Evaluation of Zone Soil Sampling Approaches for Phosphorus and Potassium Based on Corn and Soybean Response to Fertilization

Self Cite

Successful crop management requires understanding relationships between site characteristics and crop yield. We studied intercorrelations among soil and crop properties using factor analysis (FA) and principal components analysis (PCA), and their relationships with soybean [Glycine max (L.) Merr.] within-fi eld yield variability. Site variables (22) measured on 0.2-ha cells of 12-to 20-ha areas of fi ve Iowa fi elds were: elevation; soil texture; extractable nutrients; incidence of soybean cyst nematode (Heterodera glycines) (SCN), diseases, and weeds; soybean dry weight (DW), height, and P and K uptake at V5; plant height at R5; and grain yield. Agronomic interpretations of interrelationships among site variables were more straightforward for FA than for PCA. Th e factors conditions for early growth and nutrient uptake and intrinsic soil properties were present in all fi elds, plant P and K availability was present in three fi elds, and the factor soybean pests, weeds or plant growth was present in the other fi elds. Factor analysis and PCA accounted for 62 to 64% of the yield variability in the fi eld with the largest yield CV (30%) and 5 to 35% in the other fi elds (CV 2.8 to 5.9%). Two factors related signifi cantly to yield in two fi elds (plant P and K availability and intrinsic soil properties) while others were specifi c to one fi eld. Factor analysis identifi ed groups of interrelated site variables, showed how they accounted for yield variability, and showed that single measurements seldom account for most yield variation in a fi eld.

Section: Resultsmentioning

confidence: 91%

Variability of Soil Properties, Early Phosphorus and Potassium Uptake, and Incidence of Pests and Weeds in Relation to Soybean Grain Yield

Sawchik

Mallarino

2008

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“…Farmers and researchers know that different landscape positions can respond differently to management practices (Beehler et al, 2017;Cambardella et al, 1994). Zonal soil and crop management can be used to improve soil resource utilization by providing zone-specific information that might be lost if aggregated across an entire field (Jaynes et al, 2010;Mzuku et al, 2005;Sawchik and Mallarino, 2006). Research must continue into zone-specific soil health management as well.…”

Section: Soil Carbon Results From Four Perspectivesmentioning

confidence: 99%

Measuring Rotation and Manure Effects in an Iowa Farm Soil Health Assessment

Karlen

Obrycki

2019

Data from on-farm sites with known management histories are needed to quantify soil biological, chemical, and physical properties influencing carbon stocks and soil health. Surface (0-15 cm) and deep core (0-122 cm) soil samples were collected from fields under two rotations in Boone County, IA. The first was a 5-yr corn [Zea mays (L.)], soybean [Glycine max (L.) Merr.], corn, oat [Avena sativa (L.)], and alfalfa [Medicago sativa (L.)] rotation to which 0, 18, or 36 Mg ha-1 of a manure/ biosolids mixture was applied prior to planting corn. The second was an 8-yr rotation with 6 yr of mixed grass and legume pasture followed by corn and an oat crop within which the pasture mixture was reestablished. Soil samples were collected evenly across the predominant soil map units (SMUs). Bulk density (BD), soil organic C (SOC), water-stable aggregates (WSA), microbial biomass carbon (MBC), pH, Mehlich-3 and diethylenetriaminepentaacetic acid (DTPA) extractable nutrients, electrical conductivity (EC), and nitrogen (total-, NH 4-, and NO 3-N) were measured. Surface SOC data were consistent with Soil Survey values for the various SMUs. Crop rotation effects were more noticeable than manure/biosolid application rate effects. Data from this study were combined with previously published SOC data in Iowa. Results suggest extended rotation systems or those with cover crops may increase SOC 8 ± 4 g kg-1 compared to corn-soybean rotations (33 vs. 25 g kg-1). This study provides on-farm reference values for soil health assessment tools and draws attention to the importance of inherent soil properties for these assessments.

“…Many layers have been used for stratification, including farmer observations (Taylor et al, 2007), electrical conductivity measurements (Peralta & Costa, 2013), previous years' yields (Flowers, Weisz, & White, 2005), and remotely sensed spectral measurements (Song et al, 2009). A full review of MZ literature is out of the scope of this paper, yet we note that the results for performance of MZs are mixed depending on the data layers used, agronomic history, and biophysical setting (Flowers et al, 2005;Mzuku et al, 2005;Sawchik & Mallarino, 2007;Schepers et al, 2004;Vasu et al, 2017).…”

Section: Design-based Approachmentioning

confidence: 99%

Guiding soil sampling strategies using classical and spatial statistics: A review

Lawrence¹,

Roper

Morris

et al. 2020

Soil analysis is a key practice to increase the efficiency of nutrient management in agriculture. Since the early 20th century, increasingly sophisticated methods have been developed to describe and manipulate the inherent spatial variability in soil chemical properties within the realms of classical and spatial statistics. In this paper, we reviewed design‐based (classical) and model‐based (geostatistical) sampling to suggest field‐scale sampling strategies consistent with common agronomic management goals in annual crop production systems. To assess the relevance of common sampling methods in relation to practice, current extension recommendations across the United States were compared with results from peer‐reviewed literature. Despite decades of research, specific recommendations for sample sizes, sampling depths, numbers of soil cores, and layouts were highly variable for classical and geostatistical approaches. Mobile nutrients, such as NO3, are frequently lacking in spatial structure and rarely are recommended for site‐specific management. Nonmobile nutrients, such as P, are more spatially dependent and exhibit nested spatial structures that are inconsistent across fields. For these reasons, we recommend design‐based sampling in most situations for simplicity, cost, and objectivity. The common design‐based sampling protocol prescribes collection of individual cores in a zig‐zag pattern that are combined to produce a composite sample. This protocol should be amended because it is not sufficiently randomized and is inadequate for log‐normally distributed variables. To facilitate site‐specific management, we recommend structured approaches for delineating management zones or strata and for researchers to systematically enumerate confounding variables while explicitly defining the scope of inference for future soil sampling studies.