yield data collected over many years in the same field and a larger set of measured soil and terrain variables Farmers will be better able to implement site-specific management would have a better chance of accomplishing this goal. practices when they understand the causes of spatial and temporal variability of corn (Zea mays L.) and soybean [Glycine max (L.) Data collection and analysis, however, are costly and Merr.] yield in their fields. Our objectives were to determine if a data labor-intensive, and questions remain about the kind set containing 20 soil and terrain variables could explain spatial yield and amount of data that are needed to adequately unvariability better than a subset of seven more easily measured variables derstand spatial yield patterns. Additionally, it is unclear and to determine whether the relative importance of factors in exwhether different variables should be measured for soyplaining yield variability differed between corn and soybean or bebean and corn or for wet and dry years. tween wet and dry years. Yield data were collected for 11 yr in a 16-ha Growing season precipitation often interacts with terfield in central Iowa. Soil and terrain variables measured included: A rain attributes and soil properties to influence crop yield horizon depth, carbonate depth, pH, coarse sand, sand, silt, clay, (Timlin et al., 1998; Jaynes et al., 2002; Kaspar et al., organic C, N, Fe, K, P, and Zn; and seven easily measured variables: 2003). In years with below average rainfall, field areas electrical conductivity, soil color, elevation, slope, profile curvature,higher on a hillslope with greater slopes and convex plan curvature, and depression depth. Factor analysis of the variables followed by regression of yield on the resulting factors showed that curvatures usually have less available water and lower the 20-variable set explained more of the spatial variation in yield than yield than areas lower on the hillslope, with lesser slopes the subset of seven variables. Further, the analysis of the 20-variable and concave curvatures (Ciha, 1984; Halvorson and data set showed that soybean yield was affected more by pH, more Doll, 1991; Afyuni et al., 1993;Timlin et al., 1998; Jaynes by closed depressions in wet years, and less by curvature in dry years Kaspar et al., 2003). Similarly, eroded soils, than corn yield. Similarly, yield was negatively affected by closed which commonly have substantial slopes, convex curvadepressions and lower landscape positions in wet years, whereas these tures, and shallow topsoils (Pennock and de Jong, 1987; factors had either no effect or a positive effect in dry years. Alternately, Lindstrom et al., 1992), show a greater yield decline curvature had a negative effect in dry years and no effect in wet years.
Introduction Materials and Methods Results and Discussion 29 Conclusion References Cited CHAPTER 3. RELATIONSHIP OF SOIL CHEMICAL, PHYSICAL, AND MORPHOLOGICAL PROPERTIES ON CORN YIELD SPATIAL VARIABILITY
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.