Soil Physical Properties Spatial Variability under Long-Term No-Tillage Corn

Abstract

Spatial variability of soil physical and hydrological properties within or among agricultural fields could be intrinsically induced due to geologic and pedologic soil forming factors, but some of the variability may be induced by anthropogenic activities such as tillage practices. No-tillage has been gaining ground as a successful conservation practice, and quantifying spatial variability of soil physical properties induced by no-tillage practices is a prerequisite for making appropriate site-specific agricultural management decisions and/or reformulating some management practices. In particular, there remains very limited information on the spatial variability of soil physical properties under long-term no-tillage corn and tropical soil conditions. Therefore, the main objective of this study was to quantify the spatial variability of some selected soil physical properties (soil surface temperature (ST), volumetric water content (θv), soil resistance (TIP), total porosity (θt), bulk density (ρb), organic carbon, and saturated hydraulic conductivity (Ksat)) using classical and geostatistical methods. The study site was a 2 ha field cropped no-tillage sweet corn for nearly 10 years on Oahu, Hawaii. The field was divided into 10 × 10 and 20 × 20 m grids. Soil samples were collected at each grid for measuring ρb, θt, and soil organic carbon (SOC) in the laboratory following standard methods. Saturated hydraulic conductivity, TIP at 10 and 20 cm depths, soil surface temperature, and θv were also measured. Porosity and ρb have low and low to moderate variability, respectively based on the relative ranking of the magnitude of variability drawn from the coefficient of variation. Variability of the SOC, TIP, and Ksat ranges from moderate to high. Based on the best-fitted semivariogram model for finer grid data, 9.8 m and 142.2 m are the cut off beyond which the measured parameter does not show any spatial correlation for SOC, and TIP at 10 cm depth, respectively. Bulk density shows the highest spatial dependence (range = 226.8 m) among all measured properties. Spatial distribution of the soil properties based on kriging shows a high level of variability even though the sampled field is relatively small.

Forecasting potato tuber yield using a soil electromagnetic induction method

“…This field also had the highest standard deviation (SD) for mean yield and mean ECa. The variability in ECa and potato tuber yield could be due to intrinsic and extrinsic sources, including the weather, natural variation in soil (Awal et al, 2019) and crop management practices across the fields (Cemek, GüLer, Kiliç, Demir, & Arslan, 2007). These results also reflected the effect of temporal dynamics on the measured parameters due to sampling of ECa at different times during the study period (Farooque et al, 2012).…”

“…The sustainable use of soils depends on the management tools available to farmers (Keesstra et al, 2016;Lotter, Seidel, & Liebhardt, 2003;Pradhan, Fischer, Van Velthuizen, Reusser, & Kropp, 2015). This is possible through adopting precision agriculture practices that allow for site-specific management of crop fields with modern technologies capable of assessing soil variability and monitoring crop yield (Adamchuk, Hummel, Morgan, & Upadhyaya, 2004;Awal et al, 2019;Neupane & Guo, 2019;Perron et al, 2018). By contrast, conventional farming practices may limit crop yield below its potential values, compromise product quality, cause environmental degradation and reduce farm income through excessive applications and losses of agrochemicals (Corwin & Lesch, 2010).…”

Effect of Experimental Blocking on the Suppression of Spatial Dependence Potentially Attributable to Physicochemical Properties of Soils

“…MI has undoubtedly proven to be an appropriate measure to assess the spatial dependence of soil properties [30,31]. Before a spatial inferential analysis, it has become routine to do an exploratory analysis of the variables involved in the modeling process; this usually involves calculating the MI but using the response variable as descriptive statistics prior to spatial modeling or a spatial interpolation process [32]. However, in our research, we found that the application of MI on a response variable in an exploratory way evidencing spatial dependence did not guarantee that this was precisely the result when this variable was incorporated into the modeling, because precisely the effect of the blocking can remove such dependency, something that was not observed before modeling where it had not been blocked.…”

Evaluación geoestadística de atributos hidrofísicos del suelo en la granja Tunguavita, Paipa, Colombia

“…La Dr, el co y la pt tuvieron un rango mayor a 75 m y un grado de dependencia espacial (gde) de cero (tabla 3) clasificado como débil acorde con Vidana, Biswas y Strachan [40], quienes clasificaron el gde como la relación entre el efecto pepita y la meseta expresado en porcentaje (<25 % débil; 25-75 % moderado; y >75 % fuerte), lo cual hace suponer que, hasta esa distancia, estas variables son espacialmente dependientes y poseen una baja relación entre los puntos de muestreo. Una situación similar de gde moderado fue reportada por Alvarado y su equipo y Guatibonza y sus colaboradores para la Dr [26], [27].…”