Artículos

Augmentation du rendement en coton fibre (+28 %) et du résultat net (+157 %) grâce à Speedfol® Kali SP en application foliaire au Mexique

Pour étudier la réponse du coton à des applications foliaires de Speedfol® Kali SP (12,2 % N-NO3, 42,5 % K2O, 0,9 % B), un essai en plein champ a été réalisé, visant à évaluer l’effet de trois doses de Speedfol™ Kali SP sur le rendement en coton. L’essai a été réalisé dans le bloc 1401 de la localité de Valle del Yaqui, État de Sonora, Mexique. Le coton à l’essai était du cultivar Stonville, semé le 15/03/2011.

L’irrigation, les moments d’application des engrais et les quantités appliquées étaient identiques pour tous les traitements (Tableau 1). Pour évaluer les caractéristiques du sol, une analyse de fertilité du sol a été réalisée avant les semis (Tableau 2). Les traitements effectifs et les dates d’application sont indiqués au Tableau 3.

Tableau 1. Irrigation et engrais appliqués sur la culture de coton.

  SHAPE  * MERGEFORMAT <v:rect id="Rechthoek_x0020_6" o:spid="_x0000_s1028" style='width:11.25pt;height:11.25pt; visibility:visible;mso-wrap-style:square;mso-left-percent:-10001; mso-top-percent:-10001;mso-position-horizontal:absolute; mso-position-horizontal-relative:char;mso-position-vertical:absolute; mso-position-vertical-relative:line;mso-left-percent:-10001;mso-top-percent:-10001; v-text-anchor:top' o:gfxdata="UEsDBBQABgAIAAAAIQC75UiUBQEAAB4CAAATAAAAW0NvbnRlbnRfVHlwZXNdLnhtbKSRvU7DMBSF dyTewfKKEqcMCKEmHfgZgaE8wMW+SSwc27JvS/v23KTJgkoXFsu+P+c7Ol5vDoMTe0zZBl/LVVlJ gV4HY31Xy4/tS3EvRSbwBlzwWMsjZrlprq/W22PELHjb51r2RPFBqax7HCCXIaLnThvSAMTP1KkI +gs6VLdVdad08ISeCho1ZLN+whZ2jsTzgcsnJwldluLxNDiyagkxOquB2Knae/OLUsyEkjenmdzb mG/YhlRnCWPnb8C898bRJGtQvEOiVxjYhtLOxs8AySiT4JuDystlVV4WPeM6tK3VaILeDZxIOSsu ti/jidNGNZ3/J08yC1dNv9v8AAAA//8DAFBLAwQUAAYACAAAACEArTA/8cEAAAAyAQAACwAAAF9y ZWxzLy5yZWxzhI/NCsIwEITvgu8Q9m7TehCRpr2I4FX0AdZk2wbbJGTj39ubi6AgeJtl2G9m6vYx jeJGka13CqqiBEFOe2Ndr+B03C3WIDihMzh6RwqexNA281l9oBFTfuLBBhaZ4ljBkFLYSMl6oAm5 8IFcdjofJ0z5jL0MqC/Yk1yW5UrGTwY0X0yxNwri3lQgjs+Qk/+zfddZTVuvrxO59CNCmoj3vCwj MfaUFOjRhrPHaN4Wv0VV5OYgm1p+LW1eAAAA//8DAFBLAwQUAAYACAAAACEA3x3rb+8CAACgBgAA HwAAAGNsaXBib2FyZC9kcmF3aW5ncy9kcmF3aW5nMS54bWykVdtymzAQfe9M/0GjdwK4+AITkkls k+lM2mTi5gNkIRtNhEQl+ZJ2+u9dCRwTp9OHhAeQ9nK0e3ZXnF/ua4G2TBuuZI7jswgjJqkquVzn +PFHEUwwMpbIkgglWY6fmcGXF58/nZNsrUlTcYoAQZqM5LiytsnC0NCK1cScqYZJ0K2UromFrV6H pSY7QK5FOIiiUVgTLvHFEWpGLEEbzd8BJRR9YuWUyC0xAClo1pd0MQr6cWSSye2NbhbNvXaR0+/b e414mWNgTpIaKMJhp+jMYBueeK2PAPuVrp29Wq3Q3qM8u7fHYHuLKAjjZDAZDzGioOrW7RnV3T+8 aDX/rx8E0x4Ki14gpnFhyO3bzEaHzB4YrWyl2BMavSR5MDfNLZTAIKmmFZFrdmUaRi3EC84HkdZq VzFSGiduaQH+WgRP0REMSF3uvqkS+CQbq3yXvJ+ql5RJ1mhjb5iqkVvkWEOQHpxsb41tYzqYeD5U wYXwbAv5SgCYrQSqBK5O5+rl2/d3GqXzyXySBMlgNA+SaDYLroppEoyKeDycfZlNp7P4jzs3TrKK lyWT7pjDKMXJmz6tOdXKqJU9o6oOoVk4ZYdxgmGKo+MwGSV46eBcSEavl1Oh0ZaIHBf+6ZjvmYWv w/D9CrmcpBQPkuh6kAbFaDIOkiIZBuk4mgRRnF6noyhJk1nxOqVbLtnHU0K7HKfDwdBXqRf0SW6R f97mRrKaW6aR4HWOJy9GJHONOJelL60lXLTrHhUu/CMVUO5DoWFpuvG3+4UfG7u/VuWzI2wJX2he raC54EqAq9XewWslFORBBW8wqpT+dSpzdlB00GC0g4s1x+bnhmiGkfgqYV7SOEkAzvpNMhwPYKP7 mmVfQyQFqBxbjNrl1MIOXDaN5usKToo9nVJdwXCteNf4bewuC2Hswj4L5tnxGTJZ3hNNHiA3AfOd YyaDx0XHN1gAKUcSNoYtGrgtOtyWJU8bGJ7czd61+5e4H0B/f/EXAAD//wMAUEsDBBQABgAIAAAA IQCSfYfgHQcAAEkgAAAaAAAAY2xpcGJvYXJkL3RoZW1lL3RoZW1lMS54bWzsWUtvGzcQvhfof1js vbFkvWIjcmDJctzEL0RKihwpidplzF0uSMqObkVy6qVAgbTooQF666EoGqABGvTSH2PAQZv+iA65 L1Ki4gdcIChsAcbu7DfD4czszOzwzt1nEfWOMReExW2/eqviezgesTGJg7b/aLD92W3fExLFY0RZ jNv+DAv/7sann9xB6yNKkiFDfDwIcYQ9EBSLddT2QymT9ZUVMQIyErdYgmN4NmE8QhJuebAy5ugE Fojoymql0lyJEIn9DZAolaAehX+xFIoworyvxGAvRhGsfjCZkBHW2PFRVSHETHQp944Rbfsgc8xO BviZ9D2KhIQHbb+i//yVjTsraD1jonIJr8G3rf8yvoxhfLSq1+TBsFi0Xm/Um5uFfA2gchHXa/Wa vWYhTwPQaAQ7TXWxZbZWu/UMa4DSS4fsrdZWrWrhDfm1BZ03G+pn4TUolV9fwG9vd8GKFl6DUnxj Ad/orHW2bPkalOKbC/hWZXOr3rLka1BISXy0gK40mrVuvtsCMmF0xwlfa9S3W6uZ8BIF0VBEl1pi wmK5LNYi9JTxbQAoIEWSxJ6cJXiCRhCTXUTJkBNvlwQhBF6CYiaAXFmtbFdq8F/96vpKexStY2Rw K71AE7FAUvp4YsRJItv+fZDqG5Czt29Pn785ff776YsXp89/zdbWoiy+HRQHJt/7n77559WX3t+/ /fj+5bfp0vN4YeLf/fLVuz/+/JB42HFpirPvXr978/rs+6//+vmlQ/omR0MTPiARFt4+PvEesgg2 6NAfD/nlOAYhIibHZhwIFCO1ikN+T4YWen+GKHLgOti242MOqcYFvDd9aincD/lUEofEB2FkAfcY ox3GnVZ4oNYyzDyYxoF7cT41cQ8ROnat3UWx5eXeNIEcS1wiuyG21DykKJYowDGWnnrGjjB27O4J IZZd98iIM8Em0ntCvA4iTpMMyNCKppJph0Tgl5lLQfC3ZZu9x16HUdeut/CxjYR3A1GH8gNMLTPe Q1OJIpfIAYqoafBdJEOXkv0ZH5m4npDg6QBT5vXGWAgXzwGH/RpOfwBpxu32PTqLbCSX5Mglcxcx ZiK32FE3RFHiwvZJHJrYz8URhCjyDpl0wfeY/Yaoe/ADipe6+zHBlrvPzwaPIMOaKpUBop5MucOX 9zCz4rc/oxOEXalmk0dWit3kxBkdnWlghfYuxhSdoDHG3qPPHRp0WGLZvFT6fghZZQe7Aus+smNV 3cdYYE83N4t5cpcIK2T7OGBL9NmbzSWeGYojxJdJ3gevmzbvQamLXAFwQEdHJnCfQL8H8eI0yoEA GUZwL5V6GCKrgKl74Y7XGbf8d5F3DN7Lp5YaF3gvgQdfmgcSu8nzQdsMELUWKANmgKDLcKVbYLHc X7Ko4qrZpk6+if3Slm6A7shqeiISn9sBzfU+jf+u94EO4+yHV46X7Xr6HbdgK1ldstNZlkx25vqb Zbj5rqbL+Jh8/E3NFprGhxjqyGLGuulpbnoa/3/f0yx7n286mWX9xk0n40OHcdPJZMOV6+lkyuYF +ho18EgHPXrsEy2d+kwIpX05o3hX6MGPgO+Z8TYQFZ+ebuJiCpiEcKnKHCxg4QKONI/HmfyCyLAf ogSmQ1VfCQlEJjoQXsIEDI002Slb4ek02mPjdNhZrarBZlpZBZIlvdIo6DCokim62SoHeIV4rW2g B625Aor3MkoYi9lK1BxKtHKiMpIe64LRHEronV2LFmsOLW4r8bmrFrQA1QqvwAe3B5/pbb9RBxZg gnkcNOdj5afU1bl3tTOv09PLjGlFADTYeQSUnl5Tui7dntpdGmoX8LSlhBFuthLaMrrBEyF8BmfR qagXUeOyvl4rXWqpp0yh14PQKtVo3f6QFlf1NfDN5wYam5mCxt5J22/WGhAyI5S0/QkMjeEySiB2 hPrmQjSA45aR5OkLf5XMknAht5AIU4PrpJNmg4hIzD1Koravtl+4gcY6h2jdqquQED5a5dYgrXxs yoHTbSfjyQSPpOl2g6Isnd5Chk9zhfOpZr86WHGyKbi7H45PvCGd8ocIQqzRqioDjomAs4Nqas0x gcOwIpGV8TdXmLK0a55G6RhK6YgmIcoqipnMU7hO5YU6+q6wgXGX7RkMapgkK4TDQBVY06hWNS2q RqrD0qp7PpOynJE0y5ppZRVVNd1ZzFohLwNztrxakTe0yk0MOc2s8Gnqnk+5a3mum+sTiioBBi/s 56i6FygIhmrlYpZqSuPFNKxydka1a0e+wXNUu0iRMLJ+Mxc7Z7eiRjiXA+KVKj/wzUctkCZ5X6kt 7TrY3kOJNwyqbR8Ol2E4+Ayu4HjaB9qqoq0qGlzBmTOUi/SguO1nFzkFnqeUAlPLKbUcU88p9ZzS yCmNnNLMKU3f0yeqcIqvDlN9Lz8whRqWHbBmvYV9+r/xLwAAAP//AwBQSwMEFAAGAAgAAAAhAJxm RkG7AAAAJAEAACoAAABjbGlwYm9hcmQvZHJhd2luZ3MvX3JlbHMvZHJhd2luZzEueG1sLnJlbHOE j80KwjAQhO+C7xD2btJ6EJEmvYjQq9QHCMk2LTY/JFHs2xvoRUHwsjCz7DezTfuyM3liTJN3HGpa AUGnvJ6c4XDrL7sjkJSl03L2DjksmKAV201zxVnmcpTGKSRSKC5xGHMOJ8aSGtHKRH1AVzaDj1bm IqNhQaq7NMj2VXVg8ZMB4otJOs0hdroG0i+hJP9n+2GYFJ69elh0+UcEy6UXFqCMBjMHSldnnTUt XYGJhn39Jt4AAAD//wMAUEsBAi0AFAAGAAgAAAAhALvlSJQFAQAAHgIAABMAAAAAAAAAAAAAAAAA AAAAAFtDb250ZW50X1R5cGVzXS54bWxQSwECLQAUAAYACAAAACEArTA/8cEAAAAyAQAACwAAAAAA AAAAAAAAAAA2AQAAX3JlbHMvLnJlbHNQSwECLQAUAAYACAAAACEA3x3rb+8CAACgBgAAHwAAAAAA AAAAAAAAAAAgAgAAY2xpcGJvYXJkL2RyYXdpbmdzL2RyYXdpbmcxLnhtbFBLAQItABQABgAIAAAA IQCSfYfgHQcAAEkgAAAaAAAAAAAAAAAAAAAAAEwFAABjbGlwYm9hcmQvdGhlbWUvdGhlbWUxLnht bFBLAQItABQABgAIAAAAIQCcZkZBuwAAACQBAAAqAAAAAAAAAAAAAAAAAKEMAABjbGlwYm9hcmQv ZHJhd2luZ3MvX3JlbHMvZHJhd2luZzEueG1sLnJlbHNQSwUGAAAAAAUABQBnAQAApA0AAAAA " filled="f" stroked="f"> ​​

Tableau 2. Analyse de fertilité du sol (0-30 cm).

  SHAPE  * MERGEFORMAT <v:rect id="Rechthoek_x0020_4" o:spid="_x0000_s1027" style='width:11.25pt;height:11.25pt; visibility:visible;mso-wrap-style:square;mso-left-percent:-10001; mso-top-percent:-10001;mso-position-horizontal:absolute; mso-position-horizontal-relative:char;mso-position-vertical:absolute; mso-position-vertical-relative:line;mso-left-percent:-10001;mso-top-percent:-10001; v-text-anchor:top' o:gfxdata="UEsDBBQABgAIAAAAIQC75UiUBQEAAB4CAAATAAAAW0NvbnRlbnRfVHlwZXNdLnhtbKSRvU7DMBSF dyTewfKKEqcMCKEmHfgZgaE8wMW+SSwc27JvS/v23KTJgkoXFsu+P+c7Ol5vDoMTe0zZBl/LVVlJ gV4HY31Xy4/tS3EvRSbwBlzwWMsjZrlprq/W22PELHjb51r2RPFBqax7HCCXIaLnThvSAMTP1KkI +gs6VLdVdad08ISeCho1ZLN+whZ2jsTzgcsnJwldluLxNDiyagkxOquB2Knae/OLUsyEkjenmdzb mG/YhlRnCWPnb8C898bRJGtQvEOiVxjYhtLOxs8AySiT4JuDystlVV4WPeM6tK3VaILeDZxIOSsu ti/jidNGNZ3/J08yC1dNv9v8AAAA//8DAFBLAwQUAAYACAAAACEArTA/8cEAAAAyAQAACwAAAF9y ZWxzLy5yZWxzhI/NCsIwEITvgu8Q9m7TehCRpr2I4FX0AdZk2wbbJGTj39ubi6AgeJtl2G9m6vYx jeJGka13CqqiBEFOe2Ndr+B03C3WIDihMzh6RwqexNA281l9oBFTfuLBBhaZ4ljBkFLYSMl6oAm5 8IFcdjofJ0z5jL0MqC/Yk1yW5UrGTwY0X0yxNwri3lQgjs+Qk/+zfddZTVuvrxO59CNCmoj3vCwj MfaUFOjRhrPHaN4Wv0VV5OYgm1p+LW1eAAAA//8DAFBLAwQUAAYACAAAACEAhRFy8O8CAACgBgAA HwAAAGNsaXBib2FyZC9kcmF3aW5ncy9kcmF3aW5nMS54bWykVclu2zAQvRfoPxC8K5JcepEQJUi8 BAXSJoibD6Ap2iJCkSpJL2nRf++QkmPHKXpIdJDIWR5n3sxQ55e7WqINN1ZoVeD0LMGIK6ZLoVYF fvwxi0YYWUdVSaVWvMDP3OLLi8+fzmm+MrSpBEOAoGxOC1w51+RxbFnFa2rPdMMV6Jba1NTB1qzi 0tAtINcy7iXJIK6pUPjiADWhjqK1Ee+Akpo98XJM1YZagJQsP5Z0MUr2cWSaq82NaebNvfGRs++b e4NEWWBgTtEaKMJxp+jMYBufeK0OALulqb29Xi7RLqA8+3fA4DuHGAhT0hsN+xgxUHXr9ozq7h9e rJr+1w+CaQ+FxVEgtvFhqM3bzMg+swfOKldp/oTIS5J7c9vcQgksUnpcUbXiV7bhzEG84LwXGaO3 Fael9eKWFuCvRQgUHcCA1MX2my6BT7p2OnTJ+6l6SZnmjbHuhusa+UWBDQQZwOnm1ro2pr1J4EPP hJSBbaleCQCzlUCVwNXrfL1C+/7Okmw6mo5IRHqDaUSSySS6mo1JNJilw/7ky2Q8nqR//LkpyStR llz5Y/ajlJI3fVoLZrTVS3fGdB1DswjG9+MEw5Qmh2GyWorSw/mQrFktxtKgDZUFnoWnY/7ILH4d RuhXyOUkpbRHkuteFs0Go2FEZqQfZcNkFCVpdp0NEpKRyex1SrdC8Y+nhLYFzvq9fqjSUdAnuSXh eZsbzWvhuEFS1AUevRjR3DfiVJWhtI4K2a6PqPDhH6iAcu8LDUvbjb/bzcPYuN21Lp89YQv4QvMa Dc0FVwJcre4OXkupIQ8mRYNRpc2vU5m3g6KDBqMtXKwFtj/X1HCM5FcF85KlhACcCxvSH/ZgY441 i2MNVQygCuwwapdjBztwWTdGrCo4KQ10Kn0Fw7UUXeO3sfsspHVz9yx5YCdkyFV5Tw19gNwkzHeB uYoe5x3fYAGkHEhYWz5v4LbocFuWAm1geHI3B9fuX+J/AMf7i78AAAD//wMAUEsDBBQABgAIAAAA IQCSfYfgHQcAAEkgAAAaAAAAY2xpcGJvYXJkL3RoZW1lL3RoZW1lMS54bWzsWUtvGzcQvhfof1js vbFkvWIjcmDJctzEL0RKihwpidplzF0uSMqObkVy6qVAgbTooQF666EoGqABGvTSH2PAQZv+iA65 L1Ki4gdcIChsAcbu7DfD4czszOzwzt1nEfWOMReExW2/eqviezgesTGJg7b/aLD92W3fExLFY0RZ jNv+DAv/7sann9xB6yNKkiFDfDwIcYQ9EBSLddT2QymT9ZUVMQIyErdYgmN4NmE8QhJuebAy5ugE Fojoymql0lyJEIn9DZAolaAehX+xFIoworyvxGAvRhGsfjCZkBHW2PFRVSHETHQp944Rbfsgc8xO BviZ9D2KhIQHbb+i//yVjTsraD1jonIJr8G3rf8yvoxhfLSq1+TBsFi0Xm/Um5uFfA2gchHXa/Wa vWYhTwPQaAQ7TXWxZbZWu/UMa4DSS4fsrdZWrWrhDfm1BZ03G+pn4TUolV9fwG9vd8GKFl6DUnxj Ad/orHW2bPkalOKbC/hWZXOr3rLka1BISXy0gK40mrVuvtsCMmF0xwlfa9S3W6uZ8BIF0VBEl1pi wmK5LNYi9JTxbQAoIEWSxJ6cJXiCRhCTXUTJkBNvlwQhBF6CYiaAXFmtbFdq8F/96vpKexStY2Rw K71AE7FAUvp4YsRJItv+fZDqG5Czt29Pn785ff776YsXp89/zdbWoiy+HRQHJt/7n77559WX3t+/ /fj+5bfp0vN4YeLf/fLVuz/+/JB42HFpirPvXr978/rs+6//+vmlQ/omR0MTPiARFt4+PvEesgg2 6NAfD/nlOAYhIibHZhwIFCO1ikN+T4YWen+GKHLgOti242MOqcYFvDd9aincD/lUEofEB2FkAfcY ox3GnVZ4oNYyzDyYxoF7cT41cQ8ROnat3UWx5eXeNIEcS1wiuyG21DykKJYowDGWnnrGjjB27O4J IZZd98iIM8Em0ntCvA4iTpMMyNCKppJph0Tgl5lLQfC3ZZu9x16HUdeut/CxjYR3A1GH8gNMLTPe Q1OJIpfIAYqoafBdJEOXkv0ZH5m4npDg6QBT5vXGWAgXzwGH/RpOfwBpxu32PTqLbCSX5Mglcxcx ZiK32FE3RFHiwvZJHJrYz8URhCjyDpl0wfeY/Yaoe/ADipe6+zHBlrvPzwaPIMOaKpUBop5MucOX 9zCz4rc/oxOEXalmk0dWit3kxBkdnWlghfYuxhSdoDHG3qPPHRp0WGLZvFT6fghZZQe7Aus+smNV 3cdYYE83N4t5cpcIK2T7OGBL9NmbzSWeGYojxJdJ3gevmzbvQamLXAFwQEdHJnCfQL8H8eI0yoEA GUZwL5V6GCKrgKl74Y7XGbf8d5F3DN7Lp5YaF3gvgQdfmgcSu8nzQdsMELUWKANmgKDLcKVbYLHc X7Ko4qrZpk6+if3Slm6A7shqeiISn9sBzfU+jf+u94EO4+yHV46X7Xr6HbdgK1ldstNZlkx25vqb Zbj5rqbL+Jh8/E3NFprGhxjqyGLGuulpbnoa/3/f0yx7n286mWX9xk0n40OHcdPJZMOV6+lkyuYF +ho18EgHPXrsEy2d+kwIpX05o3hX6MGPgO+Z8TYQFZ+ebuJiCpiEcKnKHCxg4QKONI/HmfyCyLAf ogSmQ1VfCQlEJjoQXsIEDI002Slb4ek02mPjdNhZrarBZlpZBZIlvdIo6DCokim62SoHeIV4rW2g B625Aor3MkoYi9lK1BxKtHKiMpIe64LRHEronV2LFmsOLW4r8bmrFrQA1QqvwAe3B5/pbb9RBxZg gnkcNOdj5afU1bl3tTOv09PLjGlFADTYeQSUnl5Tui7dntpdGmoX8LSlhBFuthLaMrrBEyF8BmfR qagXUeOyvl4rXWqpp0yh14PQKtVo3f6QFlf1NfDN5wYam5mCxt5J22/WGhAyI5S0/QkMjeEySiB2 hPrmQjSA45aR5OkLf5XMknAht5AIU4PrpJNmg4hIzD1Koravtl+4gcY6h2jdqquQED5a5dYgrXxs yoHTbSfjyQSPpOl2g6Isnd5Chk9zhfOpZr86WHGyKbi7H45PvCGd8ocIQqzRqioDjomAs4Nqas0x gcOwIpGV8TdXmLK0a55G6RhK6YgmIcoqipnMU7hO5YU6+q6wgXGX7RkMapgkK4TDQBVY06hWNS2q RqrD0qp7PpOynJE0y5ppZRVVNd1ZzFohLwNztrxakTe0yk0MOc2s8Gnqnk+5a3mum+sTiioBBi/s 56i6FygIhmrlYpZqSuPFNKxydka1a0e+wXNUu0iRMLJ+Mxc7Z7eiRjiXA+KVKj/wzUctkCZ5X6kt 7TrY3kOJNwyqbR8Ol2E4+Ayu4HjaB9qqoq0qGlzBmTOUi/SguO1nFzkFnqeUAlPLKbUcU88p9ZzS yCmNnNLMKU3f0yeqcIqvDlN9Lz8whRqWHbBmvYV9+r/xLwAAAP//AwBQSwMEFAAGAAgAAAAhAJxm RkG7AAAAJAEAACoAAABjbGlwYm9hcmQvZHJhd2luZ3MvX3JlbHMvZHJhd2luZzEueG1sLnJlbHOE j80KwjAQhO+C7xD2btJ6EJEmvYjQq9QHCMk2LTY/JFHs2xvoRUHwsjCz7DezTfuyM3liTJN3HGpa AUGnvJ6c4XDrL7sjkJSl03L2DjksmKAV201zxVnmcpTGKSRSKC5xGHMOJ8aSGtHKRH1AVzaDj1bm IqNhQaq7NMj2VXVg8ZMB4otJOs0hdroG0i+hJP9n+2GYFJ69elh0+UcEy6UXFqCMBjMHSldnnTUt XYGJhn39Jt4AAAD//wMAUEsBAi0AFAAGAAgAAAAhALvlSJQFAQAAHgIAABMAAAAAAAAAAAAAAAAA AAAAAFtDb250ZW50X1R5cGVzXS54bWxQSwECLQAUAAYACAAAACEArTA/8cEAAAAyAQAACwAAAAAA AAAAAAAAAAA2AQAAX3JlbHMvLnJlbHNQSwECLQAUAAYACAAAACEAhRFy8O8CAACgBgAAHwAAAAAA AAAAAAAAAAAgAgAAY2xpcGJvYXJkL2RyYXdpbmdzL2RyYXdpbmcxLnhtbFBLAQItABQABgAIAAAA IQCSfYfgHQcAAEkgAAAaAAAAAAAAAAAAAAAAAEwFAABjbGlwYm9hcmQvdGhlbWUvdGhlbWUxLnht bFBLAQItABQABgAIAAAAIQCcZkZBuwAAACQBAAAqAAAAAAAAAAAAAAAAAKEMAABjbGlwYm9hcmQv ZHJhd2luZ3MvX3JlbHMvZHJhd2luZzEueG1sLnJlbHNQSwUGAAAAAAUABQBnAQAApA0AAAAA " filled="f" stroked="f"> ​​

Tableau 3. Traitement et dates d’application.

  SHAPE  * MERGEFORMAT ​​

Les variantes de fertilisation à l’étude ont consisté en 4 traitements organisés en blocs aléatoires complets avec 5 répétitions. Les parcelles mesuraient 5 mètres de long par 0,9 mètre de large. Les traitements ont été appliqués manuellement au pulvérisateur à dos. L’application des traitements a démarré à la première floraison puis a été répétée à environ 7 jours d’intervalle, et s’est terminée à la formation des premières capsules. La récolte manuelle du champ de l’essai s’est déroulée le 07/09/2011, après application d’un produit dessiccant sur les cultures.

Résultats de l’étude agronomique et économique :
Une ANOVA a révélé une augmentation statistiquement significative du rendement en coton fibre (p = 0,01) sous l’effet de traitements foliaires avec Speedfol™ Kali SP par rapport au traitement témoin.

La formule de régression de la Figure 1 indique clairement que le rendement maximal en coton fibre de 1 357 kg/ha a été obtenu en appliquant 15 kg de Speedfol™ Kali SP par hectare. En moyenne, les parcelles non traitées ont produit 1 062 kg/ha ; la différence entre le rendement maximal et celui de la parcelle non traitée est de 295 kg/ha (28 % de coton fibre en plus).