Produção de milho adubado residualmente com composto de lodo de esgoto e fosfato de Gafsa

Objetivou-se, com este trabalho, avaliar o efeito residual da adubação com composto de lodo de esgoto e fosfato natural de Gafsa sobre os teores de nutrientes no solo, na planta e na produtividade do milho. O trabalho foi realizado em Cambissolo Háplico. Os tratamentos, em esquema fatorial 2 x 4, corresponderam a 2 doses de fosfato de Gafsa (0 e 90 kg ha-1 de P2O5) e 4 doses de composto de lodo de esgoto (0; 25; 50 e 75 t ha-1, em base seca). O delineamento experimental utilizado foi em blocos casualizados, com 3 repetições. Em geral, a produtividade e os teores de nutrientes no solo e na planta no segundo cultivo sucessivo de milho não foram influenciados pela adubação com fosfato natural reativo. Entretanto, a produtividade de milho e os teores de nutrientes no solo e nas folhas de milho aumentaram com a dose de composto de lodo de esgoto ao solo, sendo a dose de 75 Mg ha-1, a de maior efeito residual. A produtividade do milho é menor no segundo cultivo sucessivo em razão do empobrecimento do solo em fósforo e potássio, recomendando-se novas adubações com lodo de esgoto, a cada cultivo realizado.

STOCHASTIC MODEL FOR SIMULATING MAIZE YIELD

Maize is one of the most important crops in the world. The products generated from this crop are largely used in the starch industry, the animal and human nutrition sector, and biomass energy production and refineries. For these reasons, there is much interest in figuring the potential grain yield of maize genotypes in relation to the environment in which they will be grown, as the productivity directly affects agribusiness or farm profitability. Questions like these can be investigated with ecophysiological crop models, which can be organized according to different philosophies and structures. The main objective of this work is to conceptualize a stochastic model for predicting maize grain yield and productivity under different conditions of water supply while considering the uncertainties of daily climate data. Therefore, one focus is to explain the model construction in detail, and the other is to present some results in light of the philosophy adopted. A deterministic model was built as the basis for the stochastic model. The former performed well in terms of the curve shape of the above-ground dry matter over time as well as the grain yield under full and moderate water deficit conditions. Through the use of a triangular distribution for the harvest index and a bivariate normal distribution of the averaged daily solar radiation and air temperature, the stochastic model satisfactorily simulated grain productivity, i.e., it was found that 10,604 kg ha(-1) is the most likely grain productivity, very similar to the productivity simulated by the deterministic model and for the real conditions based on a field experiment.