Abstract
ABSTRACT The aim of this study was the agroclimatic zoning of common bean in the Mato Grosso state in the second harvest. Data from 38 meteorological stations in the state and in neighboring regions were used. The zoning was based on water requirement satisfaction index (WRSI) for the common bean crop, for the three levels of available water capacity of the soils of the state (30, 50 and 75 mm) in 12 sowing periods. After generating the indexes for the municipalities, the variograms of the data were fitted in order to enable interpolation of the data for the state. Data were entered into ArcGISTM 10.0 and the ordinary kriging interpolation method was used. After generating the maps, they were clipped to the Mato Grosso State and classified as the following WRSI classes: suitable (WRSI ≥ 0.65); restricted (0.55 < WRSI < 0.65) and unsuitable (WRSI ≤ 0.55) for the stage of flowering and grain filling. It was possible to interpolate only the ten-day periods 8 to 12, because from 1 to 7 all regions of the state are suitable for cultivation. The trend of the aptitude of sowing dates is similar to the movement of the air masses active in the state, with a northwest-southeast direction of displacement.
Highlights
Located in the Midwest region of Brazil, Mato Grosso has an area of 903,357.91 km2 distributed into 141 municipalities (SEPLAN, 2011)
The objective of this study was to perform the zoning of the sowing periods of second-harvest common bean in Mato Grosso, based on the Water Requirement Satisfaction Index (WRSI) and on the available water capacity
The spatial distribution of WRSI for Mato Grosso is very similar for the three Available Water Capacity (AWC) (Figures 1, 2 and 3)
Summary
Located in the Midwest region of Brazil, Mato Grosso has an area of 903,357.91 km distributed into 141 municipalities (SEPLAN, 2011). Common bean (Phaseolus vulgaris L.), object of this study, due to its cultivation period (second harvest), as well as maize and rice, has its risk maximized because of the cultivation close to the end of the rainy period (Andrade et al, 2006) In this context, geostatistics and simulation models are used to predict variables and create scenarios, allowing to generate zonings, indicating the best sowing periods with lower risks to the crop. Geostatistics and simulation models are used to predict variables and create scenarios, allowing to generate zonings, indicating the best sowing periods with lower risks to the crop In this context, spatialization and interpolation of meteorological data, which have the zoning as the product, are fundamental in a state with the dimensions of Mato Grosso. Farias et al (2001) characterized the occurrence of water deficit for the soybean crop in producing regions of Brazil, based on the water balance and on the Water Requirement Satisfaction Index (WRSI) with the aid of geostatistics
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