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One of the most critical aspects of agricultural experimentation is the proper choice of experimental design to control field heterogeneity, especially for large experiments. However, even with complex experimental designs, spatial variability may not be properly controlled if it occurs at scales smaller than blocks. Therefore, modeling spatial variability can be beneficial, and some studies even propose spatial modeling instead of experimental design. Our goal was to evaluate the effects of experimental design, spatial modeling, and a combination of both under real field conditions using GIS and simulating experiments. Yield data from cultivars was simulated using real spatial variability from a large uniformity trial of 100 independent locations and different sizes of experiments for four experimental designs: completely randomized design (CRD), randomized complete block design (RCBD), α‐lattice incomplete block design (ALPHA), and partially replicated design (PREP). Each realization was analyzed using different levels of spatial correction. Models were compared by precision, accuracy, and the recovery of superior genotypes. For moderate and large experiment sizes, ALPHA was the best experimental design in terms of precision and accuracy. In most situations, models that included spatial correlation were better than models with no spatial correlation, but they did not outperformed better experimental designs. Therefore, spatial modeling is not a substitute for good experimental design.

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A better understanding of interactions between soil management and landscape variability and their effects on cotton (Gossypium hirsutum L.) productivity is needed for precision management. We assessed management practices and landscape variability effects on seed cotton yield in a 9‐ha, Alabama field (Typic and Aquic Paleudults) during 2001–2003. We hypothesize that landscapes have major effects on cotton productivity, but these effects vary based on management and climate. Treatments were established in replicated strips traversing the landscape in a corn (Zea mays L.)–cotton rotation. Treatments included a conventional system with or without 10 Mg ha−1yr−1 dairy manure (CTmanure or CT), and a conservation system with and without manure (NTmanure or NT). Conventional systems consisted of chisel plowing/disking + in‐row subsoiling without cover crops. Conservation systems combined no surface tillage with in‐row subsoiling and winter cover crops. A soil survey, topographic survey, and interpolated surfaces of soil electrical conductivity (EC), soil organic carbon (SOC), and surface soil texture were used to delineate five zones using fuzzy k‐means clustering. Overall (2001–2003), conservation systems improved cotton yield compared with conventional systems (2710 vs. 2380 kg ha−1); neither manure nor treatment × year interactions were significant. The conservation system was more productive than the conventional system in 87% of the cluster × year combinations. Slope, EC, SOC, and clay content were correlated with yield in all treatments. Soil and terrain attributes explained 16 to 64% of yield variation, however, their significance fluctuated between years and treatments. In dry years, factor analyses suggested variables related with soil quality and field‐scale water dynamics had greater impacts on CT yields than NT yields. Our results indicate that management zones developed using relatively static soil‐landscape data are relatively more suitable for conservation systems, and these zones are affected by soil management. In addition, the impact of NT on yields is most apparent on degraded soils in dry years.

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