Analysis of the spatial variability of crop yield and soil properties in small agricultural plots

Abstract

The objective of this study was to assess spatial variability of soil properties and crop yield under no tillage as a function of time, in two soil/climate conditions in São Paulo State, Brazil. The two sites measured approximately one hectare each and were cultivated with crop sequences which included corn, soybean, cotton, oats, black oats, wheat, rye, rice and green manure. Soil fertility, soil physical properties and crop yield were measured in a 10-m grid. The soils were a Dusky Red Latossol (Oxisol) and a Red Yellow Latossol (Ultisol). Soil sampling was performed in each field every two years after harvesting of the summer crop. Crop yield was measured at the end of each crop cycle, in 2 x 2.5 m sub plots. Data were analysed using semivariogram analysis and kriging interpolation for contour map generation. Yield maps were constructed in order to visually compare the variability of yields, the variability of the yield components and related soil properties. The results show that the factors affecting the variability of crop yield varies from one crop to another. The changes in yield from one year to another suggest that the causes of variability may change with time. The changes with time for the cross semivariogram between phosphorus in leaves and soybean yield is another evidence of this result.

A crop model-based approach for sunflower yields

“…The spatial distribution of sunflower water-and nitrogen-limited yield was assessed based on the average simulations over 31 years (1979 -2009) for all selected locations in the northern region of Minas Gerais (Figure 1). Sunflower yield variability and the Thornthwaite (1948) climatological classification were then mapped using the ordinary 'kriging' method in ArcGIS 10, as described by Lu and Fan (2013); Pringle et al (2004);and Vieira and Gonzalez (2003).…”

Zonas homogêneas de atributos do solo para o manejo de irrigação em pomar de videira

“…A utilização da técnica de krigagem utiliza a dependência espacial entre amostras vizinhas, expressa no variograma para estimar valores em qualquer posição dentro do campo (Vieira, 2000). Para a construção dos mapas de isolinhas (zonas homogêneas) dos atributos avaliados, foi utilizado o programa Surfer 7.0 (Golden Software, 1999), o qual usou para tanto os valores estimados por meio da técnica de krigagem para os locais não amostrados (Vieira & Paz González, 2003). O índice de dependência espacial (IDE) dos atributos, que é dado por [C/(C0 + C)] × 100 %, foi determinado e classificado, segundo Zimback (2001), como baixo (IDE < 25 %), moderado (25 % < IDE < 75 %) e forte (IDE > 75 %).…”

Spatial and temporal variability of crop yield and some Rhodic Hapludox properties under no-tillage

“…Analyzing soil chemical properties and yield in maize, Ortega (1997) observed that the best maize yields were obtained in areas with high levels of organic matter and low pH and calcium carbonate values. Vieira & Gonzalez (2003) studied spatial variations of different crop yields in an Ultisol and observed that crop yield variations between one year and the following suggest that the causes of variability can change over time.…”