Economic Analysis of Spatial‐Temporal Patterns in Corn and Soybean Response to Nitrogen and Phosphorus

Abstract

Interactions among environmental factors, management decisions, and field characteristics cause temporal and spatial variability in corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] yields. The objectives of this paper are (i) to test whether yield response of corn to N and P and of soybean to P are spatially and temporally stable, and (ii) to evaluate the profitability of a variable rate (VR) N and P fertility management strategy over a 5-yr, corn-soybean rotation using this response information. A field near Windom, MN, USA, was cropped with corn (1997, 1999, and 2001) and soybean (1998, 2000). Three replications of 13 N and P treatments were established in a split-plot arrangement of a randomized complete block design. Treatments were applied at constant rates in strips across the entire field. Fertilizer N treatments were 0, 67, 112, 157, and 202 kg ha 21 and P treatments were 0, 56, and 112 kg P 2 O 5 ha 21. The field was partitioned into sub-blocks for spatial analysis of yield response. Corn and soybean response to these inputs was estimated for each block, each year. Results indicate that spatial variation of crop response to these inputs is significant, and that response of corn and soybean to P is temporally stable in some parts of the field, but not others. Response to N was not temporally stable. Results of an ex post profitability analysis found that average returns over the 5-yr period from the VR N and P management strategy were $28 ha 21 higher than returns from a uniform application strategy.

A Review of Methods to Improve Nitrogen Use Efficiency in Agriculture

“…Soil mapping units [41], remote sensing [40], topography [42], yield maps, and soil EC [37] have been used successfully to delineate the MZ. Most of the delineation of MZ depends upon the sources that are static and less consistent because of the temporal variation in yield potential [43,44]. Therefore, they might not be adequate alone to account for all of the variability of N requirement in a field.…”

Life-cycle evaluation of nitrogen-use in rice-farming systems: implications for economically-optimal nitrogen rates

“…Economic effects of N fertilizer rates on crop production have been documented by many studies (Lambert et al, 2006;Zhu, 2006;Hernandez and Mulla, 2008). Due to the response of rice yields and environmental impacts to NARs, researchers have been trying to determine regional EONR to maximize profit (Roelcke et al, 2004;Zhu, 2006).…”

Responsive in-season nitrogen management for cereals

“…Another major factor contributing to low NUE in current strategies has been uniform application rates of fertilizer N to spatially variable landscapes, even though numerous field studies have indicated economic and environmental justification for spatially variable N applications in many agricultural landscapes (Mamo et al, 2003;Hurley et al, 2004;Koch et al, 2004;Scharf et al, 2005;Shahandeh et al, 2005;Lambert et al, 2006;Hong et al, 2007). Uniform applications within fields discount the fact that N supplies from the soil, crop N uptake, and response to N are not the same spatially (Inman et al, 2005).…”

“…Soil map units (Wibawa et al, 1993), topography (Kravchenko et al, 2000), remote sensing (Schepers et al, 2004), electrical conductivity sensors (Kitchen et al, 2003;Heiniger et al, 2003;Johnson et al, 2003), crop yield (Flowers et al, 2005;Kitchen et al, 2005) and producer experience (Fleming et al, 2004) have all been used with varying success to delineate MZ. While these data sources for MZ delineation can be used to consistently characterize spatial variation in soil physical and chemical properties that partially affect crop yield potential, they are less consistent in characterizing spatial variation in crop N requirements because of the apparent effect of temporal variation on expression of yield potential (Schepers et al, 2004;Lambert et al, 2006). Therefore, the soil-based MZ concept alone will not be adequate for improving variable application of crop inputs like N, primarily because it does not address weather-mediated variability in crop N demand.…”