The materials used were a cow and chicken manure, rice straw, soybean litter, rice husks, bran, procal, decomposer, molasses, clean water, Biofresh biological agents (
Bacillus subtilis ST21e, B. cereus ST21b and
Serratia sp. SS29a), inorganic fertilizers (Urea, SP36 and KCL), BISI-2 corn seed varieties. This research was conducted in the period of June to November 2019 at South Konawe Regency Southeast Sulawesi Indonesia and in the Integrated Laboratory of Plant Protection Department, Faculty of Agriculture, Kendari Halu Oleo University. The field research was arranged in a randomized block design consisting of 3 treatments including B
0 = Inorganic fertilizer (control); B
1 = organic fertilizer formula A + agens hayati Biofresh; B
2 = organic fertilizer formula B + agens hayati Biofresh. Each treatment was repeated 3 times, which in total there were 9 experimental units.
Making organic fertilizer formula A without considering the composition of the ingredients and the making of formula B organic fertilizer is carried out by taking into account the composition of the material which includes; C/N ratio of the total material, levels of N (Nitrogen), P (Phosphate) and K (Potassium), C-Organic and water content and pH. Therefore, the results fulfilled the quality standards of organic fertilizers in accordance with the Indonesian National Standard (INS 7763-2018) and the Ministry of Agriculture Regulation (MAR No. 70-2011). The total nutrient content of the standard organic fertilizer was calculated using equation (1) and the total ingredient ratio of C/N by using equation (2), which the authors formulated as follows:
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)
..........(1)
With; x = 1, 2, 3, ....; y = 1, 2, 3 ....
Where,
Kn= Total nutritional content of organic matter.
A= Weight of organic matter.
B= Dry weight of organic matter (%).
N= Organic nutrient content.
x= Type of organic material.
y= Organic nutrient type.
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)
..........(2)
With; x = 1, 2, 3, .........
Where,
Tb= Total weight of material.
A= Weight of organic matter.
B= Dry weight of organic matter (%).
C= Organic C/N ratio.
x= Type of organic material.
R= Optimal C/N ratio of total organic matter.
The process of making organic fertilizer was carried out using the following procedures: Large organic materials were chopped up to ± 2 cm in size, then all ingredients were mixed and watered evenly with a decomposer solution + molasses + water and stirred until a mixture was formed, with a moisture content of ± 30%. The dough formed was made into mounds as high as ± 20 cm and tightly closed with a tarpaulin for ± 14 days (the dough was maintained at ± 40
oC). The finished organic fertilizer (covered with white fungus, odourless with a normal dough temperature) was dried with the wind until it reached the moisture content of ± 17%. A sampling of organics was carried out at 10 randomly, then mixed evenly (compiled) then packaged and labeled. Organic fertilizer samples were sent to the bogor Agricultural Institute Soil Science and Land Resources Laboratory for analysis.
Soil processing was carried out 2 times, then 9 beds/treatment plots were made according to the study design, with a size of 20 m 50 m. Calcification using procal with a dose of 7 kg/beds (equivalent to 70 kg/ha). Corn planting is done 1 week after the application of organic fertilizer, employing planting holes as deep as ± 5 cm, as much as 1 seed/hole, with a spacing of 25 cm × 80 cm. The application of organic fertilizer (treatments B
1 and B
2) is carried out the day before planting by way of displacement on a row of plant lines at a dose of 500 kg/plots (equivalent to 5 tons/ha).
Application of inorganic fertilizer three times with a dose of 156 kg N/ha + 75 kg P
2 O
5/ha + 110 kg K
2O/ha
(Handrid et al., 2017). Time and dosage for each treatment (B
0 = 100%; B
1 and B
2 = 50%); Application I shortly after planting using urea fertilizer (46% N) = 11.1 kg/plots + SP36 (36% P
2O
5) = 20.8 kg/plots + KCL (60% K
2O) = 18.3 kg/plots. The second application at the age of 30 days after planting (DAP) uses urea fertilizer = 13.9 kg/plots by planting hole around the corn tree. The third application at the age of 50 DAP uses urea fertilizer = 8.9 kg/plots by planting hole around the corn tree.
The sheath blight (
Rhizoctonia solani) disease severity was determined using 10 plants samples. Each treatment was repeated at the age of 2, 4, 6, 8 and 10 weeks after planting (WAP) was calculated using equation (3). After determining the disease severity, the AUDPC (Area Under the Disease Progress Curve) was calculated to determine the overall disease development (
Paraschivu and Cotuna, 2013), by using equation (4). Furthermore, the Disease Pressure Index (DSI) was calculated to determine the effectiveness of a biocontrol agent against pathogens (
Nawangsih and Wardani, 2014), by using equation (5).
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAHYAAAApCAYAAADgf90zAAAgAElEQVR4Ad1cd5hV9Zk+U2AGG4kNBSSWVVk3ASQ66rq6EUFsIEJEjdFoiGvJigYbFiLYN2uIJIIUQaQmgmvcJK6aNYJYKEY0oIAyFBmGaXduPb28+7zf9zt3hrjZ/WPIPjx7n+dw557yK9/79e87WOjSJwYQIIGDUI4A4KmQRwLEESIANgBfTkeA6wGuA0QxEHpAwps9cwRAAshDfDAG/DCHMMkAKCFOHHmMj/I2P9T7vUjnKEQhELcCSQZe4MsyYj8AghKAIoA8EvmOUSqYeQIHSAqwA0fWSHLEUQDAlb3FZh1JyDFcmdfhZji/7yJJElkTf0dRhDiO5Z4gNGQo7123xXXLFe5b9k6C7fuP1bUhU2ADROBhKC7gRtypEiJMhEwhfyUxEIdA4AExgREuQJI4SGKeI0Mo0WPZswfXb0MCGwl8eB6ZgJ8YYRjCdyPhoUKs7BEF7QKiE7ogPCGBTQKEdjOSoB1B6KDg+EgSIHATICKwJYSIkQ8jYYaSQyYIEIZFIAjIQQIaQXFdH+SfMEqE/+Sc78laoigQYLm6IIzluu5OQSaPKLCk034PLJeugJJYuniCp8CCUqU8CldOUaoDhE7BbA7C9XHiIxEoBE2hAMfzAxsxAfUpMTpXEofwnbw8v/uLVowZcw227WlAMfHgUSZ9FyFCuARNJicjZYGggChRBsgUi6IBErsNiZtXQI2cFmIfhDgO8oCfAUIHtg/kPODJhyZj+k9/gmKYIBV6MpjSIFBpTyJwjfwQSNIkPRRYXuE+zV7lzn37Txcl1iyQqzXr5HZIVAGJEhn5chBfLwTckKqbdwWIPBuUSgKoHw6iI/B3lKhaSy9HQYgk8EUCKWUE9/zzR6Cl3YeXBPARiGYgnFTE1CBU2zF1dVTEwtnTMXzEtciKmuRcJZFWSjT1AIHKG6OQcEMEtrgTL897BsNH/0Cu+6U8Fs7+Be6eNBn5BMKsVMdKgBCx75Q1UWdweUcqsel+0l3/Nb67DqwBVIBNUlhiIavYqbgAhDaCYiAqjFYqFxYRp9JJ7VzeGYGkAaPcUTkmCCPaMsCxjZoPXMROOxC2Y9LECViy/DVkXKCUOHDjPOI4FDALIYGmKlaa+34Wc+Y9i2GjryGcKDk2kqSEJCnADYoCLK2qzB64gO8CVMVRA+Y9+xjOH3sjmg04iFxcMfYarPlgA8gzIX0J2mX6DKELxCnziTGQBXCP/5fgdg3YdKVkRUN3/kmZ80RefIDAOlRn6lhRmtqCQO6h80OJ4o7J9FRpoahPB4mAq3bKD8Vcw/d4I22Tg5YdGzF8yLlobKP65RBUrW3CImSEoq/rEI3IsaMW+CiI1GXolMl6Kde0xTQDgE/fjuvnD8cB/CzgbQWQQSOAPak0hw7WrHgXl198uTxH+GzHM04XAbbVYUu4shRcBTYlGb95/LU++wzYjoVSzmKBhe4UVSDCLJZMfwwHWBYsqwcqD+kLq7IGlbW1sCp4zkKFVYkKy0JlhYVuNRYqu1nmWg+cVjcMJYe0TpBQmhIHz/38XzD18UfheIDtB1gwdxK6V1pY+OJvcf0tj6Om+4mYOWM5/Tf4fgZrVj2P2loL54y6Do0JQAZzogAOIhTDHL5z6XmotSyc9M2hoCP2bzNm4CjLwhHVFqwqC3XfuRnbjKqmw9W+dSu+PXQ4WtttYQYqFJ+OHzk8cpH4ReAvAJvSav8G1qjidLFGTkwAk4gXiiSP3RvfwAmHV6Bb1SF4dt4rEgLlqcaEb0kM0oSjkMM92E47mlr2oO+xf4sBp34LbTkNnfhE4OYwZeKdWDrveeX6iBKyAdeNOR19//YsNDnA/Flv4IwB5yPvUDvkgGQj5s95AEOvuUUkj/aUYBRkpSUgtxV1xx+N19bUYw/1ca6AISceAX/3Wsx94WnUXfND7KC2oTgnDpBtweB+x+D9dRuRSTTcokMlfgFVseyDe0k9fxJKPx20Ss/s+++uSSzXU14lF041qRtQeLgt6tEc4G/Hy3OfEKk47dQhaHFBcsMTx4NOlnmczgdjSz4ZB5g9bykG1Q1B1lGfmdrA94oYO2Iklr6wEPSmEbYC9gaMG1GHafN/BwY82z/cgb/rfSx2tefgELykHsvnP4kzxtyAXVSuDKOFhagus0D+E/zkznG4/eFpAnz95zsw4frRgP05Fj//M5x11a0CLBkCYQHI7sQtV1yKN99bL/O1JeKKwTE+gzhOaThXjlc7wN33UO49YteBFa9JSaTcqWCrb0swGAMytGlBktmCwf2OQI1VidmLfy8eKHm7ZOcQS0KDlKbjQe+X1g54d/V6DKw7F4UQyNiuWO4w9HHR+cPx8rLl6n0He4DCh7j50jpMX/S6ELq4tREDjjwSm5uaUKBLVPwEv140FWd9+0YBSNINnpEov1ls6fLpj+DiK78nTtILy36Lte++BQQ7Mf8XkzHk2jtQb8IhMS/Zelw3/CwsfOlV7KaAG69aWRImniWQhuHluxOwZYHYG5B99atLwFJ6NHShRDDQZyhj3PokEaIHMbfqqc1x2/DZil/hUMtCzUHHY1OjOiteEsH3GJdQGmIkTEJEmhTYsOkznDPsQuzJq9Is+ja8wMWYkaOxaMFi8FnEGSC/FrdeMhDzl/8n2onjx9sx8KuHYXsxj0a6THE9Fs55HGePvUkklikIna8AENiwCV98vBLHfq0PtjcX8L0JU9BO4N2teHXRU/LcZyaLBjsDuA347pBvYuHy3+CLCKDbRpVM8yI5qzAWD10n+W8AFc3W6fy+QtSM02VgmXEicOIoxB3ZFontqCbhqacriDtAbiOuHjoQ3asPxR33/wIFxoKUBKpi3kNvlQTlWCETE4yKZQaxhrzN8xxcOWY05s97XoGl7OfW4QcXDcCsX72GtgDIrNuCs/v0w0cNu5CREeoxf8YDGHrtD0TCCnReuTyuP2wHvFYgzGPE0LPx+BP/ggenLUI7J3Y/xy+fnSTAfkFgZR9FIL8dN48Ygjff+UA0BONfqmkqdj6WbucvA5ty1j5GdF8Ay0UzLBHikPmM3eLGIJIUScAeMb3H1By9xKgFeza+i2MOOxQHHHA4Xl+zGS10YsSRYqaR+V4OZJIRtKlJgHzgiwfLmJGkm3DbTXhxMZ0n2vUCEGzH1RfW4V+XvI5WEnnzTvQ/8GDsKvhoI3juBiyfew/OGXkBPm710LN3HRpbjGlnvEovNixh8ZxpqK2pwso/1gvLIqzHgpmT8I/fHY+dBjgU9wDZBpx5Qj+sXb8BbbFmogm6T0YNI7i+h4Q0MB/yA4/y50snylf2yR9dkljlRkosiauxKLeiOWOTUkxCxB7LAPR8fSR2OxDl8ePbrkWNZWHY2H8SIGijsl4swT7v8wpUn4F41UxmkFkCQxm3lMWyhTMwasS58EMPgV/CkhmP4yCGSF/thw2ZCGOGDheVf9Hoq1FCgpZt76B3TwsVB1m4/6mn0fPowWhoAQo06SQlmcktYdvmtRg16gK0ZBwJvTM71+C4vrWwqg/Dvy56A4ywmfVq2vQRLj33bGTyBdE4dJpICcGLKUUBtUPVfgnHL53YJ3iWB+kasFxcJ0mlZuMp+YjEUqWGTBuZNJtRUZSO3McY1K87rAP64cU1TWD9pt1EPQRKUpGJOjdFOyfjpsmM2M/hi8/exZiRZ6NgBwIOF+IFNtqTGDlEiELafA9BwFQJc85aeSETco2lELBNBYbXqVAoaRHyCMhq9GT5bNCKktMs+6RZpFQy/n1h7myMvXyExOxBEsPxbMlnxxEzT6rF4ojfCi7n5Dz8LtMopdVf4dsAS4mjn+iZhJ4ugvOVF2FWxK90gXKRPxicm3ScrpGGUqsqUsVhui0O4dqekboYcD7FS7MfhFV5II6vG4ldgQl/OJbHtVBabZBQUUw7rSsJyCRca9KMO2+7FguWviTGgDaaBKdFDvksHARUsbKOTutjrjpkWkLPBZyPZpYJC6pSPocsYicnzJiamrjAdKE+kwsijL3qSrz37kphIK5TAYxBMDk+1y/Vqk7AGhKWwU1/y8BS2VLns8NGkylEVwk2NDukM890nFc8UvpzXzwsDu4WW7Bx1WIcUsUsSy/8fNkaSSBIzBhHmjfQHJzYOfKhJ+IDJDwfJJISFC85cRDHWvZSO2v2THZPd8K/4zYgbMB3LzkLB1RaeHr+6yK1gp84VD5Kkh8KIcl/LliYn1JMNZ1B4LXhkktHoanNlpwth2e5zI0opQ6CKK8aI+owE6owPSEOCVRekiEIzYjaba5XCZsw4UCRpkTHwAuLl+CuiQ+YjTmI/EKZx+nsqRoOxGlkaOYzx0lVHUTwAxqGEEVPnUIBKSgAxd2Yevd4PL/4N2gJ1QnzYSMO23H3hBtQVWnh66edjE0ZSbcAYQ6IMrhjwu2wuteg32mDsSnTIrixwGkFLC7Sqyyuw4/vuAJDr7tX4jXyYJT48KniJFlLJmEBLUYuVo6PiEInytBbZV01ljS78pVK8J9RUEBmkr0Vn61ciJ6WhZ5HfAP1jFo8dUBIekpeOj59Ko4YMPcsxfISEt/GroZGDBl2MTJteYQMMaR0GMMOTZ2G4ugnUoBgyo8aII5t+HEkyRHCSA9eysAsBZtDvfpIqkm+bUqMCPGLaT/Ho489IVJBJkLsInRycIIQbTlqCC3UlwJPWhBUh0fwXc8U4QN4sTYBSBmTBPIyuHnoAPS2LDz7/G+FwYssLCR5zJkzDaNHnYfY34oJ992E00bcINcRl7B45hMYOuJiiaFvue8+nH/FFWiOIwm9LFYlIns3EG7ApPGX45ZHZktOlCEIuUscIU7ukI1IbB9OrKpHi9jaOeA44lZoIj+OtOLxZ2U3BZlSYFQ1Jcqux/dH/ANqqg7FvVNmo0nzQIioYN2SGDWaKqoarslDAbbfJJKY2IHEvwRDfBVTXaIy9RMm5emFa3ycqigtImhTABnlfwNWpJYOoM9GABY1knII5geJJlZYHhSD4ABeAY8/OAlvr/9E2DuJYiRSuPcw8f67sWN3k9RyOTfXxCcn3jEOze8vw/eHn4Y5S98U4FhIZGPAlWOvwbp164BoB95952XUHjUQn7Mi4Xm4btQwvLX+QzSFwOqVq9Gztid2haF475bIQdgC+Ovx0PhR+OGjs8F4jUSk2qLXyZYPERcuku0pSapqY7mW1k3ptTJDyAUzkZQSs0OoU5tBdcUbaLda0bJpNfocfDC61fbEp6UILSLgrON6qjlpP2OgxaEHqm04UdHRNUnlJhFgHYdyHsOOqIy4FhEfKfoL+MJTTGgEQlB1o5TdOtaoSkLPqvSxbizPUMWzJsz9Ub2TQuQ4SS0z/5SXqs4Hb69C7WFHo7613XSMBBh34/W4d/IkqSoRVDpq9PbZ0YEkC9if4IZLz8CzS34vUQITlDs2r0Ovo07B++9tBLAbDdtW45DDT8HKFTvQuH4TTvjqwXj7000iobnPG9D/8GPwb++tBetRlnigLHfl38VT912Nm6dMRwOVHfcvkhlqO4pENYxL25GEbeh7RE/U0iZbFqpre8CqqoRVVQPL6olTTxuG1qwHJpOUMzVxofZLC+1lKWMNM8ri0fFjUVth4bYnZgjHOQSV8xvWJhFp26krhJbkGi2GqpYw7oQfOpI/pgZWxjLMxC+eEKYRhI2N1L//J2BFw4RGA5AfjSbi9AIy23won1ERjl2UyGne3DkYO2Yk4DlY8dab6P/3/yAASLOFRIdsAuBuHIAa09+M0cNPxawlr0uqNXIa0b77Uxzduw67WS9EM/z8ZzjxmMHYsrYVxY27MKDP1/BJUzsz3UDGwWnHnIyVGz8XwbRENhNK7EeYMn4Ubn98pthYLpXSGdIrpcQKezI2zSqHBbQnpLrSnn8xOJeapvEy+ZvnKUUpkZk95iG2jCfpDZJjS1vRv+9BuOux6cKxOn9JnBa/6EpMybGYshNgBS/hPslQ2UmMUqxMIwqO94mi4dwsRHQAq/jKAKrDJe7UBKmsSzSP2l7umwn92PfguQRCP4WiLUPaPjUauUjDK2oJ+e23Y8p943Htldfg3PMvQr3tosFPNNvFSErCMGodNiKQ/p/gO5edhbnL3xJgETZi9+bVOLzXmdhF+xQ2AeFODDzhDKx5bTOy67fhG72PxdasLRkvtNo4s29//P7DT1RiCR7CZiD4Ex7+0RW49aGnJeUmVjRm64qqHha5Iybow6zmZtn5x7YXEZwYBU89TZdhqylyOyxjCT2/DCwJQJuVcFPIYsEz03DmgDo05wJZqC1JCRMyUaPyT3rLCWBVdEN1ZRW6VVg4sKZa6rjVtazvdodVacGqrkKFVYuq6h76u4r1XgvVpu5rWZWiaVgLprfJo7KyEhVVlaiorIZVVS3fcs6y0L0bx9P7qKF4HHDgwbAqKlHVowZeFGpChc0EzFP6rC81of7zD3DIYSfhnodnojWJ4CKWfZAzKfXldGzUCkQbcc0lgzF1wRtoohChEbv+9Acc/bXzsJ1a3t8Fv3k9+p9wFj5dl0H7+k34+lG9sDVfEC8azS7O7tMfr6z+UIGVeMtuAMLNeOTO7+DWyVO1XskATxrMYhTdQIgqUuu1A4XdOLnvkaitrERlZTW69TgIVTUHwKqohlXZA3Vnn4fW9mJZSDrUnJES8XYpbYxXm+AVvkC/vqdgwcLXZAqGA26iDWki81Tpmj6WMdMMFHPJ0o5idAI9dtEccSL30/lONYb8JWGWamBdk3HihGvUUFBi+UwqucK5VL9pjw3tasBuRFUCLDuSeekkCage7WwGiJsx4rKhGH3VzfhKn4HYkS2gYIokskkKYegbhywDFP+IH4w6A7OWrRIfgxLaUr8WXzlyEN5f1yBOZm7nWhzdZzBWvrkNpU1b0P/wHnj9j+tEEIqbdmPA4cdhxeatoEVW5ylmC8gWPHzP9Rj/6M9BG6uyyIYw3aioL1KDYMSMD6lCCZRqaV6SDRoJpbeZEodEoHpkOMRv8S5ZHI+ZBGjEC7Om4sRvjsDOkmaIJPcrCQber+NzPSS4/kuDbz4s8VHyJTxSX0uAJ2aRxtusAMl11nnNhjTLxDGYm9buQu6BIYwJzVP6m4m4CQ6qP8Uvo5rmfuU0r1P7tQJxFvPmzsT1379BLq5c8R5OPuMCNEV0r3y2ERjtxyETJKUmwP8U4y4ajNlL35AOD3rZnrMHxx9/ChYtWAokTVj7/qvo9/ULJGuG1h34eu9qzF46X4D9YMVaDDppoAgl682WK22dBKsBd9w0FkOvGic2rl26olWFKtebPTGRT3Cl98hsshO4akM98fgEXBKX1RpSOYkhDdx8VuLRLBo3vofjjj4S//HBTnXzWbazmbxghklz0MSCwBrFrwQ0GR2CwgwP7SivlxmQTMfSX0KgItjSNB4iLrHbgjCrM8YCBTWTlByl1Ag4ge675HXymwVQA6zx/gkukWXYQz8i5J7cFqz5/a9x4UWXoC1fAtOf3MeN4x/CkDHjkAV7rBk2MT9JRiA9i0DLn/C9YXWYu/jfpVqUidgC6+CeH/0QV4++EPncVtxxzz/jrsdmIUsHJG7BwxOuwsWXX4wmx8edEx/EpMeelDiW6VmLLgNbTT5c9Qq6034cdCSeefE10D6SQ0mAVFpkb6JGjbSajQnb6kX1DlGUbkPa0bSZTNpGWZmhxipoHxQl84EfTcDIS0aLt8vUA8MIyTtS9xrwODRPazs3dyU70+viwBHUWFrKJfzgo5Ev+yJD8Xm2vbJzn4wQea7Y6jY7TdZ7KBWzkjHSexV4zlneFv/ofFDSkkQiBjIOaSTdHG4RP514N9Z++EcUZMUeCnY7XBeYcNcjyPq6emGmIIfIbsWvnp+BQcf0woFix2twzoUjsam5FTb3GezBhd/6BqzuFsaMG4c28gLlKd4D+F9g6PnnwqrujguuvxYNCISO3L7FOJUKLCqxudJDs5OIV8aUIR2nLwNLlSNDf1lc5QztZgqsAhmJvWaiIELsklvpFrZj65aP0Lv38fho43YU3KIkFcRUdSScdA75l3NyyTz4t1GNDIukp9hItDktOeK4JM3g/LtIu8txgryco5KXjDQrTixKMGb3Xek25H306JmT+YvgpnZGVpKA4RkjCJHAUkmaAfh+AVvdaL19phpZQGITgMMMGVmBkUURiB3EJVcmY9KHc9J5LUjOuQ3wpVVAEhFcWxSV4HsNSOJm0QbMcdMF3YMYbLslA1oilWJ7PFAtcTrp2xHVqTLCwcqca0idMm8nyhtik/DsC6bDRWDp4jMcIJXoMRqHx8thzLdHYvwDU5DnBAQk8BHFbAdVCeBp/VAlp6BSwacf2msypqpMEoQSz/m8fCsyu7bgqBoLRx12MLa05ZCh+nP2YPBxvWBVHYppv3wNb616W7zlA2urUEEvuYadk9V4YPKjwgh7Actp040bYIt5xyQtuGoPTmiLU6QMTAdJtR6TsBKbixmK4UpbDtWXjdguwCd/0uv31YypiQwQuuzN8IQJqdEYJjGNSbbkO1Ns4SWd+EsycxGTRokCK/ZPnBtPWjmFcPQUTds3N6dSogRM96YETkUk/VaiSzgjLS/GYQpc6fwXgEIXK177HQ4++EC0+ImU6wTYSPuKqSeY3NB5zdxibzmHYTJdpLymIa+HGLunqJMRmIfM4d+fmyp139sfnyocj6ARH7z+Ir415vvC5W+ufBvnnDUI2bY9IrFbd+zEUccci22NzcLkXEPHOr4MbMhgmYB4jAL4JoISmOcIbswwUapCBJivQ2idmctngkNehWHoU/ayWTokVAwDjDbyXQEtx9w0x/Vs2KWsyVypP8BsW8Hlay/qMliSLmMCIWKPEsWcHfvav0QJk3NCTpWY9De/9SDYmnTQEF85TxialjagUlEmoRMRcfLQxtVjrsDkHz+CNpvEYIM3A35V/eySkGSAsZ+prZUJiW06uXCDSiw94YA0pksrDgm1RBHLpk/G7TdeDesr/bCNHXHhTmx8ZxnufHKOxOurVq/DlEl3yRpd18a9DzyIyY89iayrbxL8j8BSI7G5Nd8m9pbL4lt29E8IMF870SWSRj4cX4sJToGmimXGSH0Kql55/Y9JxgilmBovVT90JplGVdtsO2HZb6E2pJlj+BWKlmSTtb6dZonnGPrl0ETUGoGV1xQ6JFTBIYAddkeBVGD1eirXGgeq+vTg2TnNXnGXfhYvzH0Gpw44Hfl8KDllqn8qoKyUtEx7DEGV1yWUS7kxHmVQZedE2RN7pfCaxTFvJ204efxyzpPSbTHssqsxcfIjgLcZ69//NW564BnpRmTCg71OtPnbP9uIw/oeh1dXfSDSSmbZC1jemx7kXGokYdxQCvm8RueNzzBm5Z6E25hjF5Oh9o8dmRyG41PgORR3x8qTvoME0CPnTREdTT7PnZIUjLM5DBk4rbqJg2tr/ZlEcmljZVARSfNPqlLT7/Rax+/0EX4TXGFL6hI6B8bw0/GSd1/iEoJiRhYpDoTdiJOOPRRLl70KmxQwgxEYAizDcXE8z6FLAa67/Co05XNg1ZNd//xQ1WsIxddE1AZxDBmEAyXk+gKemz8NS5bOwpr/fBVf61mJlu1v4f3Vb+DWHy/EbkqXzMNOxZ14+Ec34LxvjxPAZazOwMo6uaDykuVvXaSeNydk6WWG4HPlZ024ZG4sX5LfSsf0nDzC8+mJdJjOk+x1TZ9P6da11ph0QZyA45pXFAmuaH7Goh5TkA58m7FvjPmzfoYzTx+E1oIWuuQ5lsKMQhd2J3YcjwDnA5x+0jewrWmPBOLUGSwR8gVjucmoayp8AYMOrnTGMYFn45n5z2DRL2cB+R24ftggTH/iNrz51hu4/eEXJV4n40vCpWUDBvTriT98vF1Lh4J4J4kVIvJm/XSiaXpqv/ruGrCdACUGInFme8ZpVHKztbOwE83bP0Gf3sfjrVXrJZzgMylH8ln5neJlBLLl8y9w/NH9UN+8B+3ypqy+3abvpPJ59jLRwpjnKSocSDCI8bNZT+OV3y0Bgl3Y/PYy9OpmYfV76zDxqZfQzDmoO71WLJ43A6NHj0KmpK9UcrySyXlzSPmIA0cVqaqxk8JJ79hvvrsOrOTTYunek74Kbj59XZ80ZtUjagPiXbj/tu/issuvE3XNHKtcY3BXrgIZoKnV6f3FwMTb78KgvxuINs9Bli9QMfGQ6NvsSsVYsks8JwBwPD5IE+R6mDN/NhYtfQ6ImqUXeFDfwzD2srF4dOZvtNzF9RV2o+/fnIzZi15CwCRGEoqZoOYhw+0FrHltg+fIP+Tt/fHTNWBJPdrShC8jqifIv3lww/xX3kSP82ja8ra0f7JIYFV9BZXda1BVrVUXqZjwrTtWZqTywuqLHlUV1Ti97kw020UJ2ilJLJXxw16iziqRcbMm6Elyehghnps3FwsWzNc0pJ/By7OeQs9u1bj1oWc0/PEy+NWcqTix7ltoYGQWFLFz4zo8OW2mqP69gSUTagjy/x9YkzggKXmkxpGOOw+BN8hjzMVn48AqC926VcGq6i4lL4JXUVEhh1VZATlMyUzKZxW8txr9Tx0E9gBlfQ92oiFRgX3HlGpx36hTSXSVWmZ6aHEjt4hZM+fiuTkvIGEcwtdNsjtwXK9DpO7LrigUmnFKv0MxY+krWgcN8lgy62nc/8hPysCKVAoHcY4OxiW4ck32vX/903WJFeumbjx3yc0yHpUwPbKFuEKMKJTGbnbdMWGWhhLEI4216OmmDEKnl1qeEtMeaFuszf9MhJAx4meo4Jp4T/hJJVTnj+GWWtG0qx69Du+Dr/bsjVUr1uiAXhMWzHwKEx75KfiG3Jp3/oAezJF3Z+22BjWVFg7oVoF7p/wEOfYVcGxBzxh/g5/gvH9huddqugwskxMiq2bfCgxzoWx5YSWFiXe++sjOApIjjyjICMGYSBGipVQqM4YKPk1lxmRdSqyBEkzqAUqmvJXXaS+UJH23zwGSV2IAAACXSURBVGRutL+XuQqaTQpxxLek+ZZ6UpT/P4IpOoZKSZCXdKqEaNB0Hx9Rhc81doCqjJPOy/P756eLwHbalAFpr40Lp6sklQEURuhIdMgIvI8Pqh+lUmJoRgLzkKGocTv9LRq3fIIMRgnWwKk8WHo9xUaSF+pFM0RStc01MhxTDSBaIfXy5XkyjS4o1Sg6fsf5TpTYL/78L56uHNxbT0OfAAAAAElFTkSuQmCC)
..........(3)
Where,
I= Attack Intensity (%).
ni= Number of samples with damage scale vi.
vi= Scale damage value for example i.
N= Number of plant samples observed.
Z= Highest damage scale value.
With a scale value: 0 = no attack; 1 = 1
st (lowest) frond damage ≤ 25%; 3 = 1
st, 2
nd, 3
rd frond damage > 25-50%; 5 = 1
st, 2
nd, 3
rd frond damage > 50-75%; 7 = 1
st, 2
nd, 3
rd frond damage > 75-90%; 9 = 1
st, 2
nd, 3
rd frond damage > 90-100%.
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)
..........(4)
Where,
Yi + 1 = i + 1 observation data.
Yi = i observation data.
ti + 1 = i + 1 observation time.
ti= i observation time.
n= Total number of observations.
![](data:image/png;base64,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)
..........(5)
Where,
Dic = AUDPC on the control.
Dib = AUDPC at treatment.
Analysis of salicylic acid level and the peroxide enzymes activity were carried out to determine the effect of organic fertilizer plus and the Biofresh biological agents ability as biotic elicitors in stimulating plants against diseases. Adult leaf sampling was taken in the vegetative (4 WAP) and the generative phase (8 WAP). Furthermore, the sample was extracted and analyzed at the Integrated Laboratory, Plant Protection Department, Faculty of Agriculture, Halu Oleo University Kendari. The salicylic acid level analysis was carried out using the UV-Vis spectrophotometer method
(Warrier et al., 2013). 9 ml of the mixture contained 1 ml of leaf extract (at 1:10 g/v) and 8 ml of Fe Cl
3. 1 g of leaf sample, cut into small pieces was placed into the Erlenmeyer, then 10 ml of 10% ethanol solution was added and shook for 15 mins at a speed of 150 rpm and filtered. The filtrate was placed into the eppendorf and centrifuged for 10 mins at a speed of 12,000 rpm and 25°C, then 1% FeCl
3 solution was added. The absorbance level was measured at a wavelength of 525 nm.
Analysis of peroxidase enzyme activity (AOAC, 2005), was carried out by weighing 90 mg of BSA (Bovin Serum Albumin), then dissolved in 25 ml of distilled water and added with 1% NaOH and aquads, in order to obtain a standard protein solution of 3600 ppm. The standard solution was pipetted at 0.2, 0.4, 0.6, 0.8 and 1 ml respectively into a test tube and diluted with distilled water up to 6 ml. Subsequently, 6 ml of biuret reagent was added to each tubeand left for 30 mins at room temperature. Blanks mixture containing 6 ml of water and 6 ml of biuret reagent were used with 2 g of protein samples were dissolved in 20 ml of distilled water, then centrifuged for 30 mins and cooled for ± 20 mins. 1 ml of the sample filtrate was pipetted and added to Blanks mixture and left for ± 30 mins. The sample protein content was then calculated using equation (6).
![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAGsAAAAxCAYAAADdqledAAAfjUlEQVR4Ae18B7hV1Zn2ASIKjhEVoyKIaEQTO6BR0MSuIEhHKcovYqJBLCGWqKgUQ2IyY/5MDM2CJKICgvrnt9HrpUgv0u6F2/s5555zdi/vPO+39jr33As4JKAzmWfO82x2W3utb33v19e6xEJ4CBEg++Mlj1A/CcA2Plw51MvoPdvktJN32WcuAFe9lzYeAB6Nv9Hj/KPniOCQfZNWwGdXgRrahQEbSXieo0g3PMD24Hg2DHjIwEed5wplgRcCPIQdAbzABQJ2FMCDOoRKPUeZ1z9K99//XewgsEjAIcEiqIohDQjOjqmZph80BqfxPdsJV/QH/+BZj6vOWbBCBZaDDDJ+AmEYgZDJAJ4rApoJAwHKiQB2XR9wQ8APYdu2EuJQ9ev/9wCL0pjDtENIDd/rI8tg3S7L4npm8ZVqpyQ9955v1H32w6O8UONS+tkx+xbNknE8WKGptMIFfJP/JACrEju/2o0bbu+N757cGtPfmgkCZniedOCalvRju3yqwarvV14e20kcEQ9iHLPBuPpBzsNDPFIf5bTRIOq2ekKN7785sNREsuOJgHmwQ2XiSE9gOYBRhK15/x8nffdUlFRk8OaUmeh0xVUoS6Vg0VC7gGNR8wCHN7zINa9HxNZvplEWrNxJamk65JDZhpFE60bRc0q1SPZh7vlYS75ihO7gKM/suIEFUP3ZfgDL84XpvpUB/Bqs/mIu+gy4G4YFFG0pxIVnX4AK20axZcDhtGgJI5+nqYqmc0xJ1n0f6bkRWDRb2rccwj81oFi34+wi9eQkNRjRpHUrmWUjM3VMZ54Fi76mPrAhPQTMNDMATCCoAYI6uFrWqkN0vehqbCstRy21iYetzLfrRXOLuNlg+tGzb/N0CLBoAng0AktPTlMcgRqGfuS8Kb2MGaOYz/eBwM9CLz4lUEzIahY1IQy/9jhiZghf+U8GIUEhnYESHsNRcyJIMEoAP8mAUBFa7OKmi7thc2kZKhWc8qnj+lq+GpCQnX6Dp9/OTYwUaQIYROgQPRsl6peHAcsPPQSBB/gOwsCDE6qACj6feQ3BChlRajOoOgyCAF93HBEb2GkWLFOxPKKboFA4OB/PqQHCCoRGCSqqU7j1hgFoE2uFNk1aIz+VRqFAXc+PIIc3QnhkQOqF7YioO2aNYqGaCfyQUk+wmFOpMJ13Aibb5IAV0qBHbfgNwfJcQzSLaYol6mUDgStpC4UYkgcpx00+in8hyNHP9yMNZXbmurAsuvu/40eSpGMObmdzPIJFkyevQgNwdgNBMdp1+CE+nrsaqACuPPNiAassAosuTvusCPOoA3XKBctjBMnZ0GowPaAw5sxFv5cXJDFqo7/JPes2h3sWE9FXYwj7mU8w/c04hkzQc5gYRmCRSs5E+KKSRvVpkAVLSyM8MsyDy2iZ7QMHga9CYnYhSSo8OI4jE9WEGoahL9F4otkXjS9IRBYsMk8DRsFT45MOBeJ+zHp7Irr3GqTMYBVw8yXXYXc8gfzAlojQtDwBy/NDBXLOeBwqYpc8zWV+TjO5JGj8Ufg4T/5y2/M6914a5PzTeP4xTjJ0PWEMu3YCH1agJilEaeqYMEZRFftTuhdKeKuctysARNZPuGfbyncEQrPSRk2L6yowpS/PE2nkNSdIIhsTqr875Jk0HgQWQQvAIIFXbOLZdUBQghnTJmLCb/4NGQMo21SMH5xxAb6qrJYAw4j6YXv6rdyfZgXPuT+CoX+adq1d1Dj9ywVGP9fPmIRrcPU7/Z0+x0JGSZKlA9QKksfJGY5ysK5jiT8SMxaVdNgx2yh2qC9MKwXLoilUjHN95Z9470fBmes7ME36FP4CEDBOjiaPRKdSKXlDonMZoNp/zb/RmErkI8oiztJMM9NSTHEBuwJvTf497ho0GIYDTHzqZZxy3MlYtmUL6iItNDNW1gzqUaPu1BD6YXTOpVUzX5/JeKmGROYvVwg5bw1Qoy7ltvG7GAIbcJUGWLYv9p3TNZ1QCFYiS27bgE8TpUBgG0tcWgACpSdD4O2MATtQvkI6iaIyJaeBmETLTouP1FLU2Ef9XYBlwaIUU8pVJYJEmU4KPkw4bga2kaR6watL4rpbbsOlP7oWyxauQLuzz8VFXbqgPJ1mACsCR63yeMO+vyYpzmV+LmikX78jcBo8fc4FhyZSt+GZPMntS7eNwUsxtReiSBeDAyaGpJmahtBF6GUQugmJssLAlneSj4jZchGEjmiixB0hgfUkX0lLScADAg8m66diYur9CbvXE+KZBOYemsj/9Cx0klYOEB0yFuuyaVhhHL7M0QPq6iR6SDo2mHlZXijzSREcDsRarsVcLapgRP2RH0rYVJvGNGmh4/NcRude57bRYOaCpwETMiLQcseJIUgDbkpJEE1WyCRSESbM5xQCTisth+uxVh0KGCnxmcrs8N86wwZYizOTePix57AsbxvgUGuV2VQu1kXo0uAEsBxlp2kmXnjhBWzYsEEmqiWLZz2Bw581PqwnRcVawYtxt4MA1KyMMr9SpDURWgmhn+JHeRKZoum3TMUALxDzRBMqqhaqGJmBlzA3ODg31AEEmatBocnX1xRGbdb4TAupBiPXVMoYkSDnzju2eem7aBGLIfad4xGLnYgWzU7CezPngr5VJImmL6zDnv27cePNP8YJTWO4/JILsGDVJpFM8kd8TxScwE1i/HNj8MzYcSKpG9euxskntsQV19yMqgxV3EPgEvgAzz3/AtauXSvXB/J34c4et2L56rxIggP40bIGFUbhwESbjGLMyoPX/JwgRe8YMIlWUYMZ0ZpwfBXZUl1CmsJIeGjUeVCIRKuE07whWFEi7Tvis9mGoKqc0gPTF1k9idTNtl1551LwYcI1Enjn7RmY8c4cKeQLmXTe8PDUmEfQ6l+a4L77BmWFRYTac/D8uJcRa9oC9414ULS7HiwfMfgb8KtH++DxCb8FJW3+G2/ge7EYlq/YJdmK71QhnSlB28u6YtpfPwSsUvygdTO89eFSHHDV8o9t0ux5MIIU1uUtRt8eNyAI40L03XcPR1mljTHjfo8p784TIALPB514vz79kUgwQmNOlkDesv+HQfc/IAwUs8WI0QPMlGKmIYKjfJ4NF0boSH7oWFFexVSAys0g3alDINUMbTQIB020SrrYhgd/PPOt8F1eEG0+4fgp0PRnPCDDx9Q2z1IVX97TrXmAQTsv31QBfiHmTp+E42MxTJ31eTZwQWBi9fJP0bXzufDjW9Cnd1f869z5EoUiU4Vdi/6Gjp1vxl4TuK57X8yZ/6HIoSLSQQzGQox/sj8emfRHVJLQdAUevvNGPPvSFCRkFjWY885kXNt7JGpInFOKj6ZMxKszPpTyjCTAdORBLWzEMXr0KPxt/lwgLIOVKcbDj7+EKgf4Ys0WPPXSOLWghwArlubhhbEvw5SM1QXcKrjJEgwYej+WfbkVLk1vYMr8Gf/Q37GCTmb5noWMlxYBEUlHANthwOLCJn8lLnBhk9ERGKqExu+jB5wLXwoAkdnX7/hcXhD5BNVRBEiqi7aB0EnL68BUzl3ikABg0MQKyRfzX8WA687FmS2bYPKclcy7kUgxLjDx5OM/x8dzpgLhHrw9YzxuHPlzlHM8owaTRt6HyXMXoQTA9L/MQ88evZG2PQmZgtAmWMvxry+OwEMvvqoQdgoxafQgjBr/J1ST5kwC991+I6bOWaLeG+XYseoTzPjgE5RYsugqAYiPImScYlx51Y0oOlALpA6gpng7+o18BuUAdhVWYcSIIYBTBrhxdL/zbqzZVCjam8xwkow2Xbw99a+YOOF3SAcpOLCRpsQysnTUgqBKCchhF5ZZJwEQzaXDFEPzPwTq0gRKVWVyz9JIN4yQVKdIM9i1BpJsSu/Hp/PewLayGtRQlmkW+dzwMHPKDNRWxxXm/JzmGCZeHvsQULYGI/r+BH94f4nMnxUi2HXo1/dubFq/DjB2YH3ebMROOh0FlQGQzOCnt92OhdsPSPvFHy9GmxanoLgug6TorIsY7DV4blRfjPnNdAWGux8TRvXFz389XWplqK1G13ZnYNGGfSgTKczANyvEJNDekzwENHn5WLv2I9zaawQyku/WYt3C+bhx4GgZ/Msd+/DIQ8Ok1FO8azVuumMACpMALTzZ7FusC4XY9PlqXPXDToh7KSSpKdkxFBNpqpivgf5MfIDyA6x8cKWXa1CsPJBvSsN0+q7NXoSGEJ6FLjJhSjAIlnJFDI1L8dKTD6DHPUNRJRaUbRws+GwZTjulLaritXBCG9q0yxqYWwsY23DPLZfg3+ctwwHS7BlAJo4LL+iK5cs3AmEpivNX4IwOl6KMda5aD93adMCHazeLkmTyy9H5zPbYWlIhmslZxmCtxguj++KxX/8p0pwDmPSL+zF60kwxc3X7vsLlrVtgydptIlmOsJcmCKhjvhzYCJ1iwN+Gt6a8gCfGTUOCM/XTWPvZPHTv/ygI5Z7yWvz0Z4OB9G7MmTIJvxr/OxmPflKwZYc24OwqxA9OPwO7qstl/HhgI21wHUpiaQkrWDwGa4cuq7Q2HNdQgQYjWVeVtBh4qIVfapfKDRU+2rdEUiDRCNtEYEYgkhxZQ3AqcWDnOpx/znlYvWwVYMYBz8AtfYZh2rzPJdZMSVrDoCSEyQ/pgzPbMaJnF7w6ewGKKQpBCsW7N+OsNtfgQBEBr0JoFuKijt2wdkEB/C+L8KPvdUCh4aOE1fDaBK4+sw2W79grZpFuQMB65cl78NiEV4Q5cA18//RT8cdZi1EdAJsXfY62zWPYXxUXpjsSXdXBYbQoE6O/KQaCbRj35FD8ea6y0fAtVO/ZivtHPI1aC9iWvx/33tsXsMowvPdNWLVxmwQ01E6CJaUzTrS8DNed3x6r9xagKFsFD2AxPwppv5nn2fBrErjkrHZo3iSGps1iOOH4f0HTJs1xfIvmiDWJ4YoruoJlRgVCY7CU7xN7JwFHI7AItOga/2XqkcGbr76Ckf3uEiFcumQRzu9yC4pdoE7SGAqUqr5QSIJUHKjdhOHdr8S/f7AQ+0SB0yjN34o2ba5DCf0CEjDje3HemVdi3+paeBvK0anVOdhTnRJ1QDqBbu3bYuGGHWKZHI9g2XkYN7onnhk/UaKW1yb/FSed+D3sr0wLwZsXLkabli1RYiQQRyB+hFNhYCGEsdpulQD2NvS75YciSdTqwHdRvmsbbrv5LtgWsGZFHia8+Dz27tmJO3rcjqpkHLf17IkWLU/G7NkfK7PDwmKqCCN7XI+PV20TsGiv1fI643fup0jC8eKQtRiLdkmxVUJnRu9RLY5aJUtqknoxP2L5TIXj2TOXHCKVUtrHygcPsffyirug4CSA9AFcdd4pWLZ8IW4fMART31mAWperZwz9PdiBIzumpPDtZ4C6HRh517X4w6y/iRKk7RKUFGzGmWddg6JSbh2oAIJadDr/BuxcVA5rQyku+W4b5FfEFVhV+3HtBedg0fqdYoGYSsXgrsbE0b0Qa9YCsePa4tyLr8farXuVH/EcbFlAsE7C9pJCpVmsaDBXinyCTM6vAeq2YMRdV+MPcz8VX0dtgWdieP+BiJeUYuyYJ7B+3RpMnTEbz054BctXLsPEcc+ifG8+etx4G2pT1DFLJvlA92sw7YPF0EsW5CfBZw4TikilgbShKku+A89XFRhyl6DxR02lMDGxp3NnlYURleR5Qj/vxWZJ+yxYBDsKMGRcic2ZOxRj9pTxiDU/Dh273orSpPK1rHQaoVqpYB/CD5fOeCeGd++CKbM/E7BCJFBYsAltz+6K2jhgmSUInTJcek43bP28GMamEnQ+6zwU1qSQoMDYVbi87elYuO4r4TuXemLwVmH86D4YP2m6RB2sLRiSodgIQxN7li7B2c1PRH6lgSohzBNnyYlIZCYLjwmgbjsGdeuINz9fjnxABuB0ti76DBe2aokHh/eH4Zq4a+BDWL1+P5544jF8tWklkIrjZwP7oyhRgTRzM38Pht56Gf48a5Gov0HN4BqZrG+xDEQK07DTyYiprFJQzVVerIAiUlLlkjVQPmPNpfnxTdCsWRPEYjE0Oy4m5rJJLIbvRNex2HdwQpMWYEjuyhKRjUCC9jogUwCY5Tjr3I6YMucLeUqgmHuZLAyAtVRL1vDgVAPpHRjevROmv/+JBGYhkig7sB0dzrkWBQX8sgpOpgAXnXMtti0rR2Lzflxy1tnYVcWok51Wosv5ZwtYjMop/DGkF+LXTwzA48++hlImd2Klua7jwg/TSGxejwtPaoWiBEQdJXbwU1LJpgxLSGrWAtYejLyjM16b8xl2AwI8C6iw44BRAYQ1KC7ei2t+0g/lCeDBB3+GmvydgJHAxNEPYvGmVdzhByQ34aF+1+P19xajLAQSUZJLhlMTLK9WSkhkD9fHOA3LyYjlYkKs8y7LDLnLQHYr0QSyfTrDUAdSevIYBEgGw34IioogA1vsJkeD7WUUWGS+WYG5U15FkybNcGGX61GcVAU49iKeTbatUSMMwCgHnJ0YfvulmDJrvgiuYVWh4KtNaNumEyrKqdEl8O18dGh/FTauKIWzrxAdvnsCDhi2SpnSVbi07enI254v9wosZxkYYDw+8Q0xOykuFoYOKh1OLIMwfyM6tT4VC748IB85GUp0naqtMf/h7L06wN6Fkd27YPJ7X4hG0JZ7oCYwh6oBUoV478+/xzMTfotqDxjQfyi88gogXYPfjfkZFm7PQ5LyYxVgZI9ueP3dz5VmSVQG1FSyTMRoz4DpJxFPlqJ9h9ZoGmlF8+atEIsdj+NPaCqacunFVyOjfL5U3BWlHkzbEk1l1EhAQlatA/pFtdHG90xZFeAeQoJAIJnMwsvgsnan4cvFH+HeYXfjtVnKvCWYRUhoHoiplqjLSwDxtRhx58WY/N58xXxWW1JJXHzhj5G3aitCZzcO7F+Ks8+/GqWlAEoLcO15rfHJ1p0SPRbu2Y7L2p+J3SVlEoiR2hjsZXhxVB+MnjAd+czbxGQEcg44aPFmdGvfGv/2l7+JGaQf2rjwQ7zx3lzRHraHlwScPRh+2xWY/u4SyQtY/XNhIY0E/KAaMMrwcP87UFhVKU556JARMIvLgXQtXvnlw1iwdR1qCX26CPd3/wnytuTLeEnSFKVGel1MfIP0kobtJaVOyMqF8lEGAjKXwJr0XUr7TIt0qGq6XrLPcHcuq0eydufBDQgUZxSgOm2ooCdk2pDGE6MfQZ+e3SUoWPLZPFzY+WZUcXmMntbPWagkrVYCyGzByN5XYMrsecIPUWnbwfdO+z7ef28egEIUF+eh0839UUNmxYvwozYt8cc5H0tgVVS4H7dc2xm1ZloFMRK6Oysx7hcDcfPAURLPV/tADcNQOiUuFFr5eHFUf9wwdIQK7dM1mPvqJLw5/3OliaSCu4aSW/HqMz/FmLF/VvkTJwBXtIAGo3D7l3jq4QekJFNnZHD3wHtQW1wCZJJ4ZezT+HT9WiEKdVXoet45WL5hi0ik0tBoexjTBYeRFKv1KbjcFi3aq4CiWyNQNN8SJESBgtIqE5adlGkx+ODSPafIQ20xCOBL3qbGoOEUKMx8lO3Nw2nndsS+OD0TNdFB99t64VfPvCSAZriIGvUVcsnCSgLufvS6/vuY/sFHwjcW/LnT98lfPotfvzwBobkfS5d/gJ8M/D/iepApxW8fHoLHXvqNCOnSFWswZFB/OFH8TUGNwf4SE355L24f9LCYHYbKtOBi3zIEqwgbFr6H2MknY8WO3ajasRld2rfFtNmfZqM1z6oG/P14508TMea5P6KCRU+J7cSaS23tjanT8O7MWWIcGXi8Mmk81q1ahjCTxr1Dh2F/PI3ajItMwQFc1Po0FFbXiPpT6GjhCbD8yEV2S58VGBLs0N9wZZ2axeepTKWq2DNidXw4Nr9Nw3JqVJSoarliEpX5C0Q7+bWTzpYvYBmVgF+E58bch0dfekWKAnHThmumsWPdRpx58qnYX1gAhjimpDXkmwckayTUv7zjGRg1dpz4rDr6QgCr85bj8kvOh58qw4ABPfDGR5+hgn8skalE/rJP8IPLOqHcAu7oPRhvvj1L0gKmBmRCbNeSGTjlOzHEmp6KR38zFdV0xIy4uNzPzTKOIfb63RnT0OrkE3Fck6bo3Lkb8nYVCRFkJFND7m3YvPIT9Bn2CMojsDgJBCnAzWBQ/5+iojJUhVuYeH3yRIx/8XEUFJVi8P2PgCkTm+9cugJXd+yIVOCAoQQDHo5hwIIEBbQ7PJiV8wPuroqWSlj5lq0F0oAZsYoKmfsEqMaYpx7EiSeegaVLtkTAKnXgupPte7IZVFSN3UrfjFjimPnmayjPBIgzj4MP36qTrdivT5kK004jHRginHEp8NqYPfn/4pSmMTTj0lOLVoid2g4Hag3YoQ/LrsCzvxyJE2IxDB9yH5KMdDkcVScVx9OPPQJGpcOGPy6Vfr5zyQHJs7wqwIrL9BgUp7jMIFm7DclmKcWyC0ZVwcmilC95vXwj29Z8LpdXwTeq0e6CK1AQD4UAtW3ZxIFdO/HgA8+AW83ZHUsvLLf07nk9mrQ4EcvX7RC/7KY9vD/1dUx4+mkYIf8cR+HCay6JMJkVjSd6JITrU1wy54T0Sr6oF8OeyP/Q55mcci0MuxzfP+9yrMvbo5a/WDykfYr2nnCzkMBhGxIscEeWlI64UBoqoVEEcGdv1Dak3HsiVGKRGKH6XMQU9ScnZR6qUkNK6yChvfpcCSLzKuYnkgZx+cVTi8DR/CXUCQLEpNodqt1MwkjhAa9oKyJ7IWeV8HE4jqMPOnuG0LJzCB5GPToGM9/9QPYLiuIHIfh3T6RdqgrSpYswzIgZY52P7TwvkGLosEGDsHHdeqQtW4oUehye5RdpoHwkA+gXPItKROdoDtK3A5vhN0z06XUP1q7aFjkrF74VQgJD+hQGI6KNHkxufYr6F/oi60sfqdjPM4WC9MsOS+GJ4ll9FYR0kxKeVXf8RuWFfMBn0TANeM5neu7iJ0P6rKP6cdldkaPzmyVLlqFXr95R3sNwVgmhFAayu29ZAVEVBG4OZQTGJHTRwi/QvXt3xOPx7K63hpOJZiaziwQpSz+BIlsa/hwW1eR5Bul0Ne69+36sX7UJAfMhMo2fSeRogWG70mVXgg2R0YihinHiHbNtVL9aQBqO+03cHTVYXP5m1ZuzVv4CGDv2RbwwdrwApsGidWLdTh0KLJo1ldi6KCouQK9ed2LlypUyT+LB3VYHgZXlwpGBxeakL2DEChN9e/bH1g3b4XH5mQm17Yof9UJW65XRst2ERIi0TJIONJDyiIDDE5al8FhfHCVYumLAnbX0C0rKTNPGL554CqtWrhXzR8A4afoXbhohwNQs8UH8U9F0As8//yzWrVuT3TbNXCjrhw456yMDi7uLWDtkmcq2azB4wBBsXLtJVnsdp050juUi+gU75IJnneRuNO9SV8wBitqV/UUamWPDsq++qYujBouMVyAFyGRSsomEJo9rO/zxzHutWWoLlpPdkCIVAtlmTR+gweY+v3qtUj01ZsGRgaW+oilMw7KqMXTQMKxcsgo3/fgqtGzZFLM/WhAFB0Cdy78/5u4tS62bNQKKW3Sypk9r1qGJa0zsMbk/arDIYGpVPM5F7/olCouhn9zzmT6UGRTNYuU78MRXkQH0fTSjeomDPLCd+sRVOmvwz5GBxS1i3ODJKMwwKzCgd38MHTREqi6vv/4nXH7VrVKrTEsSrzSMEa7lq/2R2smr/VRRRCqBRePxGxD3jdwcM7BInWa03kNHwJT5U+/qt1Wp7WQqONFBigKdmsd2GSbkX/tTWljfREt9/RN1xecubId/fZVGv7t6Y9bMvwBhCoZRi3btu2DjtqSseqs/V5KSrqyhcVVBBxZq5yDLVbQAPKitjWloPPaxvT8mYGmiNUgkUWuWJlcDpe/5jQaL1zqa5Hu25Y/h/OF/jd/xvvEz9uGopQvUSUI6eOAAbP5yvSS7phlH23ZXYvpbX0heQ2A4B9YQSY+2dEqrCJzSLFWbPHisw9N6bN4cU7D+PpIOzdwj7+NQzDr4mdr5Sr+alL2M/e7qhfV5q8UsulYcvXoOw8y/fCb7b2RsKeTa8Lj/LQKfoNWbwyg30kgeOcFH3fJ/PFjkkGGy4lkHx63CfUMGi2Zx82ropdC711D89e1PslkrS0ncLh5KmS1KXqP0rgFgUVj/TxUNHq24HFZA9Qt9zg6kNFLtBcw+jC4O1iy1zE+TpgKMQf36Y83KFarsAxO9ut+D999ZKGuGKtiLykV6LwbHz8nFNWAHkRVR8E2ejoFmHR15h520fqHP2WG+DqxsowYXrlQm1P6NewcPwbrVeRJspJOVuOzi67By+R5l8TgWa2LcHs0yvmTFEVJSLiboPBiEfPu/fyKwFEiKqypiOyzDNMAsI1kqCQ/DFHw/idtvug3vv8OlmjT27duOVqd1RH4htzCoRQbXZqWeiSKz8qjWJAMx+tORIM+qLqj92rcB3T85WAebPWFaDlhSHBf7lpaSU687emJQ34FIxYvx9sxpGHz/o6jzgTT3n7hquYUAyJaynH4UKARIA/a/YNULqGaUMLqhVpFxyhQdGVgqpTBhWbV48dmXMPqhR9C8aQw/vKQDthSUyD69uO/Dkv9My5U/EeLfSTOZz8bvol0RHdnVCG0i68n+Jq/++2vW14J1GNZooLlOReslv0gjIn77TlTIjXZiqfUmrpux1KSW7yXdy+mrAXBRP9+m8/ovB0uz8ps/a+5qbVD3jO6Ip4ry/hON1UTmAqiffQvn/wAYRiK3DHxjsgAAAABJRU5ErkJggg==)
..........(6)
Where,
PC= Protein content.
a= Weight of protein.
b= Sample weight.
The observations from the harvest yields included, the number of cobs/clumps shortly after harvesting, before and after the corn kernels were peeled off and dried using an oven for ± 15 hours at 70
oC until the water content reaches ± 17%. While other observations included, the seed weight/cob (g), Weight of 1000 seeds (g). The production of ton ha-1 using equation (7) was formulated as follows:
Where,
B= Weight of seeds/clumps.
y= Volume/ha (cm
2 ).
n= Volume of spacing (cm
2).
The data collected were analyzed using variance analysis. The results showed significant differences with the Duncan Multiple Range Test (DMRT) at 95% and were also used in determining the best treatment from others.