Corn Yield Variability Research Articles

Hillslope Variability in Corn Response to Nitrogen Linked to In‐Season Soil Moisture Redistribution

Spatial variability of corn (Zea mays L.) yield within a field is often identified as the primary criterion to justify site‐specific nitrogen (N) management; yet, observed yield variability may be unrelated to N supply. The objective of this study was to characterize the spatial variability in economic optimum N rate (EONR) for corn. Ten plot locations were selected in 2005 along a 300‐m toposequence of a field in central Pennsylvania. At each location, two replications of six N treatments (0, 56, 112, 168, 224, and 280 kg N ha−1) were broadcast applied at planting as NH4NO3. Soil water content (0‐ to 90‐cm depth) was recorded approximately weekly at each location between 5 June and 2 September. The quadratic‐plateau response was selected as the most appropriate grain yield response function for 9 of 10 locations and for the field‐mean response. The EONR ranged from 47 to 188 kg N ha−1 among the nine locations, whereas EONR for the mean response was 137 kg N ha−1. At four of nine locations, observed EONR deviated from field‐mean EONR by 40 to 50 kg N ha−1. The relationship between EONR and the change in soil profile water content (0–90 cm) between 30 June and 25 July (representing the driest and wettest soil conditions early in the growing season) was the defining relationship in this study (r2 = 0.92; P > F < 0.0001). Successful site‐specific N management depends on an evaluation of the spatial variability in EONR and the corresponding causal factors.

Identifying important factors influencing corn yield and grain quality variability using artificial neural networks

Soil, landscape and hybrid factors are known to influence yield and quality of corn (Zea mays L.). This study employed artificial neural network (ANN) analysis to evaluate the relative importance of selected soil, landscape and seed hybrid factors on yield and grain quality in two Illinois, USA fields. About 7 to 13 important factors were identified that could explain from 61% to 99% of the observed yield or quality variability in the study site-years. Hybrid was found to be the most important factor overall for quality in both fields, and for yield as well in Field 1. The relative importance of soil and landscape factors for corn yield and quality and their relationships differed by hybrid and field. Cation exchange capacity (CEC) and relative elevation were consistently identified as among the top four most important soil and landscape factors for both corn yield and quality in both fields in 2000. Aspect and Zn were among the top five most important factors in Fields 1 and 2, respectively. Compound topographic index (CTI), profile curvature and tangential curvature were, in general, not important in the study site-years. The response curves generated by the ANN models were more informative than simple correlation coefficients or coefficients in multiple regression equations. We conclude that hybrid was more important than soil and landscape factors for consideration in precision crop management, especially when grain quality was a management objective.

Spatial Variability of Soil Properties, Corn Quality and Yield in Two Illinois, USA Fields: Implications for Precision Corn Management

Better understanding of within-field spatial variability of crop quality parameters and yield are needed for precision management of crops. This study was conducted to determine the magnitude of within-field variability in soil properties, corn (Zea mays L.) quality parameters and yield and to characterize their spatial structures. Another objective was to compare the effects of hybrid on corn quality, yield, and the spatial structure of grain quality. Four Pioneer hybrids were planted side-by-side, two in each of the two study fields in eastern Illinois, USA. Coefficients of variation (CV%) for soil properties varied from 6.3 (pH) to 56.8% (soil test P). All the soil properties (except pH at Site 2) displayed well-defined spatial structures, with either strong or moderate spatial dependence. Variability in corn quality and yield (CVs < 10%) was smaller than variability in soil properties. Most quality parameters examined at Site 1 exhibited either moderate or strong spatial dependence, except that corn oil (both hybrids), kernel roundness and weight (hybrid 33Y18) did not show any spatial correlation. Hybrid 33G26 had significantly higher yield and quality for most quality parameters than 33Y18 at Site 1. At Site 2, hybrid 34W67 was significantly lower in oil and protein content, length, roundness and vitreousness than 34K77, but higher in other quality parameters. Significant differences in spatial structures were also observed across hybrids for some corn quality parameters. We conclude that hybrid selection is an important strategy for precision management of corn for optimum yield and quality.

Between-Observer Differences in Relative Corn Yield vs. Rated Weed Control

Crop yield and weed control rating have been used to measure weed and crop response to weed management treatments, eliminate unacceptable weed management treatments, and select “best” treatments for recommendation to farmers. However, the mathematical relationship between crop yield and rated weed control has not been reported before from such treated screening experiments. Likewise, differences have not been reported before in rated weed control among experienced observers (i.e., reliability) when rating the same experiments and for an experienced observer over time (i.e., repeatability). Data from published experiments on zone herbicide application in field corn in which weeds reduced yield to various amounts were reanalyzed to examine these issues. For this study, relative corn yield was calculated as a percentage of the 1× broadcast herbicide rate for two observers and either three experimental site-years or their average. For observer A, relative corn yield (%) increased linearly as rated total weed control (%) increased for all 3 site-yr and their average. For observer B, equations were curvilinear in 2 of 3 site-yr. For both observers, equations accounted for little data variability in relative corn yield (r2= 0.25 and 0.25 in site-year 1, respectively, 0.38 and 0.36 in site-year 2, 0.58 and 0.57 in site-year 3, and 0.43 and 0.42 for their average). When rated total weed control by observer A was graphed against that of observer B, the relationship was a nearly ideal 1:1 linear relationship in only 1 of 3 site-yr. In two other site-years, equations were nonlinear, indicating that one observer distinguished smaller differences between treatments at lower rated control than the other observer. Between-row total weed cover and in-row total weed height influenced observer weed control rating.

Weed community and corn yield variability in diverse management systems

The effects of crop rotation and management system on annual variability in weed communities and crop yields were assessed in a 4-yr study in Michigan. Variability of the weed community and corn yields were assessed using the coefficient of variation (CV) and a multivariate dissimilarity index (Bray-Curtis) that accounted for changes in both weed species abundance and composition. The treatments included two rotations: continuous corn and a corn–corn–soybean–wheat rotation, and two management systems: conventional (CONV) and organic-based (ORG). Weed biomass was significantly higher in the ORG system; however, there was no effect of crop rotation on weed biomass or number of weed species in a treatment (species richness). Annual variability in weed community composition and structure was affected by both crop rotation and management system and was highest in the ORG rotation. In contrast to the weed community, variability in corn yield was highest in the least-diverse cropping system (CONV monoculture), despite that system having a more constant weed community. Corn yield in the ORG rotation was not significantly different from that in the CONV monoculture. Results of this study suggest that management aimed at increasing cropping system diversity may have additional effects on weed communities and crop yields beyond those commonly reported, and these may have important implications for the development of more efficient and sustainable weed and crop management practices.

Analysis of Soil Fertility and Management Effects on Yields of Wheat and Corn in the Rolling Pampa of Argentina

AbstractThe Rolling Pampa is the most productive region of the Argentine Humid Pampa comprising around 10 Mha. Wheat (Triticum aestivum L.), corn (Zea mays L.), and soya bean [Glycine max (L.) Merr.] are the main grain crops produced. To develop sound cropping strategies, a better understanding of the impact of soil fertility and management on crops is needed. The objective of this study was to develop models for estimating the effects of growing season precipitation, soil fertility and management on wheat and corn yields. Data from 347 wheat and 323 corn field experiments and production fields over six growing seasons were used. Soil, management and weather characteristics were determined and yields were then evaluated. Data were analysed using linear and quadratic models and a quadratic polynomial surface model. Soil fertility, management and rainfall and interactions were analysed. Growing season precipitation correlated with wheat (R2 = 0.42) and corn (R2 = 0.25) yield. Maximum wheat yield was achieved with 350–400 mm rainfall and corn yield reached a plateau around 700 mm. Soil fertility accounted for 33 % of wheat yield variability and 5 % of corn yield variability. Management accounted for 48 and 9 % respectively. Whole polynomial models integrating rainfall, fertilizer N and P rates, soil N and P, previous crop and tillage system accounted for 67 % of wheat yield variability and 51 % of corn yield variability. Soil organic matter was not included in the models but an indirect effect on yield was detected as organic matter correlated with initial soil N levels for both crops. Soya bean as a previous crop had a positive effect on wheat and corn yields. Wheat was insensitive to tillage system but corn yield was higher under no till. N and P fertilization had a two‐ to three‐fold greater impact on yield than soil nutrient levels. As this region is considered to be of high soil fertility and has a history of very low fertilizer consumption, adequate use of N and P fertilization will be essential to maintaining high wheat and corn yields.

Associação entre El Niño Oscilação Sul e a produtividade do milho no Estado do Rio Grande do Sul

A alta variabilidade interanual da produtividade de milho, no Rio Grande do Sul, é determinada, principalmente, pela variabilidade da precipitação pluvial, e esta, em grande parte está associada ao fenômeno El Niño Oscilação Sul (ENOS). A disponibilidade, hoje, de previsões sazonais do ENOS faz vislumbrar a possibilidade de uso dessas informações para minimizar prejuízos e maximizar a produtividade. Há necessidade, porém, de se avaliar a vulnerabilidade da produtividade do milho a esse fenômeno. O objetivo deste trabalho foi quantificar a associação entre a produtividade de milho e a variabilidade da precipitação pluvial, causada pelo ENOS. Para a análise, foram tomadas séries históricas de produtividade, de precipitação pluvial mensal, de ocorrência das fases do ENOS (El Niño e La Niña), de Temperatura da Superfície do Mar (TSM) no Pacífico equatorial, e do Índice de Oscilação Sul (IOS). Há forte tendência do El Niño em favorecer a cultura do milho, o que dá oportunidade à alta produtividade, ao passo que em anos de ocorrência de La Niña há alta freqüência de baixa produtividade. A precipitação pluvial mais associada à produtividade do milho é a integrada de outubro a março. Essas informações são úteis para decisões quanto a alternativas de manejo da cultura, frente a uma previsão de El Niño ou La Niña.

Trends and Variability in U.S. Corn Yields Over the Twentieth Century

AbstractThe United States is currently responsible for 40%–45% of the world’s corn supply and 70% of total global exports [the U.S. Department of Agriculture–National Agricultural Statistics Service (USDA–NASS)]. Therefore, analyses of the spatial and temporal patterns of historical U.S. corn yields might provide insight into future crop-production potential and food security. In this study, county-level maize yield data from 1910 to 2001 were used to characterize the spatial heterogeneity of yield growth rates and interannual yield variability across the U.S. Corn Belt.Widespread decadal-scale changes in corn yield variability and yield growth rates have occurred since the 1930s across the Corn Belt, but the response has varied substantially with geographic location. Northern portions of the Great Plains have experienced consistently high interannual corn yield variability, averaging 30%–40% relative to the mean. Increasing usage of irrigation in Nebraska, Kansas, and Texas, since the 1950s, has helped boost yields by 75%–90% over rain-fed corn, creating a yield gap of 2–4 T ha−1 between irrigated and nonirrigated corn that could potentially be exploited in other regions. Furthermore, irrigation has reduced interannual variability by a factor of 3 in these same regions. A small region from eastern Iowa into northern Illinois and southern Wisconsin has experienced minimal interannual yield variability, averaging only 6%–10% relative to mean yields.This paper shows that the choice of time period used for statistical analysis impacted conclusions drawn about twentieth-century trends in corn yield variability. Widespread increases in yield variability were apparent from 1950 onward, but were not significant over the entire 1930–2001 period. There is also evidence that yield variability decreased from the early 1990s to 2001. Corn yield growth rates peaked at an annual-average rate of 3%–5% in the 1960s (124.5 kg ha−1 yr−1), but have steadily declined to a relative rate of 0.78% yr−1 (49.2 kg ha−1 yr−1) during the 1990s. A general inverse relationship between increasing corn yield and decreasing yield growth rates was noted after county-level yields reached 4 T ha−1, suggesting that widespread, significant increases in corn yield are not likely to take place in the future, particularly on irrigated land, without a second agricultural revolution.

Evaluating Corn Nitrogen Variability via Remote-Sensed Data

Transformations and losses of nitrogen (N) throughout the growing season can be costly. Methods in place to improve N management and to facilitate split N applications during the growing season can be time consuming and logistically difficult. Remote sensing (RS) may be a method to rapidly assess temporal changes in crop N status and to promote more efficient N management. This study was designed to evaluate the ability of three different RS platforms to predict N variability in corn (Zea mays L.) leaves during vegetative and early reproductive growth stages. Plots (15 × 15 m) were established in the Coastal Plain (CP) and in the Appalachian Plateau (AP) physiographic regions each spring from 2000 to 2002 in a completely randomized design. Treatments consisted of four N rates (0, 56, 112, and 168 kg N ha−1) applied as ammonium nitrate (NH4NO3) replicated four times. Spectral measurements were acquired via spectroradiometer (λ = 350–1050 nm), Airborne Terrestrial Applications Sensor (ATLAS) (λ = 400–12,500 nm), and the IKONOS satellite (λ = 450–900 nm). Spectroradiometer data were collected on a biweekly basis from V4 through R1. Due to the nature of satellite and aircraft acquisitions, these data were acquired per availability. Chlorophyll meter (SPAD) and tissue N were collected as ancillary data, along with each RS acquisition. Results showed vegetation indices derived from hand-held spectroradiometer measurements as early as V6–V8 were linearly related to yield and tissue N concentration. The ATLAS data were correlated with tissue N at the AP site during the V6 stage (r 2 = 0.66), but no significant relationships were observed at the CP site. No significant relationships were observed between plant N and IKONOS imagery. By using a combination of the greenness vegetation index and the normalized difference vegetation index, RS data acquired via ATLAS and the spectroradiometer could be used to evaluate tissue N variability and to estimate corn yield variability given ideal growing conditions.

Yield Variability as Influenced by Climate: A Statistical Investigation

One of the issues with respect to climate change involves its influence on the distribution of future crop yields. Many studies have been done regarding the effect on the mean of such distributions but few have addressed the effect on variance. Furthermore, those that have been done generally report the variance from crop simulators, not from observations. This paper examines the potential effects of climate change on crop yield variance in the context of current observed yields and then extrapolates to the effects under projected climate change. In particular, maximum likelihood panel data estimates of the impacts of climate on year-to-year yield variability are constructed for the major U.S. agricultural crops. The panel data technique used embodies a variance estimate developed along the lines of the stochastic production function approach suggested by Just and Pope. The estimation results indicate that changes in climate modify crop yield levels and variances in a crop-specific fashion. For sorghum, rainfall and temperature increases are found to increase yield level and variability. On the other hand, precipitation and temperature are individually found to have opposite effects on corn yield levels and variability.

Least limiting water range indicators of soil quality and corn production, eastern Ontario, Canada

The least limiting water range (LLWR) attempts to incorporate crop-limiting values of soil strength, aeration, and water supply to plant roots into one effective parameter (on the basis of soil water content). The LLWR can be a useful indicator of soil quality and soil physical constraints on crop production. This study focused on assessing dynamic cultivation zone LLWR parameters between different cropping/tillage/trafficked clay loam plots at Winchester, Ont., to identify potential management impact on surficial soil physical conditions for contrasting growing seasons. This study also evaluated dynamic cultivation layer LLWR variables as indicators of corn ( Zea mays L.) plant establishment and corn yield. The results suggest that no-till soils had lower average air-filled porosities (AFP) and O 2 concentrations than respectively managed tilled plots for both years of study. Potential trafficking effects on aeration properties were most evident in no-till relative to till; preferentially trafficked no-tilled plots had lower AFP and O 2 concentrations than respective non-preferentially trafficked no-till plots for both years of study. Corn establishment and yield variability were principally explained by cumulative differences between daily AFP and aeration threshold values, and the cumulative number of days daily AFP was below an AFP aeration threshold for specific corn growth stage periods. Lower AFP was linked to lower yields and plant establishments. Soil strength, as measured by cone penetration resistance, was important over certain sites, but not as important globally as AFP in predicting crop properties. Overall, conventional tilled soils that were not preferentially trafficked had most favorable aeration properties, and subsequently, greatest corn populations and yields. No-till soils were at greater risk of aeration limiting conditions, especially those in continuous corn and preferentially trafficked.

Corn (Zea mays L.) Yield Prediction Using Multispectral and Multidate Reflectance

Yield predictive models based on multiple sampling dates may explain more yield variability than models based on a single sampling date. This study determined the influence of two approaches (multiple regression of either soil and crop multispectral and multidate reflectance or variables developed during principal‐component analysis of reflectance data) on corn (Zea mays L.) yield predictions. Research was conducted in 1999, 2000, and 2001. Corn yield data from two 65‐ha fields were collected with a yield monitor, and crop and soil radiance [green, red, and near‐infrared (NIR) wavebands] was measured three times (April–May, July, and August–September). Relative radiance (reflectance) was determined by dividing the measured radiance by the radiance at invariant target. Stepwise multiple regression based on reflectance or principal components was used to develop predictive equations. Multiple‐regression models based on 2 yr of reflectance data were biased and provided poor estimates of yield whereas a model based on variables developed during principal‐component analysis of reflectance data measured in the spring and summer of 1999 and 200 was unbiased and explained 45% of the corn yield variability in 2001. Differences between these models were attributed to multicollinearity of the data. Models that included data from two or three sampling dates generally explained more yield variability than models that used only one sampling date. Reflectance measured early in the season provided information about soil water and color while reflectance measured in August and September provided information about plant conditions.

Spatial and Temporal Variability of Soil Nitrate and Corn Yield

High levels of residual soil NO3–N can contaminate ground water by leaching through the soil. Our objective was to reduce the level and spatial variability of residual soil NO3–N while maintaining optimum corn (Zea mays L.) production by variable rate N fertilizer application. The experiment was located on a 60‐ha sprinkler‐irrigated corn field in central Nebraska and included four N management practices: uniform rate, variable rate (VRAT), variable rate at 75% of recommended amount (VRAT @ 75%), and variable rate plus 10% (VRAT + 10%). VRAT @ 75% decreased the amount of residual NO3–N in the soil while maintaining similar grain yield to the other treatments, indicating over‐application of N with treatments receiving the recommended rate. Increasing the recommended rate by 10% (VRAT + 10%) did not increase corn yield or residual soil NO3–N. Based on multifractal spectrum, no consistent pattern of spatial variability of soil NO3–N was observed for each treatment across years. Spatial variability in corn grain yield was much lower than that for soil NO3–N, indicating noneffectiveness of using soil NO3–N spatial distribution for variable rate N application unless some areas in the field are severely N deficient. Variable rate N application did not reduce variability of residual soil NO3–N or corn grain yield as compared with uniform N. Multifractal analysis quantitatively characterized the extent and pattern of spatial and temporal variability in corn grain yield and residual soil nitrate.

Spatial and Temporal Variability of Soil Nitrate and Corn Yield

High levels of residual soil NO3–N can contaminate ground water by leaching through the soil. Our objective was to reduce the level and spatial variability of residual soil NO3–N while maintaining optimum corn (Zea mays L.) production by variable rate N fertilizer application. The experiment was located on a 60-ha sprinkler-irrigated corn field in central Nebraska and included four N management practices: uniform rate, variable rate (VRAT), variable rate at 75% of recommended amount (VRAT @ 75%), and variable rate plus 10% (VRAT + 10%). VRAT @ 75% decreased the amount of residual NO3–N in the soil while maintaining similar grain yield to the other treatments, indicating over-application of N with treatments receiving the recommended rate. Increasing the recommended rate by 10% (VRAT + 10%) did not increase corn yield or residual soil NO3–N. Based on multifractal spectrum, no consistent pattern of spatial variability of soil NO3–N was observed for each treatment across years. Spatial variability in corn grain yield was much lower than that for soil NO3–N, indicating noneffectiveness of using soil NO3–N spatial distribution for variable rate N application unless some areas in the field are severely N deficient. Variable rate N application did not reduce variability of residual soil NO3–N or corn grain yield as compared with uniform N. Multifractal analysis quantitatively characterized the extent and pattern of spatial and temporal variability in corn grain yield and residual soil nitrate.

Análise de interações solo-planta-clima em zonas diferenciadas de área de cultivo de milho

A expressiva área de cultivo de milho no Brasil e a necessidade constante de aprimoramento do seu manejo, justificam o estudo sobre novas metodologias de tratamento dos recursos financeiros ou técnicos destinados à produção. A cultura do milho tem demonstrado significativa variabilidade espacial da produtividade em estudos sobre a questão e variabilidade temporal também pode ser esperada, ao longo das estações de crescimento. A observação do crescimento e do desenvolvimento de cultivos através de modelos de simulação, pode servir de apoio para o estudo das causas das variabilidades citadas, além de atuar como elemento de integração de dados de solo, planta e clima, principalmente em estudos sobre implementação do manejo localizado, onde se busca aproveitar diferenças de potencial produtivo na mesma área de cultivo. Em uma área de produção de milho do município de Angatuba, SP, região do Vale do Paranapanema (23º33'S; 48º18'W; 670 m), foram levantados dados de solo, planta e clima, adequados ao banco de dados do modelo Ceres-Maize, para simulações de elementos envolvidos na produtividade da cultura de milho, em uma estação de crescimento e ao longo de uma série climática de médio prazo, simulando-se, separadamente, para três tipos de solo classificados na área. O modelo Ceres-Maize possibilita a simulação do crescimento e desenvolvimento da cultura do milho, em escala diária, com base no balanço de carbono, nitrogênio e água, para uma cultivar com características morfogenéticas conhecidas. Interações de solo, planta e clima, causaram diferenças moderadas nos padrões de flutuação da produtividade simulada ao longo dos anos, em duas zonas distintas, dentro da mesma área de cultivo de milho, diferenciando moderadamente as dosagens de nitrogênio para o máximo rendimento econômico. A simulação evidenciou que a aplicação do modelo como ferramenta de análise "não pontual" está condicionada ao contraste espacial existente nas propriedades físicas do solo e no potencial produtivo dos cultivos.

Tillage and Rotation Effects on Soil Physical Characteristics

Farmers are adopting different cropping systems, so our objective was to identify tillage × rotation interactions for soil physical characteristics in the sixth year of a tillage (moldboard plow, chisel, and ridge) × rotation {continuous corn (Zea mays L.), soybean [Glycine max (L.) Merr.]–corn and soybean–wheat (Triticum aestivum L.)/clover (Trifolium pretense L.)–corn} study. Moldboard plow had lower penetration resistance (0.97 MPa) and bulk density (1.19 g cm−3) and greater infiltration (75 μm s−1) and porosity (−2.5 to −40 kPa soil water potential) compared with ridge tillage (1.39 MPa, 1.31 g cm−3, and 24 μm s−1, respectively) at the sixth leaf (V6) stage and greater infiltration (106 and 31 μm s−1, respectively) during early grain fill (R3) of corn. In ridge tillage, the interrow had lower penetration resistance (1.10 Mpa) and bulk density (1.28 g cm−3) and greater infiltration (35 μm s−1) vs. the row (1.68 MPa, 1.34 g cm−3, and 13 μm s−1, respectively) at V6 but greater penetration resistance (3.13 and 2.46 MPa, respectively) at R3. The soybean–wheat/clover–corn rotation had the greatest earthworm densities (504 m−2) and infiltration (68 μm s−1) among rotations at V6. Earthworm densities and infiltration explained about 25% of corn yield variability, which may have contributed in part to the 15 to 40% yield advantage for corn in the soybean–wheat/clover–corn rotation in moldboard plow. Tillage × rotation interactions did not exist for soil physical characteristics, so 5‐yr tillage effects would be consistent across rotations.

Stochastic simulation of soil strength/compaction and assessment of corn yield risk using threshold probability patterns

Soil compaction can suppress crop yields. Thus, compaction indices such as cone penetration resistance (PR) can be useful to farmers and researchers interested in identifying areas in the field where soil strength/compaction, or governing soil properties reflected by PR measurements, might be problematic to crop yield. The general aim of this paper was to apply stochastic simulation to spatially delimit critically important soil strength patterns, the spatial disposition and uncertainty of those patterns, and to determine if broad strength/compaction spatial units defined via PR threshold probability patterns could be used to reflect corn yield variability under different rotation, tillage, and trafficking management treatments. Shallow (3–15 cm depth) linear PR patterns were strongly anisotropic while deeper patterns (25–40 cm depth) were more regionalized in character. Stochastic simulation produced very strong global patterns of low (<2 MPa) and high (>2 MPa) PR values. Broad spatial units defined on the basis of these patterns were used to generate corn yield risk maps. It was found that yields, under the various treatments, reflected the risk patterns in an expected way 75% of the time over 5 years of observation. That is, under similar management treatments, lower risk areas (areas where there was lower probability of soil strength exceeding 2 MPa in the top 3 to 15 cm of the soil) produced higher yields than higher risk areas (areas where there was higher probability of soil strength exceeding 2 MPa in the top 3 to 15 cm of soil).

USE OF PRECISION FARMING TO IMPROVE APPLICATIONS OF FEEDLOTWASTE TO INCREASE NUTRIENT USE EFFICIENCY AND PROTECTWATER QUALITY

Spatial variability in crop yields can be caused by many factors, which makes it difficult to determine the most limiting factors. Application of animal wastes to relatively infertile areas offers the potential to supply needed nutrients and improve soil physical properties. The objectives of this study were to test a manure application strategy to reduce spatial variability in corn (Zea mays L) yield and to identify the most limiting nutrients in relatively low yielding areas in a field. Fresh solid beef feedlot manure was applied in 1997 to a strip across areas with variable fertility status. No fertilizer was applied with the manure in 1997. Uniform N fertilizer, but no manure, was applied in 1998. Leaf tissue samples and chlorophyll meter readings were collected along the strips during the growing season and from adjacent strips without manure application. Grain yield was determined at plant maturity. In 1997, chlorophyll meter readings indicated season long N deficiency (<95% sufficiency index) in no-manure plots with sufficiency indices of 93, 88, 85, and 88% for the V10, V17, R2, and R3 growth stages, respectively. Only an early season N deficiency was detected in a few of the no-manure plots in 1998. Leaf tissue analyses indicated N and P were growth limiting factors in 1997, with leaf N concentrations of 25, 26, and 27 mg g−1 for non-manure plots and 30, 33, and 31 mg g−1 for manure plots at V12, R1, and R3 growth stages, respectively. Leaf P concentrations were 2.0, 2.0, and 1.9 mg g−1 for no-manure plots versus 2.5, 2.7, and 2.3 mg g−1 for manure plots, respectively. In 1998, neither N or P were identified as limiting factors. Grain yields in 1997 were 10.2 and 12.2 Mg ha−1, which increased to 11.9 and 12.8 Mg ha−1 in 1998 for no-manure and manure plots, respectively.

Elevation and infiltration in a level basin. I. Characterizing variability

Spatial characterization of soil physical properties could improve the estimation of surface irrigation performance. The aim of this research was to characterize the spatial and time variability of a set of irrigation-related soil properties. The small-scale experimental level-basin (729 m2) was located on an alluvial loam soil. A corn crop was established in the basin and irrigated five times during the season. A detailed survey of the soil properties (generally using a 3 × 3 m network) was performed. Classic statistical and geostatistical tools were used to characterize the variables and their interactions. Semivariograms were validated for the studied variables, except for the clay fraction, the saturated hydraulic conductivity and the infiltration parameters. The resulting geostatistical range was often in the interval of 6–10 m. For the three surveys of soil surface elevation the range was smaller, about 4 m. No correlation was found between saturated hydraulic conductivity and the other soil physical properties. Soil surface elevation showed a high correlation between surveys. After the first irrigation, the standard deviation of elevation increased from an initial 9.6 mm to 20.8 mm. The soil physical parameters were used to map the soil water management allowable depletion. In a companion paper these results are used to explain the spatial variability of corn yield and soil water recharge due to irrigation.

Model-based technique to determine variable rate nitrogen for corn

Past efforts to correlate yield from small field plots to soil type, elevation, fertility, and other factors have been only partially successful for characterizing spatial variability in corn ( Zea mays L.) yield. Furthermore, methods to determine optimum nitrogen rate in grids across fields depend upon the ability to accurately predict yield variability and corn response to nitrogen. In this paper, we developed a technique to use the CERES-Maize crop growth model to characterize corn yield variability. The model was calibrated using 3 years of data from 224 grids in a 16 ha field near Boone, IA. The model gave excellent predictions of yield trends along transects in the field, explaining approximately 57% of the yield variability. Once the model was calibrated for each grid cell, optimum nitrogen rate to maximize net return was computed for each location using 22 years of historical weather data. Results show high spatial distribution of optimum nitrogen fertilizer prescription for grids across the field. Grid-level nitrogen fertilizer management used lower amounts of fertilizer, produced higher yields and was more profitable than either transect- or field-level (single rate) fertilizer application.