HIGHLIGHTS:
Crop model had a less than 16% bias when comparing simulated and observed maize yield under rainfed and irrigated conditions.
The best sowing date varied by site when considered yield gain and irrigation demand.
Maize yield can be increased three-fold with irrigation of 342 to 565 mm per cycle.
ABSTRACT
The state of Rio Grande do Sul, Brazil, has a low maize production when compared to the total demand, particularly under water deficit conditions. This study aimed to estimate the yield gain of maize using irrigation. The FAO Agroecological zone model was used to simulate the yield after previous calibration and evaluation, following an experimental design of randomized blocks, with 40 growing seasons as replicates and 20 sites. Two water management (rainfall and irrigation), three sowing dates (Aug 15, Sept 15, and Oct 15), and three soil textures (sandy, sand-clayey, and clayey) were evaluated. The generic hybrid obtained from calibration based on multiple hybrids with a medium cycle of 150 d was utilized for the simulation. The model evaluation showed an absolute bias of 16% and an overestimated yield of 2%. The mean irrigated and rainfed yields were, respectively, 16,094 and 5,386 kg ha-1. The irrigated yield had statistically superior values for the sowing dates Sep 15 and Oct 15, although it required a greater amount of irrigation. The yield gain reached a maximum value of 56% in the site of São Gabriel, with irrigation amount increasing 14% on the sowing date Oct 15 compared to that of Aug 15. The soil types showed statistical differences for rainfed conditions, and irrigation minimized the differences, while no statistically significant differences were found for the yield. Irrigation showed potential to increase the maize supply, and the response across sites can be considered in the agricultural management plan.
Key words:
Zea mays; water deficit; sowing dates; crop model; agriculture plan
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ns, * and **: Not significant, significant at p ≤ 0.05 and at 0.01 by F test; The bars in the points indicate de standard deviation to growing seasons analized