Spatial Variability of Irrigated Corn Yield in Relation to Field Topography and Soil Chemical Characteristics

Abstract

Abstract. Corn yield, topography and soil characteristics were sampled on a 26 ha area of a centre pivot irrigated cropland. The aim of the study was to determine relationships between corn yield, field topography and soil characteristics. The study was carried out in the Alentejo region of Portugal. Corn yield was measured with a combine harvester fitted with a grain-flow sensor and positioned by means of the Global Positioning System (GPS). A grid-based digital elevation model (DEM) with 1-m resolution was constructed and several topographic attributes were calculated from the DEM: the local slope gradient (S), profile curvature (Curv), specific catchments area (SCa), and a steady-state wetness index (W). Yield and topographical attributes were computed for areas of radius 5, 10, 25 and 50 m, being considered its maximum, minimum, range and average values. The soil was systematically sampled with a mechanical probe for a total of 109 soil profiles used for analysis of the following soil superficial (<0.30 m) characteristics: extractable phosphorous (P 2 O 5 ) and extractable potassium (K 2 O), soil pH, cation exchange capacity (CEC) and exchangeable bases. With centre pivot irrigation systems, the Wave 50 index was shown to be useful for the identification of field areas in which low corn yields may be due to lack of water. At the same time, SCa was found to be useful for the identification of field areas in which low yields are due to excess water and drainage problems. Higher positive correlation between pH, Ca and Curv were observed; calcium concentration was found on the transition areas between flat surfaces to concave ones, while lower values were detected in convex and concave areas. Topographical indexes, namely Wave 50 , SCa and Curv, can be especially helpful in site-specific management for delineating areas where crop yields are more sensitive to extreme water conditions.

Spatial relationships between soil attributes and corn yield in no-tillage system

“…thus, the semivariograms explained most of the variation in experimental data. the spherical model was fitted for corn yield, which agreed with the result obtained by Silva & alexandre (2005). a strong spatial dependence was observed for corn yield (table 4).…”

“…Montezano et al (2006) observed a positive correlation between corn yield and clay, oM and boron content, and a negative correlation for the sand, copper, manganese and zinc content in a Rhodic hapludox under no-tillage. Silva & alexandre (2005) found a positive correlation between corn yield and the CeC, Ca 2+ and K + contents in soils arising from calcareous deposits. the h + al content showed a negative correlation with corn yield, which was expected because higher concentrations of h + al content increase soil acidity.…”

“…Silva & alexandre (2005) and Montezano et al (2006) found that the highest r-values did not exceed 0.39. the stepwise multiple regression analysis revealed that approximately 65 % of variation in corn yield can be explained by soil properties, where V accounted for the greatest part, which explained 44 % of the variation in corn yield (table 3). these results revealed that more than half of the variation in crop yield can be explained by soil properties, which is a valid result because crop yield depends on several factors such as germination rates, weeds, insects, diseases and climate.…”

“…Kitamura et al (2007) reported low correlation coefficients between the texture of a Rhodic hapludox under no-tillage and bean yield, where 7.3 % of bean yield was explained by the clay content in the surface soil layer. in a study of Silva & alexandre (2005), a positive correlation was observed between corn yield and cation exchange capacity (CeC), and Ca 2+ and K + contents in soils developed from calcareous deposits. in addition, Montezano et al (2006) observed positive linear correlation between corn yield and clay, organic matter and boron content, while corn yield was negatively correlated with sand, copper, manganese and zinc content in a Rhodic hapludox under no-tillage.…”

Multi-time scale analysis of sugarcane within-field variability: improved crop diagnosis using satellite time series?

“…These potential yield variations can be directly linked to the topography and consequently to the water conditions of the plant, with limited water stress during the dry year in the lower part of the field and water logging during the rainy year. The influence of the topography was previously shown by Marques Da Silva and Alexandre (2005), who proposed topographical indices to help in sitespecific management for delineating areas where crop yields are more sensitive to extreme water conditions. Our results confirmed the observations obtained by yield monitoring; Eghball and Varvel (1997) and Lamb et al (1997) showed that, under rainfed conditions, the temporal variability of yields was more dominant than spatial variability, indicating that spatial patterns in grain yields were greatly affected by yearly climatic conditions, particularly by year-to-year changes in seasonal water supply.…”