Crop Modeling in Maize: An Online Tool to Improve Resources Management of Argentinean Farmers.

In maize (Zea mays L.), like other crops, agronomic management of resources (i.e., hybrid selection, nitrogen rate, plant density) generally depends on grain yield expected by farmers before sowing date. In many cases, yield expectation is estimated by mean of maize productivity achieved in previous seasons. However, this approach has certain limitations because it does not consider (i) possible yield reductions that could be attributable to previous agronomic practices (eg, poor control of weeds, low-potential hybrid, poor nutrition), and (ii ) interannual variation of weather conditions. Crop simulation models are a well-known approach for providing estimations of expected yield and its variation among years. We develop an online platform to provide crop modeling results for Argentinean’s farmers to be use in their agronomic management plans. Crop simulations were done by mean of Ceres-Maize 4.5 and include a combination of (i) 15 regions with different whether conditions, (ii) 2 sowing date (early and late for mentioned regions), (iii) 2-3 soil type representative of each regions, (iv) 4 levels of useful water content at sowing (≈0%, 30%, 65%, 100%). This tool is intended to (i) achieve high input-use efficiency, and (ii) evidence the gaps between potential and actual farmers’ yields.