active radiation (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0120 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0120><span class=anchor-text>Jones et al., 2003</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0115 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0115><span class=anchor-text>Hoogenboom et al., 2012</span></a>). Meteorological factors (e.g., temperature, precipitation) could affect the maize yield (<a class="anchor u-display-inline anchor-paragraph" href=#b0410 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0410><span class=anchor-text>Zhao et al., 2015</span></a>). Early planting with low temperature most likely reduced the leaf number and internode length of crops, resulting in lower crop production (<a class="anchor u-display-inline anchor-paragraph" href=#b0310 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0310><span class=anchor-text>Tsimba et al., 2013</span></a><span>). In addition, low air temperature led to lower <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/soil-temperature title="Learn more about soil temperature from ScienceDirect's AI-generated Topic Pages" class=topic-link>soil temperature</a>, which could affect seed emergence (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0360 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0360><span class=anchor-text>Yang et al., 2011b</span></a>). In contrast, late planting under high temperatures reduced the photosynthetic activity of crops (<a class="anchor u-display-inline anchor-paragraph" href=#b0235 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0235><span class=anchor-text>Otegui et al., 1996</span></a>). <a class="anchor u-display-inline anchor-paragraph" href=#b0310 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0310><span class=anchor-text>Tsimba et al. (2013)</span></a> also demonstrated that delaying the planting date made crops susceptible to water stress more than advancing the date. In this study, the precipitation in September was 15–94% lower than that in August in the growing season. These results demonstrated that the recommended planting date ranged from late April to early May.</p></section><section id=s0095><h4 id=st110 class="u-margin-m-top u-margin-xs-bottom">3.2.3. Planting density</h4><p id=p0150>The sensitivity analysis of the DSSAT model showed that maize yield was sensitive to the planting density within a varying range (<a class="anchor u-display-inline anchor-paragraph" href=#f0025 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0025><span class=anchor-text>Fig. 5</span></a>c). The maize yield increased when the planting density changed from 3 to 7 seeds m<sup>−2</sup><span>, whereas it gradually decreased with the increase in the planting density due to plant lodging and the low <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/photosynthetic-efficiency title="Learn more about photosynthetic efficiency from ScienceDirect's AI-generated Topic Pages" class=topic-link>photosynthetic efficiency</a> (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0385 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0385><span class=anchor-text>Zhang et al., 2014</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0400 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0400><span class=anchor-text>Zhang et al., 2017a</span></a>). The maximum yield was observed at a planting density of 7 seeds m<sup>−2</sup> and increased by 2.3% along with a higher AEN, PFPN and REN in the DSSAT model compared to the default values (<a class="anchor u-display-inline anchor-paragraph" href=#t0025 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0025><span class=anchor-text>Table 5</span></a>). These results demonstrated that the current planting density should be improved to obtain a higher maize yield. In this study, the maize planting density was lower than other countries (e.g., 7–9 seeds m<sup>−2</sup><span> in the <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/maize title="Learn more about corn from ScienceDirect's AI-generated Topic Pages" class=topic-link>corn</a> belt of U.S.) (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0270 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0270><span class=anchor-text>Qi et al., 2011a</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0275 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0275><span class=anchor-text>Qi et al., 2011b</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0010 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0010><span class=anchor-text>Balboa et al., 2019</span></a>) depending on different cultivars, soil and climate. Previous studies indicated that the maize yield increased with the increase of planting density, but decreased when the planting density exceeded 7.5 seeds m<sup>−2</sup> in northeast China (<a class="anchor u-display-inline anchor-paragraph" href=#b0385 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0385><span class=anchor-text>Zhang et al., 2014</span></a>). This might be due to the kernel number and thousand kernel weight per maize ear decreased with increasing planting density (<a class="anchor u-display-inline anchor-paragraph" href=#b0385 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0385><span class=anchor-text>Zhang et al., 2014</span></a>) and the high logging percentage would increase with high density when excess 8.25 seeds m<sup>−2</sup> (<a class="anchor u-display-inline anchor-paragraph" href=#b0400 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0400><span class=anchor-text>Zhang et al., 2017</span></a>).</p></section><section id=s0100><h4 id=st115 class="u-margin-m-top u-margin-xs-bottom">3.2.4. Tillage</h4><p id=p0155>There was no significant linear relationship between the tillage depth and maize yield in the DNDC and DSSAT models based on the sensitivity analyses (<a class="anchor u-display-inline anchor-paragraph" href=#f0025 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=f0025><span class=anchor-text>Fig. 5</span></a><span>d). Tillage-involved factors influencing crop growth are complex and include soil moisture, temperature and nutrient availability. However, maize yield was little affected by tillage depth in our modelling study, partially due to that the models did not adjust some <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/soil-properties title="Learn more about soil properties from ScienceDirect's AI-generated Topic Pages" class=topic-link>soil properties</a> overtime (e.g., bulk density) (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0020 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0020><span class=anchor-text>Brilli et al., 2017</span></a>). Maize yields were not affected by tillage depth in our modelling study which was in agreement with <a class="anchor u-display-inline anchor-paragraph" href=#b0170 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0170><span class=anchor-text>Liu et al., 2013</span></a>, <a class="anchor u-display-inline anchor-paragraph" href=#b0100 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0100><span class=anchor-text>He et al., 2018a</span></a><span><span>, who reported little or no change in maize yields between <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/no-tillage title="Learn more about no tillage from ScienceDirect's AI-generated Topic Pages" class=topic-link>no tillage</a> and </span><a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/conventional-tillage title="Learn more about conventional tillage from ScienceDirect's AI-generated Topic Pages" class=topic-link>conventional tillage</a> in the DSSAT model in northeast China and the DNDC model in Canada. Thus, the effects of tillage depths on maize growth need to be better characterized in the DNDC and DSSAT models for future improvement. More factors should be considered when exploring the effect of tillage on crop yield, such as rooting depth effect and the change of soil physical and chemical properties under different tillage (</span><a class="anchor u-display-inline anchor-paragraph" href=#b0045 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0045><span class=anchor-text>Corbeels et al., 2016</span></a>).</p></section><section id=s0105><h4 id=st120 class="u-margin-m-top u-margin-xs-bottom">3.2.5. Combination of optimized management practices</h4><p id=p0160>Maize yield and nitrogen use efficiency further increased when all optimized management practices were combined (<a class="anchor u-display-inline anchor-paragraph" href=#t0025 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0025><span class=anchor-text>Table 5</span></a>). Compared to the default data, the maize yield, AEN, PFPN and REN increased by 2.3%, 6.5 and 6.0 kg kg<sup>−1</sup>, 8.2 and 7.4 kg kg<sup>−1</sup>, and 9.9 and 8.7% when optimal management practices were combined in the DSSAT and DNDC model, respectively (<a class="anchor u-display-inline anchor-paragraph" href=#t0025 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=t0025><span class=anchor-text>Table 5</span></a>). <a class="anchor u-display-inline anchor-paragraph" href=#b0230 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0230><span class=anchor-text>Niu et al. (2013)</span></a> reported that the combined improvement of maize genotype management and agronomic management would have a more positive impact on maize productivity in northeast China. In addition, <a class="anchor u-display-inline anchor-paragraph" href=#b0395 data-sd-ui-side-panel-opener=true data-xocs-content-type=reference data-xocs-content-id=b0395><span class=anchor-text>Zhang et al. (2015b)</span></a> reported that the DNDC model optimized critical agronomic and environmental N rates for maize growth in North China. These results indicated that integrated optimal management practices could have the potential to further improve crop yield and N agronomic efficiency.</p></section></section></section><section id=s0110><h2 id=st125 class="u-h4 u-margin-l-top u-margin-xs-bottom">4. Conclusions</h2><p id=p0165>The DNDC and DSSAT models were used to investigate the impacts of ecological intensification management and farmers practices on maize growth and soil C & N dynamics and to explore best management strategies to improve maize production with high NUE in northeast China. Both models overall indicated “good” performance in simulating maize yield, above-ground biomass and N uptake based on the statistical evaluations. The DSSAT model showed better simulations in maize yield under the N-unfertilized treatments compared to DNDC modelling, but the two models performed poorly in simulating maize growth and <a href=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/soil-nutrient-dynamics title="Learn more about soil nutrient dynamics from ScienceDirect's AI-generated Topic Pages" class=topic-link>soil nutrient dynamics</a> under dry conditions, mainly due to the flaws in the soil water balance module. In addition, the DNDC and DSSAT models showed “fair” performances in simulating soil organic carbon (0–0.20 m) and mineral nitrogen (0–0.30 m) for the N-fertilized treatments, but “poor” performances for N-unfertilized treatments which was partially attributed to underestimated mineralization rate under N stress conditions. The sensitivity analyses indicated that the maize yield and nitrogen use efficiency could be further improved by adjusting the planting date, planting density, fertilizer application rates and times. This study suggests that optimizing management practices based on modelling approach can be useful for policy decision in planning best management practices in an effort to improve nutrient use efficiency and maintain a crop sustainable production footprint.</p></section></div><section id=ak005><h2 id=st130 class="u-h4 u-margin-l-top u-margin-xs-bottom">Acknowledgements</h2><p id=p0170>This research was supported by the <span id=gp005>National Key Research & Development Program of China</span> (No. <a class="anchor u-display-inline anchor-paragraph" href=#gp005><span class=anchor-text>2016YFD0200101</span></a>) and the Fundamental Research Funds for Central Non-profit Scientific Institution for the <span id=gp020>CAAS-IPNI Joint Lab for Plant Nutrition Innovation Research</span> (No. <a class="anchor u-display-inline anchor-paragraph" href=#gp020><span class=anchor-text>161032019047</span></a>).</p></section><div class=Appendices><section id=s0120><h2 id=st140 class="u-h4 u-margin-l-top u-margin-xs-bottom">Appendix A. Supplementary material</h2><p id=p0180>The following are the Supplementary data to this article:<span class=display><span class="e-component e-component-mmc1" id=m0005></span></span><div class="article-attachment u-margin-xs-bottom"><a class="anchor download-link u-font-sans u-display-inline anchor-default anchor-icon-left" href=https://ars.els-cdn.com/content/image/1-s2.0-S0168169919306532-mmc1.docx target=_blank download title="Download Word document (535KB)"><svg focusable=false viewBox="0 0 94 128" width=20 height=20 class="icon icon-text-document"><path d="m35.6 1e1c-5.38 0-10.62 1.92-14.76 5.4-9.1 7.68-18.84 20.14-18.84 32.1v70.5h9e1v-15.99-2.01-4e1 -17.64-32.36h-56.4zm0 1e1h46.4v22.36 17.64 4e1 2.01 5.99h-7e1v-49c0-6.08 4.92-11 11-11h17v-2e1h-6c-2.2 0-4 1.8-4 4v6h-7c-3.32 0-6.44 0.78-9.22 2.16 2.46-5.62 7.28-11.86 13.5-17.1 2.34-1.98 5.3-3.06 8.32-3.06zm-13.6 38v1e1h5e1v-1e1h-5e1zm0 2e1v1e1h5e1v-1e1h-5e1z"></path></svg><span class=anchor-text>Download : <span class=download-link-title>Download Word document (535KB)</span></span></a></div><span class="captions text-s"><span><p><span class=label>Supplementary data 1</span>. </p></span></span><p></p></section></div></div><div class="related-content-links u-hide-from-md sf-hidden"></div><div class="Tail text-s"></div><section class="bibliography u-font-serif text-s" id=bi005><h2 class="section-title u-h4 u-margin-l-top u-margin-xs-bottom">References</h2><section class=bibliography-sec id=bs005><ol class=references id=reference-links-bs005><li><span class="label u-font-sans"><a class="anchor anchor-default" href=#bb0005 id=ref-id-b0005 data-aa-button="sd:product:journal:article:location=references:type=anchor:name=citation-name"><span class=anchor-text>Abebe and Gebremariam, 2019</span></a></span><span class=reference id=h0005><div class=contribution><div class="authors u-font-sans">T. 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Zhang, C. Yu, K. Zhang, Y. Zhou, W. Tan, L. Zhang, Z. Li, L. Duan</div><div id=ref-id-h0400 class="title text-m">Plant growth regulator and its interactions with environment and genotype affect maize optimal plant density and yield</div></div><div class="host u-font-sans">Eur. J. 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Agron., 18 (2003), pp. 267-288</div><div class="ReferenceLinks u-font-sans"><a class="anchor pdf link anchor-default anchor-icon-left" href="https://www.sciencedirect.com/science/article/pii/S1161030102001089/pdfft?md5=007ab3728cc02ee1328d96f20f0693d7&pid=1-s2.0-S1161030102001089-main.pdf" target=_blank rel=nofollow aria-describedby=ref-id-h0125><svg focusable=false viewBox="0 0 32 32" width=16 height=24 class="icon icon-pdf-multicolor icon-pdf-multicolor-adjusted"><path d="M7 .362h17.875l6.763 6.1V31.64H6.948V16z" stroke=#000 stroke-width=.703 fill=#fff></path><path d="M.167 2.592H22.39V9.72H.166z" fill=#da0000></path><path fill=#fff9f9 d="M5.97 3.638h1.62c1.053 0 1.483.677 1.488 1.564.008.96-.6 1.564-1.492 1.564h-.644v1.66h-.977V3.64m.977.897v1.34h.542c.27 0 .596-.068.596-.673-.002-.6-.32-.667-.596-.667h-.542m3.8.036v2.92h.35c.933 0 1.223-.448 1.228-1.462.008-1.06-.316-1.45-1.23-1.45h-.347m-.977-.94h1.03c1.68 0 2.523.586 2.534 2.39.01 1.688-.607 2.4-2.534 2.4h-1.03V3.64m4.305 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