330 lines
109 KiB
HTML
Executable File
330 lines
109 KiB
HTML
Executable File
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Page saved with SingleFile
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url: https://tspace.library.utoronto.ca/handle/1807/36687
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saved date: Tue Jan 19 2021 17:47:51 GMT-0500
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--><meta charset=utf-8>
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<title>Normalized Difference Vegetation Index (NDVI) and phenological data
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integration to estimate county yield of corn in Córdoba, Argentina | TSpace Repository</title>
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) 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Dy8YajaX3+RPabSPvmJM/gZ4V9sDYik1pQex/Qlg/kTtftaF/Yu8/8X8qxxBxrXuHtxQsmkMi6S7/pzeNBxP1NboNJU9dFsiaVl/YoMC1AZ20MFVucRyRvcR+/Kwh4CZX8+5IbQF8GqglGSG4FZs7P2Pvzj65qf9oNYExlyO07mcwivXhWZlyJjh6qd67kWZvl7/K06OQznxVrvIaFKVkKK+bWLnsZX9GlLn8DiabulJ/yQmZLJWFiSqOoRn+CYvq2vRu5IXb7h9qtD7ZXPpY/845qMgv4/M3JEJT2pS9MATu9nCUTIrZagPYqUvE++q1M7Yms8l24ELUbrNofu26btUGHD5RboQC6WvANcrceOITRVoXMcv2lUDQ08ghkdkoE/7KW1+NiwlOiQjMmttZM60k6CTkwfaqE8q01L1evNPcdkzvEnuiTbcoFXt/hfOzlMZoGxayJlRTJZqO2+MDSISbvRwMOIUYmQeMzsmafQ+bo1rELMM3OZtlMF1+0ADN3BZ17Esb2qOtnRdn0RsjAOOiMpQMmRlisVf0rQF2UDq5yhkHsMi8vuxZKBrweUJBj6kHmcnBNt7yi86qHTps1IjLta0B02qynVGOZEKNMIuuLcloXEUHK4y3CAKFOk7KOgIoY/iLzyMK8vxaoyAIhAz8mjcUupHnxkflJW0XbD/207wvVrTafZ/5huCqEUZjguusHFc1DbnssFFCzOECUofbn5RXfgrU/kETuWEelNhVWWNMRmmmDPpMExPbtXXdOrJcejhTZc1x4imqTsN5tcpUMxg1pEElAbtd9E0xWjWImZmclb6mGJv0h/HbsmbydQ5Q+6c9xxZRbR25WQst9FPMdMZw9ydSBilwqBWYKAOetXXKrEjGO3hkaZOvSMtD+Oz9cRGq2QEngjxDj0B4ey06sC0uji3YqzaWB0vat5UFNAOHj+veFlWevVAKkixy6rdxcWB3tPl0OriYtW63Pn89mlFNQePtKL6d4+d09pDzPL1t7WF5ItyC8tkN/cr1sGpGLtQfzfPaD9+RtDH+lpGW7Q51kM9xJRMy0H9pOQtDoa4+BoFKOD0q+babjSP0isjshl13lVZKWZ0scB2c+mccjX5lyP9ZNxnbBV7f8eJX6sl4qTFT1sr7Wkuni5Z+3JUzsqbaCzrqF04K6jFR1sobWvMGy9fPvry/71SPN1c1lu7flZVTEQtrzBacihZ5rtFl88NXAPuhs9Hi8syZ4eyotXytFR2xOvy6jrzO2l5tO58FZ+DV5JBASA4PkXqEgIqhD2FuVPzvuZUNc7cCKlvgECam5pa6MPMLkqcG2LKizLpq/+bLWy2/TDpkPxt/2sqIfUA/+XwWzr0Y7RJpXwrvULJ0LACQm+tBKusNJ+m8BGRxjtd7niKWgFEcab4CuVGf0y1bjGGXJiTim02DLJ3j2W1Ha6/3R7Iv3pib8NED3SkfvGFb0Fdf7E06IZox2DZltwTQdK0vJd6ve/521r+SaoCzSXFZV2LVVkRZQmp/h2wSAtMjCfqnSNl7/2sOarJ8a5jg+NiqXrQwz723cuvivkHQZp+9Uu5RvI7Ka9BLxafgzn+fU+3b7TQ2NAO6Zu9fk073q/+rKuOThe71grCJIgZkGJv2Hei8YjTxbYSQe+/QwT1haK6DheeHay++MG9+5zf5d1iUd1AR2np9VKvSiuPdNQlh/6yS0hpxZfS0rVrVMUQqCyEurL0drQ1ktpfhu53qRxLYv698/Dd79WJ39D8XLt4RmzDuwk+ktdlOrW68np+YsDVqhlUhQmVJtxyKaUwPtibnFFvWmQYl+sdUhJnZ4ZBedWQzIWLHSRDj76oCGgL6TgPuRFmn7/efPGPb7PyUU085L1m8dXh1pLi1nJsjZV7Ggrn1F8WYiq+LePZcUnZX9aNPixymB2fUM3AyELerOST0Sl6oDc+rtR6SmwntCgf0Su3OddArva4ndqlHK2PKCjkypuQ4m5PFwWX6ZAtqwIw2QQU0TmUEp3kUAnx1zLImghcn9oZ77JSirYJTIkKdkXg/eT6VXTDvS79CMrdIvf3DelhbuRLAo4gZbeKgNCq9tW+1ff/ppFdEkhRiT2xASoWf7t7qPzEybL+x1YjPW5PoaaWmttPXalTmC8RJUWh3HuV+Y+AT+0ZTFtEk/e/uY1Tese4LYVdKWWDFv5Dm9aDm90QxYliha8TNFzHPNhVPlnUT4XQNa8vVMm8psDVM0Epac+TELDVU5wH3T/8J9CClGBci81KuhE5/StGAdo/kFOWw0wbt4INwT6KIcRlhfR+1s7pVFbrPTb3zcY9a8dcHL7zqgtjWXrdTsmV8jY8JxVfCfAwp10TEgY9ZGTkznwv2dCCqqn80IzZrWY1le1b5StFNSB2fv88iftW+2rDZ/99XCRCPijW2+Lzgl8w3dwZr9+xXE0rFzDDgurGUipgqERNifyspYHTEdDZx9mXsYHOaM7oTpjgFV+vUpSwXVBoPD6C8S7bJ6c2pd8uiczXZBvDzNmr2mXZJKqxbrmcpdJkHWV5ToKO1e42XwPXBc5+YYKk4cx/jfU2H7sJkrU4ro05AmahX1ZAJ9gME4hVnobu3gJJHYPIWDacxz576BdYamTumxcW4peDNAwoRm/5q/7R/vj5O1RN+ZvWp8+7hgoqU+jR+UOpbpPq3ZIgX17UPmOVNpXeLGRNwjP/sks+Ja6ZYnHfz1oFoMjACX7+L1+rpceAtQ15xZCCElaAn1bpW0ZbUXFpZ9rNh2iEHHVGnH3EnW/W6qm4ox7W2XWFaGvkX+RUCltB9y42dfxd3LrHdICXM2y6squJl2x1/TMdc7ReOnWPw/OLPaLJblSjqzcgTpXMgV/npEEgsOyf3s6x4cW52dHF+ZERRcQCcxj8ldkOXmAyRLkG9JvlahWa9BulvB2B9tPEHREEbIFLNEOz7tTztXYJVybjODFb86ZorRQT3nsyKIFxCpauSQPPni6ErxwPojIgVTM5lGIK4+Xv+dGJcLMos6H5QQWje9R7IzIZabrdmjLE7DGwyQFVK7PmqHbq5LFaRj3anAWMX6xCOYDnsRxY8DpT/oIppngS2kNhqfDWvQmwltIZBVqQ9A6thvjG7o2EfpNhfUb9KSjkbjQC1j+VKroii8QzGMLO4Ksgp9BmdGLLSNtV29MnRnmQ6dGJibndGO1UR/VpaMD8RM/0/kfS1b6O2qDrvAu8vZFEr0gfS7UXXncOlY4fP1K7HdFnb+gkJF8QD/CK0BPT+IJBxJaVvM7K2deva2911lY+2rpduYUMET7khggJg9S/OlyTu6VdEJJ+Vtu+c7mZ3ZhbbFAepaek31jOT6C/ZB8qnjjxQf3247imY7z2XLaT9CL/R8gOiOusGVlXeEu71VlGe7axXbtnlir8WUKZj59b98H6h+OPcJ+5p0cW2/u7rSrcFevA/HMvM+u6jlUbX8unG6o+XxRfpC/O28wZGlcEuJPk1TL9ZJC+nsl5luL65iJo3LL4gHqsZdP9qWloG07X9ZyVmtfswGRlMVNhUoL9pqEsOK2UKFmm53DByMQTqeoiH94WyWDP3nJDCst8BTzBFmwtJUd4RiFrFvEpETwtRsR0Munz/vdv5JSU6PqFrIQMSk/9+G1jKv496WzXeAEte4YXibU0ubkCc6Oj6YMq+iQ1lUo9vSoVtTgFtfd+7dlIYXN7tIv7H/oRUtDSZ+VJA6PV7R4YELcwvn2PxKLwJEfQWlfjW/V1+Qg6bsgXEwt0BFgZMZS4q+X9o8H0z484vq5kbM+Ejs6WqgSdd+j0DOewHwVpuY4GxY+NTG6voDAc6W4YvZ1Cr+047IG/tRj/SYSi5BWFjmkclj/PTeLoERJUO3x1HAqH8my33W9pab+3s9V+53rL9T2VHmEVSAv6uc3h4ALPFqsEmXh2OETf21c9JiPFx58/b68c5VhWlIiGihpfLq813jOxi6NX93NvK766yuoQqaeNmjvJebsHqoLHPpy7/fRyB794ZQaaH3j6XrCAxTECHamG9La6KlZrfhxOnWA32dLRuaeXQUbhXUQ1Xah67fPr6/cSkDR7ZcY5F0q+sysi2qvRuEbvisvFCJusu7TJ/nZLg6xW86y4eCtTjbUg3UL4LCqnWOcUScNJKC6tO+yk2kWHgmuOdUiLt/7/LqZfpAb54OAi8+hFxpxSP76HOCVXvIy/GYYraenWza01eKMUN3rgfa7D82PmoFaS7YrmQOizKqG5RFGDbYKUe6V9169Mn2age3F10Tf4rEHtWgyRJRJjjqTbJUwCweQrJ+93n/6C1hjWSha7CW6RbRlf3w0lWhPa4xfGJ+QBCHrgQ6z93mlf0Kzzt5dJA9KkJxYjY7QcW6uT1AhkF3rK6Cok6gxeMcgNmv46HfIc5aN+TzCUhI3lKLjJsdFB0wxL8bhcmM5BxPprX2V3MfNf8swosx8pCbUzW0PmlUghAOIuFOO/+L0p5ZlnPPkj2z212gFkr3SOwsQUjzDN2tXOmxwF0XnXPFSVEBUfK6zhtHQh1cy1OiiyBBl7xkPR9EyAlKZOmolHVUhUEToFbClrLespDrnXNfr3yeWyE4wMUf9Z8zVjqQ+zMFE36jsKu1DGs0RdX5adPvnrT75xd3arNWchmFcrP1M7qrtp++5u/XJbYaHBRbXrrMkOjFFmrW1xUnJSgTI9jf4wGJH9I/FXO42ncPX8l8e30m4ZDTxeRQ9vJ1qFZ+X7YLMyL6EpaSHonExfLDXXKjYuwyqUkoXF5mSGoNKygtBZmVhsZrZV+JWk2LoGEulaPZ7Q0EQg1bWALR8l3u6V4uLkTTPuNPYlhVqFjNKMOvMAyUHOATcefU+DCj8X8hdCQIOqbq45dhArZUy0a8J63oJv/Vl/JTglQClLypZENEAYJWJqdUbNK6sivJwKrFRqHMsK3tbs3ktDVNhBZjScswpsLM//jNAnqBXCqJ6m+NIAu4wwuhFnjkMMpixmRM/8tEuQubArHm6nqBQF1/ZXUkQkQ7/5FCnFbzwZ3V2FlKweWntqU4Zbskby5+AzqNmNoztbD75OXSEb6pMjtHVK0UuLPj6s5SWfRZbP8sKMp8/G5obP+qxn2EJPj4ZGd3evak+vSl9vj6pKb5/zn6woOXM5cQzWv8AvOjA0tDaslYfEpUytCL6kreaH16VScsIS5m+MB8eyGNAArhiuUD1dJyXXqjgk2AfsG4dyrcqTcq8iIWV8ZHxJKPcqVbNy8RO5VL28M9s/Ix39nRE2rgif8P5sR5dEW+mDvPb1gOzAiiQeOi3d7JQhnqvjgwgVEUoTOsX3pZzCGKJQhhgZLk5FpRgXCmOQQHEm4Z0cCbHn93JsrDOGgJcrsPIOe//Sw9XJH2yjg3RAmsADFJFQU3dKdE1mWXqso0dVWW5P64iCMvy1o1OoudiN881Xhovaa4i6SWXmSTvNpa/JEfGXAu2Rxg7m5mYxagSk537+Bd+cujh9iYpPf6NvyCKWRdURPTc/xwi7JvP+66qf2DUOi+VIlglE019WuWMF+DGNCDPoQL+A4uAmALx9ewdPRNiWy53ln7+57/59c065etvMGKlnmQfNvtrEB8EBNbgq2FL4oqGDo7KOuj088qR7f7QOYVAJIqrILI6kmmN5PF29s3zN/MDqL2JaO5lVZe2jTS1tjOqa1iGOpkSvhDAcNiExCUvGXUJa0YAiJJw5KBlfGR8S0l1UQuBzrcDPx2/XBUu0LvfHXMYZWUvY6gVnxCY6VioGaBpkzfvdmmSPdqLkw0w8PJx8g90RRB/ZQbXsBENjpBnCFKXbphVTkPehCYSdBA63OJ9t9qlfZ7KqkWzpKmkppPXM9uRQnH1S1GUk3DA1l1ZFqx5wSopjYBH6r7mTqpK+TQa6g1ZVBAzma/wC/QJ8/KIL8MX6BgdeJvkFBfsHStqXve/LP0UF8fYRtf0LUfahx606H/+seAdc+RRrMPxkVK0aF4Pg1GcrOhqXqvEG16ru3MCh28eMuPLZIMvyk8/PH8iO2J/f1J0AW1J0XStQrP0Zq6i18mbu8qqOacWn7CKCu4WumRy4A6Vnp53vG1IX0jjTOTgRjqS9unoezWbaOIdHWaQ6+SW6mCBxKLi1nonnBcdslzSRxwz6wMD9sUfrT+q4USjgc77p+8BfvmFr6huSmBhf0oyBpVqp3IIHUlrdVHzh7p1+0dOvBR5F3OTLM8DWyWOUKlwVK8AhcslvHFPncVktIx09VX7hHh6lbe20oGu8o3wtkYbb2gl51wWu8dWltQXQA4cvPkoeJsl97vsjMaFnr3h5HjwLukzU21udPE5DR7zJxBr7aCFJV9Eh1ge5NuHrcdVFrYVt4uWDHXVOYW29zOYbjUvWjP9rXirIm8lzEjfq1oHXG+jfgMHXv3zJMAugJi0GiSHh7XwoV6oBO8AjjWiDiSPZ+GemVsO1fnbBQzap1nCCLeBQtdOqHbRJQWnjRwiSDZnrKJrBMPXosA9nf1AXFDrSuP0ooVEIymdrskIHQAB9G87jaSnVXj3MsaT6mq/Zxy7fvChgG/3XFYuOBYCQp7elf054GJaKCPUUO15tx1auUpayM5y/9hPQUlYFtL/HuoHJbtIYT38RxeR3xsTNuNzHglogfh719xmrccZ4/Zipy6msA9Az3F2+HGvfRnk3pQp3pWpZu0/6O9X32lpqdrce0nav0yvYyNg7aSUtgtfA9Rktrifnj1KzQ9LC6J6Rmcnp4vHuOpmUbKozjCihvTcQmBgXGhJPCgy8TAgOSYqL8NEzNYLB/LnQ05vQhyOMZKDI++pOEHsNNEReW/1BoNBlQg72vCvETsMSAob+OggSuhybA3G6U+Ndc+o4QJ4G1n5xYmaGRD0DBHUvJ5n+hXw7LezKfHl3vl2ixVlZ9un9O8y81L2dwJckTTAAC/mrprwpqXsC5T1xLz9+3YQUySBTmJC3FLjlfUJsSiSOmJQUT7gSHoGnCJvxZ5M6K6gZCqRUvjL1yMziwxdMGDp0wcS+7F+6kATibJq8kT4FGVye76yrMa0qRXQdgKPn0J+rkdRAHd5Jb1XiVjj3z2r1R+Vdd8XL1Eclqr6LpCyXLpeFVJvlw1KOz73MY3113IMHjW1fjSahceYB9fgE5nASkEqOA4AF2AT8T6JsPTMALpA4geCz06T/H44JmPh+OJbY7DcpIf0NxPc9ttdRUdlUAtm42GzOhnM4GTV8mkL4LO6TcHQp587Ql85ApBKSs1lZpbh/OKhiN5Hw6fn7eJzTslYtf7kf+qxmEggnh0D4NEb4LNrxOHuybgE7inCyDYY9heNxamRDlR9Km2/jZfY28CWEfGnI9Gchsx+E4H7omPkzamFRZ6CilgVt70kI+bqQ6T87cDvRm308y+rkazh5gsOn17bjcetw1LEp316/0wKzj1wI+Tb7dDpk9o8O3Kl+9wVmJwghPxoyfbADd3YUi+0gBNdniteJxi7BAvyrR0LbkbJJ5tnLbvjtqQsXZl/kUdn0qFZ7pBcHI6mEbZOUfOxkNna1DYZ9HEZkU5kfMxK8pP77+dcPsxa/cYAfOgKW4m6aJF4YDu6La44NoG91pvOAfv++QAow5DqLvCz95EIkqBlUdOOVsorURl/BArQfmnngU3ToJoumlLnfJ5kHfOpYRWO05DzAtZVKEsr3ZWhrQ066+0N5h5lUxFWk5Zc3hlKnNaWloO/Me4AIknM6AKmtzEFoV3o/IkunD1DI0kgrB+ZY3/MpcKQVWr+0AwinhYQX7GDT2WT9EVEOSa4/k8HlpQF1dsgGhLrHTmCuvc8X8QBAFC1ErUaMtM9FCodwiFtOeg/8TF3+UCg4oQKY7qeZNaN1fPniwUZjOFGIjDnu5Je4jiOcToeU4ThMKbNjmueUzlkBVISkjeQKk5Pzv9/fAoCXn493vV211JjknMmhrsiW0nTrjSLZE4ORBiFkI+0OFz0Odjjb6f/eq/IFK+a3EFN8qo1t+lJhXxrMJzUs+oJWwDSf5nw8TtOHvzTPhwSEcD6umPRLjEEq98cDvvTd368BqzXfX6fLdU9023ixAxhFMuJuuvTSfw7Ki/q2fs/69rBqVp/T3zZyP050AqapzGQ+3rsL03xMZFYGmT01zZzxWkMUtrtZ7fd3N/ZFlPcz+u3B711evJQvhvFyVEXBaV5Ip0JRZgEJx7/P0ap0sJbnt4e7w/Pz+mpcd+aTT2f3hrmE6+f1CzHheIW3t2dTPjNvOSM4HWBApU39saZIYObC8n/QkAQXVPTfmxG2tWQFloDqUF07ZSdXk8kmvGVQB4t07eZFKIip31QP0YRIlnnunLeKpK4BTn93XyqqHmAP1gIiI4TzqLT6CgiaaZg+sI9XqdnfdTBWAEzEkWPoEgmCgAXnJR4kCeC8wS8xHdRQOlL2WAkkEHnGEp7r2xmcBDkOpn2HURjShyOHyg41aUA3wDSrHBmlmJFsgCYZmvIxBXIa9AamCpSa0WIZ7TuiYsN70vFFtfT16yUUpQC7vZc3z7DvVOcdfW8vLzfvuFQ2xWZxQJ0Kn0zZP840C8N5pRBQYGmYyOI9JqicUUkkzaR8sVllnLOAY6ObHmm1hGpyALJtyXzLWyndO2SlhgqXUy1StdsByIRXl66q158zneoGm/4dzYi6fZSrnOVH5Wf5uBgTAjgqjfw9KGHBlwDuMoc+XDU/w7UxKFMT5PRCpmdnmvGOZ88cuAVucSOc56mAYk7FY+coLJdpby0Akapd1xtX39Q1b5oBHKO4qsBr+hzy8fB6V3aU0gF0fV2+pI4SxUsHb+iq68x7YUxTqvglxtJpkagTCAQjikcBpkni6zqKE3AwmsRXEIPZGxhB5vsm4HrLMScIxiFrqEQl2TXe316VAquarm8ALuLF5ksuumjfEIkqi20T5fndJ/UvVLMEeNbUd+LhZvjsM/qVTtX+TRy293h10KMvlo/3s+HhwSch5XWlmQAEhaQohpq/oRJ1TQKBMpxm9TPQwOnX5bSF1jaMY21J1bTb6zCozltCqKEe31Jn6PO0Ako/LxhXrBtTYz9keHOWqZJVVyhdDUHUrckAQS7Cv0Ug1UGEWbfwZjJpLSPQjJyX5g1SMs7FGzgyXj6NLLeJn1coARhqhrbKZMs9oxn6sLJGj5zXS02rNSzfACCcS29wJPo8jaJUs/LAMAaaSIDwTuMbtBZWIhPMJjMwumz6QqXSug4GgZw+2BcrmHbRQZMRvLKoIhw3MmZ9cy0VAUgqBihukFnQsK+FkFVde+mbltmEslJYg1wjsNP+SJLmGLn7XUIABSlXqIJGpso1QxrNNPWGIurNfSpKaEmAKv+OoDSW3mKtVCq/RaFsCNuxKxRG0XYAhHbnhF758Q2eVBECUaDnH4Bp29b4JrgzSVXWKsOp640HD2/PvOeI5GURK07J/OQXb9yjTXEAQxF0eeU22HWy8OIdoalbBWgGRxOYYIjaKKRNjKRMMLOQ7GJEZhVTJSeN5AGHqGxmVC2Jkas0AiHmlufBDLa3q492uWjrWpghWWRGJBgIg2yRSVJUFqTU+OgmCG7/NhoJLhQaC4IpFCVwBNbxNEMFihjZSlC9uECmW5pR9ZaQjICBNoKjZNeVGOl4zj80NrSfqiKw91AbUNdImP63eBAAlRYciMM01Vz3nc6IXafmxFLt9XtVWfk1g2nx76vlNHtdbTbjmDVeXjFebV4xF6+gbcMjbRSLR0QHbZPMvy/0XNq+D8Gt0eHZzoNrc7JJgsxLyVdvHoGoAAvnwcbi24VDQGwNDr30OY6NkCt3JtBrsZY91HWS6WyX2wDAmENjSjnFRCwPIXass8JssH2FmCyuln31SN+DlE15N65NBosFLknjXOFcocC2s+ypaYEJJR4pKuATtlIM7RiW8Tmz+HAuD+Nnl277K/GysCL47X61vRztb+9W4WNVfUT/4hV2r2+Dru2621v+irf35pRTbmJ3p3ZLfIQ6vU+GaUr5eDy0xPpB7bQ20JmuH2qTM2FfOVoA08JpPmkAnvPI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<link rel=schema.DC href=http://purl.org/dc/elements/1.1/>
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<meta name=DC.creator content="de la Casa, Antonio" xml:lang=en_US>
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<meta name=DC.creator content="Ovando, Gustavo" xml:lang=en_US>
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<meta name=DCTERMS.dateAccepted content=2013-10-08T15:33:30Z scheme=DCTERMS.W3CDTF>
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<meta name=DCTERMS.available content=2007-01-22 xml:lang=en_US scheme=DCTERMS.W3CDTF>
|
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<meta name=DCTERMS.available content=2013-10-08T15:33:30Z scheme=DCTERMS.W3CDTF>
|
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<meta name=DCTERMS.issued content=2013-10-08 scheme=DCTERMS.W3CDTF>
|
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<meta name=DC.identifier content="http://www.bioline.org.br/abstract?id=at07044" xml:lang=en_US scheme=DCTERMS.URI>
|
|
<meta name=DC.identifier content="http://www.inia.cl/at/agritec.htm;
|
|
http://www.bioline.org.br/at" xml:lang=en_US scheme=DCTERMS.URI>
|
|
<meta name=DC.identifier content=http://hdl.handle.net/1807/36687 scheme=DCTERMS.URI>
|
|
<meta name=DC.description content="Integration of satellite data with agroclimatic information can result
|
|
in better procedures to evaluate the state and evolution of grain
|
|
crops. In this work, phenologic calendar of corn ( Zea mays L.) crop
|
|
based on growing-degree days procedures was integrated with Normalized
|
|
Difference Vegetation Index (NDVI) estimations from global coverage of
|
|
National Oceanic and Atmospheric Administration-Advanced Very High
|
|
Resolution Radiometer (NOAA-AVHRR) system. The main objectives were: i)
|
|
to evaluate the relationship between NDVI and corn yield in different
|
|
stages of crop cycle; ii) to analyze the influence of sowing date, and
|
|
iii) to develop a predictive model of county (departmental) corn yield
|
|
using satellite and ground data. The NDVI values accumulated in
|
|
different corn phenologic stages showed a positive association with
|
|
yield, and this relationship was modified in function of sowing date.
|
|
The NDVI value during the reproductive stage, for any sowing date,
|
|
always expressed a high association with corn yield, reaching
|
|
significant correlation values (P < 0.05) in all cases, and even
|
|
higher (P < 0.01) for some evaluated dates. The higher sensitivity
|
|
showed by the reproductive stage confirms that it is a critical period.
|
|
Starting from this information, a prediction model was obtained that
|
|
explains around 80% of corn yield variability of Marcos Juárez
|
|
Department in Córdoba Province, Argentina." xml:lang=en_US>
|
|
<meta name=DC.description content="La integración de datos radiométricos con información
|
|
agroclimática puede resultar en mejores procedimientos para
|
|
evaluar el estado y evolución de los cultivos. En este trabajo,
|
|
datos del calendario fenológico del cultivo de maíz ( Zea
|
|
mays L.), estimados a partir de procedimientos de sumas
|
|
térmicas, fueron integrados con registros del Indice de
|
|
Vegetación de la Diferencia Normalizada (NDVI) de cobertura global
|
|
del sistema Radiómetro Avanzado de Muy Alta Resolución de la
|
|
Administración Nacional del Océano y la Atmósfera de los
|
|
Estados Unidos (NOAA-AVHRR), con los siguientes objetivos: i) evaluar
|
|
la relación entre el NDVI y el rendimiento de maíz en
|
|
distintas etapas del ciclo del cultivo; ii) analizar la influencia de
|
|
la fecha de siembra, y iii) desarrollar un modelo de pronóstico
|
|
del rendimiento de maíz a escala departamental. Los valores
|
|
acumulados de NDVI presentaron una asociación positiva con el
|
|
rendimiento de maíz, variable de acuerdo a la etapa de desarrollo
|
|
y la fecha de siembra consideradas. Durante la etapa reproductiva, el
|
|
NDVI expresó una elevada asociación con el rendimiento en
|
|
cualquier fecha de siembra eventual, alcanzando valores de
|
|
correlación significativos (P < 0,05) en todos los casos, y aun
|
|
superiores (P < 0,01) en algunas de las fechas evaluadas. La mayor
|
|
sensibilidad que presenta la etapa reproductiva del cultivo de
|
|
maíz pone de manifiesto su condición de período
|
|
crítico. A partir de esta información, se obtuvo un modelo de
|
|
predicción que explica alrededor del 80% de la variabilidad del
|
|
rendimiento de maíz del Departamento Marcos Juárez en la
|
|
Provincia de Córdoba, Argentina." xml:lang=en_US>
|
|
<meta name=DC.language content=es xml:lang=en_US>
|
|
<meta name=DC.publisher content="Instituto de Investigaciones Agropecuarias, INIA" xml:lang=en_US>
|
|
<meta name=DC.subject content=Corn xml:lang=en_US>
|
|
<meta name=DC.subject content=Yield xml:lang=en_US>
|
|
<meta name=DC.subject content="Development stages" xml:lang=en_US>
|
|
<meta name=DC.subject content="Sowing date" xml:lang=en_US>
|
|
<meta name=DC.subject content="Zea mays L." xml:lang=en_US>
|
|
<meta name=DC.title content="Normalized Difference Vegetation Index (NDVI) and phenological data
|
|
integration to estimate county yield of corn in Córdoba, Argentina" xml:lang=en_US>
|
|
<meta name=DC.title content="Integración del Índice de Vegetación de la Diferencia
|
|
Normalizada (NDVI) y del ciclo fenológico de maíz para
|
|
estimar el rendimiento a escala departamental en Córdoba,
|
|
Argentina" xml:lang=en_US>
|
|
<meta name=DC.type content=Article xml:lang=en_US>
|
|
<meta name=DC.rights content="Copyright 2007 - Instituto de Investigaciones Agropecuarias, INIA
|
|
(Chile)." xml:lang=en_US>
|
|
<meta name=citation_keywords content="Corn; Yield; Development stages; Sowing date; Zea mays L.; Article">
|
|
<meta name=citation_title content="Normalized Difference Vegetation Index (NDVI) and phenological data
|
|
integration to estimate county yield of corn in Córdoba, Argentina">
|
|
<meta name=citation_publisher content="Instituto de Investigaciones Agropecuarias, INIA">
|
|
<meta name=citation_author content="de la Casa, Antonio">
|
|
<meta name=citation_author content="Ovando, Gustavo">
|
|
<meta name=citation_pdf_url content=https://tspace.library.utoronto.ca/bitstream/1807/36687/1/at07044.pdf>
|
|
<meta name=citation_date content=2013-10-08>
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|
<meta name=citation_abstract_html_url content=https://tspace.library.utoronto.ca/handle/1807/36687>
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src=data:image/png;base64,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 alt="University of Toronto" height=50px></a> <a class=navbar-brand href=https://tspace.library.utoronto.ca/ title=TSpace><h1>TSpace</h1></a>
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</div>
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<nav class="collapse navbar-collapse bs-navbar-collapse" role=navigation>
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<div class="nav navbar-nav navbar-right">
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<ul class="nav navbar-nav navbar-right" id=user_login_logout>
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<li class=dropdown>
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<a href=https://tspace.library.utoronto.ca/mydspace class=dropdown-toggle><span class="glyphicon glyphicon-user"></span> Login</a>
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</li>
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</ul>
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<form action=/simple-search class="navbar-form navbar-right hidden-xs hidden-sm" scope=search>
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<div class=form-group>
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<label for=search-terms class=hide-label>Search</label>
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<input class="form-control stretcher" placeholder="Search for faculty and student research" name=query id=search-terms size=35 aria-label="Search for faculty and student research" value type=text>
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</div>
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<button aria-label="search button" type=submit class="btn btn-primary" id=tequery-hidden name=tequery><span class="glyphicon glyphicon-search"></span></button>
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</form>
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<form action=/simple-search class="navbar-form navbar-right visible-xs sf-hidden" scope=search>
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</form>
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</div>
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<ul class="nav navbar-nav">
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<li class=dropdown>
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<a href=# class=dropdown-toggle data-toggle=dropdown>Browse <b class=caret></b></a>
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<ul class="dropdown-menu sf-hidden">
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</ul>
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</li>
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<li><a href=https://tspace.library.utoronto.ca/help>Help</a></li>
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<li class=dropdown>
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<a href=# class=dropdown-toggle data-toggle=dropdown>About <b class=caret></b></a>
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<ul class="dropdown-menu sf-hidden">
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</ul>
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</li>
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<li>
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<a href=https://tspace.library.utoronto.ca/help/firsttimeusers.jsp>Deposit your Research</a>
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<span class=item-field-label>Title:</span>
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<h2 class=item-title>
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Normalized Difference Vegetation Index (NDVI) and phenological data
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integration to estimate county yield of corn in Córdoba, Argentina<br>Integración del Índice de Vegetación de la Diferencia
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Normalizada (NDVI) y del ciclo fenológico de maíz para
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estimar el rendimiento a escala departamental en Córdoba,
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Argentina</h2>
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<div class="item-field item-author-field">
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<span class=item-field-label>Author:</span>
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<a class=author href="https://tspace.library.utoronto.ca/browse?type=author&value=de+la+Casa%2C+Antonio">de la Casa, Antonio</a>; <a class=author href="https://tspace.library.utoronto.ca/browse?type=author&value=Ovando%2C+Gustavo">Ovando, Gustavo</a></div>
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<div class=item-field>
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<span class=item-field-label>Issue Date:</span>
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8-Oct-2013</div>
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<div class=item-field>
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<span class=item-field-label>Publisher:</span>
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Instituto de Investigaciones Agropecuarias, INIA</div>
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<div class=item-field>
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<span class=item-field-label>Description:</span>
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Integration of satellite data with agroclimatic information can result
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in better procedures to evaluate the state and evolution of grain
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crops. In this work, phenologic calendar of corn ( Zea mays L.) crop
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based on growing-degree days procedures was integrated with Normalized
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Difference Vegetation Index (NDVI) estimations from global coverage of
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National Oceanic and Atmospheric Administration-Advanced Very High
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Resolution Radiometer (NOAA-AVHRR) system. The main objectives were: i)
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to evaluate the relationship between NDVI and corn yield in different
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stages of crop cycle; ii) to analyze the influence of sowing date, and
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iii) to develop a predictive model of county (departmental) corn yield
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using satellite and ground data. The NDVI values accumulated in
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different corn phenologic stages showed a positive association with
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yield, and this relationship was modified in function of sowing date.
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The NDVI value during the reproductive stage, for any sowing date,
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always expressed a high association with corn yield, reaching
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significant correlation values (P < 0.05) in all cases, and even
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higher (P < 0.01) for some evaluated dates. The higher sensitivity
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showed by the reproductive stage confirms that it is a critical period.
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Starting from this information, a prediction model was obtained that
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explains around 80% of corn yield variability of Marcos Juárez
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Department in Córdoba Province, Argentina.<br>La integración de datos radiométricos con información
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agroclimática puede resultar en mejores procedimientos para
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evaluar el estado y evolución de los cultivos. En este trabajo,
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datos del calendario fenológico del cultivo de maíz ( Zea
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mays L.), estimados a partir de procedimientos de sumas
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térmicas, fueron integrados con registros del Indice de
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Vegetación de la Diferencia Normalizada (NDVI) de cobertura global
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del sistema Radiómetro Avanzado de Muy Alta Resolución de la
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Administración Nacional del Océano y la Atmósfera de los
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Estados Unidos (NOAA-AVHRR), con los siguientes objetivos: i) evaluar
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la relación entre el NDVI y el rendimiento de maíz en
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distintas etapas del ciclo del cultivo; ii) analizar la influencia de
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la fecha de siembra, y iii) desarrollar un modelo de pronóstico
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del rendimiento de maíz a escala departamental. Los valores
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acumulados de NDVI presentaron una asociación positiva con el
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rendimiento de maíz, variable de acuerdo a la etapa de desarrollo
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y la fecha de siembra consideradas. Durante la etapa reproductiva, el
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NDVI expresó una elevada asociación con el rendimiento en
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cualquier fecha de siembra eventual, alcanzando valores de
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correlación significativos (P < 0,05) en todos los casos, y aun
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superiores (P < 0,01) en algunas de las fechas evaluadas. La mayor
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sensibilidad que presenta la etapa reproductiva del cultivo de
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maíz pone de manifiesto su condición de período
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|
crítico. A partir de esta información, se obtuvo un modelo de
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predicción que explica alrededor del 80% de la variabilidad del
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rendimiento de maíz del Departamento Marcos Juárez en la
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Provincia de Córdoba, Argentina.</div>
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<div class=item-field>
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<span class=item-field-label>URI:</span>
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|
<a href="http://www.bioline.org.br/abstract?id=at07044">http://www.bioline.org.br/abstract?id=at07044</a><br><a href=http://www.inia.cl/at/agritec.htm;http://www.bioline.org.br/at>http://www.inia.cl/at/agritec.htm;
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http://www.bioline.org.br/at</a><br><a href=http://hdl.handle.net/1807/36687>http://hdl.handle.net/1807/36687</a></div>
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<div class=item-field>
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<span class=item-field-label>Keywords:</span>
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<a href="https://tspace.library.utoronto.ca/browse?type=subject&value=Corn+">Corn</a>; <a href="https://tspace.library.utoronto.ca/browse?type=subject&value=Yield+">Yield</a>; <a href="https://tspace.library.utoronto.ca/browse?type=subject&value=Development+stages+">Development stages</a>; <a href="https://tspace.library.utoronto.ca/browse?type=subject&value=Sowing+date+">Sowing date</a>; <a href="https://tspace.library.utoronto.ca/browse?type=subject&value=Zea+mays+L.+">Zea mays L.</a></div>
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</div>
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<li><a target=_blank href=https://tspace.library.utoronto.ca/bitstream/1807/36687/1/at07044.pdf>at07044.pdf</a></ul>
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