No abstract

Effectiveness of precision agriculture depends on accurate and efficient mapping of soil properties. Among the factors that most affect soil property mapping are the number of soil samples, the distance between sampling locations, and the choice of interpolation procedures. The objective of this study is to evaluate the effect of data variability and the strength of spatial correlation in the data on the performance of (i) grid soil sampling of different sampling density and (ii) two interpolation procedures, ordinary point kriging and optimal inverse distance weighting (IDW). Soil properties with coefficients of variation (CV) ranging from 12 to 67% were sampled in a 20‐ha field using a regular grid with a 30‐m distance between grid points. Data sets with different spatial structures were simulated based on the soil sample data using a simulated annealing procedure. The strength of simulated spatial structures ranged from weak with nugget to sill (N/S) ratio of 0.6 to strong (N/S ratio of 0.1). The results indicated that regardless of CV values, soil properties with a strong spatial structure were mapped more accurately than those that had weak spatial structure. Kriging with known variogram parameters performed significantly better than the IDW for most of the studied cases (P < 0.01). However, when variogram parameters were determined from sample variograms, kriging was as accurate as the IDW only for sufficiently large data sets, but was less precise when a reliable sample variogram could not be obtained from the data.

Over the last 60 years, soil microbiologists have accumulated a wealth of experimental data showing that the bulk, macroscopic parameters (e.g., granulometry, pH, soil organic matter, and biomass contents) commonly used to characterize soils provide insufficient information to describe quantitatively the activity of soil microorganisms and some of its outcomes, like the emission of greenhouse gasses. Clearly, new, more appropriate macroscopic parameters are needed, which reflect better the spatial heterogeneity of soils at the microscale (i.e., the pore scale) that is commensurate with the habitat of many microorganisms. For a long time, spectroscopic and microscopic tools were lacking to quantify processes at that scale, but major technological advances over the last 15 years have made suitable equipment available to researchers. In this context, the objective of the present article is to review progress achieved to date in the significant research program that has ensued. This program can be rationalized as a sequence of steps, namely the quantification and modeling of the physical-, (bio)chemical-, and microbiological properties of soils, the integration of these different perspectives into a unified theory, its upscaling to the macroscopic scale, and, eventually, the development of new approaches to measure macroscopic soil characteristics. At this stage, significant progress has been achieved on the physical front, and to a lesser extent on the (bio)chemical one as well, both in terms of experiments and modeling. With regard to the microbial aspects, although a lot of work has been devoted to the modeling of bacterial and fungal activity in soils at the pore scale, the appropriateness of model assumptions cannot be readily assessed because of the scarcity of relevant experimental data. For significant progress to be made, it is crucial to make sure that research on the microbial components of soil systems does not keep lagging behind the work on the physical and (bio)chemical characteristics. Concerning the subsequent steps in the program, very little integration of the various disciplinary perspectives has occurred so far, and, as a result, researchers have not yet been able to tackle the scaling up to the macroscopic level. Many challenges, some of them daunting, remain on the path ahead. Fortunately, a number of these challenges may be resolved by brand new measuring equipment that will become commercially available in the very near future.

Analysis of yield variability is an important issue in agricultural research, and topographical land features are among the most important yield‐affecting factors. The objective of this study was to determine how useful topographical information can be, alone or together with selected soil properties, for explaining yield variability on a field scale. Yield–topography–soil relationships were analyzed using dense corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] yield data collected from 1994 to 1997, a detailed terrain map, and relatively densely sampled soil organic matter (OM) content, cation exchange capacity (CEC), and P and K soil test concentrations from eight fields in central Illinois and eastern Indiana. Soils of the Illinois fields were classified as Haplaquolls and Argiudolls; soils of the Indiana fields were classified as Hapludalfs. Topographical land features used in the study included elevation, measured with survey grid global positioning system (GPS) and land‐based laser, and slope, curvature, and flow accumulation, derived from elevation data. Soil properties explained about 30% of yield variability (from 5 to 71% for different fields), with OM content influencing yield the most. The cumulative effect of the topographical features explained about 20% of the yield variability (6–54%). Elevation had the most influence on yield, with higher yields consistently observed at lower landscape positions. Curvature, slope, and flow accumulation significantly affected yield only in certain conditions, such as extreme topographical locations (undrained depressions or eroded hilltops) combined with very high or low precipitation. Soil properties and topography explained about 40% of yield variability (10–78%).