Relative Corn Yield Research Articles

Comparative Sensitivity Analysis of Hydrology and Relative Corn Yield under Different Subsurface Drainage Design Using DRAINMOD

DRAINMOD is a process-based hydrologic model used to analyze the effectiveness of various drainage systems and management strategies. In this study, a sensitivity analysis of DRAINMOD hydrologic parameters for two different field settings located at Champaign, Illinois, was performed to determine the most sensitive parameters that affect the subsurface flow and relative productivity of corn. Latin-Hypercube One-Factor-at-a-Time (LH-OAT) was used to determine the sensitivity index of 17 parameters for six objective functions for daily flow, water balance, and relative yield for the productivity of corn. The results indicated that flow and yield were highly sensitive to drainage design parameters such as drainage depth and spacing. Winter flow and the water balance were sensitive to soil thermal conductivity parameters; however, they had no impact on the relative corn yield. The significant difference in sensitivity of the two fields was observed in the hydraulic conductivity of soil layers due to varying thicknesses for different soil types. This study highlights the need for more careful calibration of these sensitive parameters to reduce equifinality and model output uncertainty and appropriate drainage design for optimizing crop productivity and drainage outflow.

Design drainage rates to optimize crop production for subsurface-drained fields

Agricultural subsurface drainage is critical for crop production in temperate humid regions. With the heightened concern of its water-quality implications, we need a method to design drainage systems for both crop production and environmental protection. The objective of this research was to develop an empirical equation that estimates the design drainage rate (DDR) for local soil and weather conditions. This DDR can then be used in the Hooghoudt equation to estimate the optimum drain spacing that maximizes profit. We conducted DRAINMOD simulations for each combination of four factors: three drain depths, three effective radii, seven locations, and five soils. For each combination of factors, simulations were repeated for a range of drain spacing from 5 to 100 m using 30 years (1990–2019) of weather data planted with continuous corn (Zea mays L.). Simulations provided a 30-year average relative corn yield and drainage discharge for each combination of factors. The drain spacings that maximized annual economic return on investment were identified as optimum spacings and used to calculate the DDR for each combination of factors. Results were then used in a multiple linear regression to develop two empirical equations for northeast and southeast USA with DDR as the dependent variable. The independent variables were the long-term average growing-season precipitation, drain depth, equivalent saturated hydraulic conductivity, and depth to restrictive layer. The environmental value of the empirical equations is that they help avoid too narrow of a drain spacing, thereby preventing more drainage than is needed. In conclusion, application of these empirical equations is a means for estimating the site-specific optimum drain spacing that maximizes economic return on investment.

Corn response to tillage and side‐dress nitrogen management on claypan soil

AbstractSince information islimited regarding N management and tillage options for corn (Zea mays L.) grown on claypan soil in the eastern Great Plains, the objective of this study was to determine the effect of preplant and side‐dress N applications on corn grown in conventional‐ and no‐till systems. The yield penalty for no‐till grown corn was nearly 20% due partially to an 8% stand reduction, but also to lower kernel weight and kernels ear–1 than with conventional till. These yield and yield component reductions with no‐till may be because of lower dry matter production and N uptake throughout the growing season. The general lack of interactions suggest that N management system effects on corn were not influenced by tillage system selection. Fertilizing with N more than doubled corn yields primarily by nearly doubling the number of kernels ear–1, but with additional increases in kernel weight and average number of ears plant–1. Averaged over years, split‐N resulted in up to 15% greater yield, and additional side‐dress N resulted in up to 28% greater yield than when 168 kg N ha–1 was applied only at preplant, with no yield reduction for delaying side‐dress application from V6 to V10. The relationship of relative N uptake to relative corn yield approached a direct 1:1 relationship by R1 (silking). The decision of whether to split‐apply fertilizer N or to add additional N, regardless of tillage system, will likely be influenced by economic factors, but side‐dress N applications may extended to the V10 stage with no yield penalty.

Assessment of In‐Season Soil Nitrogen Tests for Corn Planted into Winter Annual Cover Crops

Core Ideas The Solvita 1‐d CO2 mineralization test could be a new tool to improve in‐season N rate recommendations for corn. Solvita and soil NO3–N tests may be useful for predicting the level of early‐season N mineralization from winter cover crops (WCCs). Soil NO3–N collected at the V4 growth stage of corn at 0 to 15 cm was positively correlated (R2 = 0.45) with corn check yield. Neither the Solvita nor the presidedress nitrate test detected WCC N mineralization consistently enough to use them in rate recommendations. Environmental and economic goals encourage the use of soil N tests to improve fertilizer N (FN) management in corn (Zea mays L.). Recently, the Solvita 1‐d CO2 burst test, which proposes to estimate soil potentially mineralizable N (PMN), has been promoted as a tool for FN recommendations. We aimed to compare the Solvita test with the established presidedress nitrate test (PSNT) for estimating optimum sidedressed FN rates in a typical corn crop rotation in the Mid‐Atlantic United States that includes winter annual cover crops (WCCs). Research was conducted at eight locations from 2012 to 2014. Three WCC treatments [cereal rye (Secale cereale L.), hairy vetch (Vicia villosa Roth ssp. villosa) or a cereal rye–hairy vetch mix] were the main plots and 10 FN rates were the subplots. The WCCs affected preplanting (PP) Solvita results at one location, V4 NO3–N at 0 to 15 cm (PSNT15) at four locations, and V4 NO3–N at 0 to 30 cm (PSNT30) at two locations. Correlations between soil N test parameters and relative corn yields ranged from 0.31 to 0.13. Values for PSNT15 and PSNT30 correlated positively with corn check yields (r = 0.41 and 0.39 respectively). Solvita did not provide additional information to PSNT for predicting preplanting PMN, V4 PMN, or corn check yields. The advantages of the Solvita test were its simplicity, speed of analysis, and lower coefficient of variation relative to the PSNT. Neither method was consistently effective for predicting WCC effects on soil N or relative corn yield.

Prediction of Relative Corn Yield with Soil‐Nitrate Tests under Irrigated Mediterranean Conditions

Soil nitrate tests are a useful tool to manage corn (Zea mays L.) N fertilization under rainfed conditions, but the feasibility of using these tests under irrigated Mediterranean conditions has received little attention. The objective of this study was to determine whether the preplant nitrate test (PPNT), pre‐sidedress nitrate test (PSNT), and soil available‐N content test (PPNT or PSNT plus N fertilizer) could accurately predict relative corn yield (RY) under irrigated, high‐yielding conditions. Twenty‐one corn N‐response experiments were conducted in Northeast Spain between 1999 and 2009. The trials were flood‐ or sprinkler‐irrigated. In each trial, three to six N treatments (from 0–300 kg N ha–1) were surface‐applied to corn. Predictions of RY with the soil nitrate tests were low when the two irrigated systems were considered together (R2 < 0.43). However, the available‐N content provided moderately good predictions of RY for the sprinkler‐irrigated fields (R2 = 0.54–0.65) and poor predictions for the flood‐irrigated fields (R2 = 0.17–0.36). The critical soil available‐N content (0–30‐cm soil depth) for maximum RY was established at 193 to 209 kg NO3–N ha–1 (≈46–50 mg kg–1) for sprinkler‐irrigated corn preceded by a cereal crop. These critical available‐N values were lower when corn followed alfalfa and were greater than the critical PSNT levels reported for rainfed conditions in the Midwest and in Argentina (20–30 mg kg–1). The soil available‐N content test could be a useful tool for managing corn N fertilization under sprinkler‐irrigated Mediterranean conditions.

Critical Soil Zinc Deficiency Concentration and Tissue Iron: Zinc Ratio as a Diagnostic Tool for Prediction of Zinc Deficiency in Corn

ABSTRACT Zinc (Zn) fertilizer application is most economic if based on soil test and plant analysis information. The aim of this study was to determine the soil test [diethylenetrinitrilopentaacetate (DTPA) and ethylenetriaminepentaacetic acid (EDTA) extractable] Zn-critical levels and tissue Fe/Zn ratio for corn (Zea mays L.). A greenhouse experiment with 12 soil series and two Zn fertilizer treatments (0 and 15 mg Zn kg−1 as zinc sulfate) was conducted. Critical Zn deficiency levels were determined using the Cate-Nelson procedure. Relative corn yield varied from 0.59 to 1.64. Critical deficiency levels based on the Cate-Nelson method were 1.50 and 1.17 mg kg−1 for DTPA and EDTA-extracted soil Zn, respectively. No accurate critical deficiency level could be established using the shoot Zn concentrations. The critical iron (Fe)/Zn ratio in the corn shoot was 3.9. Values greater than 3.9 indicate hidden Zn deficiency and probable response to applied Zn.

Using vegetation health indices and partial least squares method for estimation of corn yield

In this paper the possibility of predicting corn yield by the use of real time acquired satellite data from the Advance Very High Resolution Radiometer (AVHRR) sensor and Partial Least Squares (PLS) method was investigated. To test the methodology in practice, 23 years (1982–2004) of AVHRR data together with official corn yield statistics of Haskell County, Kansas, USA, were used for model development and validation. The AVHRR reflectances in the visible and thermal bands, extracted from the National Oceanic and Atmospheric Administration (NOAA)/NESDIS archive, were transformed into Vegetation Health (VH) Indices (Vegetation Condition Index (VCI) and Temperature Condition Index (TCI)). The PLS method was used to construct a model relating corn yield anomaly with VH indices. Independent model verification showed that the error of corn yield prediction in Haskell County, Kansas, USA, was less than 6%. For several reasons this approach could have excellent potential as a commercial application: satellite data are inexpensive, easily available and yield estimates can be known two to three months before harvest has been completed.

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.

In-Row and Between-Row Interference by Corn Modifies Annual Weed Control by Preemergence Residual Herbicide

The presence of row crops, such as field corn, improves herbicidal control of weeds, but the impact of crop row position on herbicide dose–response relationships for weeds is unknown. At midseason at three site-years in Missouri, total weed cover (WC) was reduced by increasing soil residual herbicide rate in a dose-dependent response and was as much as 20% lower in-row (IR) than between-row (BR). Preemergence atrazine +S-metolachlor + clopyralid + flumetsulam at different rates (0×, 0.25×, 0.5×, 0.75×, and 1×, where 1× rate was 2,240 + 1,750 + 210 + 67 g ai/ha, respectively) were applied at planting in field corn to control giant foxtail, the chief weed present, and annual broadleaf weeds, largely common waterhemp. Lower herbicide rates were required to reduce IR WC to the same extent as BR WC, but these rates varied between site-years. At all three site-years, a least squares regression equation adequately described data variability relating corn yield to IR or BR WC (or both) (i.e., Y = a + bBR2, where Y is corn yield in kg/ha, BR is BR WC [%], and a and b are coefficients).

A NEURAL NETWORK FOR SETTING TARGET CORN YIELDS

In this research, artificial neural networks were employed to model corn yield. The overall objective was to test two hypotheses, i.e.: (1) That an artificial neural network (ANN) can be trained to approximate the nonlinear function relating corn yield to the factors that influence yield, and (2) That an ANN trained for one field can be retrained for a different field with a much sparser data set. The specific research objectives were: (1) To train an ANN using corn yield and input factor data from the Morrow Plots, (2) To evaluate the performance of the trained ANN, and (3) To retrain the ANN using data from the Dudley Smith Farm. First, the factors affected corn yields were analyzed. The corn yield was expressed as function of soil factors, weather factors, and management factors. 15 influencing factors were fed into input layer of the neural network. The neural network structure was designed with 15 input factors, one output, corn yield, and one hidden layer with 20 nodes. The network topology and parameters were set by trial and error. The Morrow Plots data was used to train the neural network. The data set was divided to training set and test set. After training with 5,000 epochs, the test set was used to verify this network. The RMS error was about 20%. Then, the network performance was evaluated in four aspects: for prediction yield trend with each influencing factor, the network gave realistic prediction trend with each factor; for interaction between nitrogen fertilizer and late July rainfall, the network captured the interaction between the two factors; for the influencing factors combination searching to get maximized yield using genetic algorithm, the network predicted a yield 75% larger than the maximum observed yield in the training set; for the influencing factors sensitivity analyzing, the calculated yields were most sensitive to the corn growing season rainfall, especially late July rain, then nitrogen fertilizer. The network was retrained with the Dudley Smith Farm data. The network topology was changed accordingly. The verification of the retraining model, the RMS error, was about 17%. Several training and retraining strategies were discussed.

Relationship between Late Spring Soil Nitrate Concentrations and Corn Yields in New York

Proper N management is important to optimizing profit and minimizing N loss. The efficiency of N use may be improved in humid regions with a reliable soil test to guide fertilizer N recommendations. Recently, a presidedress nitrate soil test (PSNT) has shown promise as a means of quantifying the size of the potential mineralizable organic N pool in soil. This study was conducted to determine if the PSNT could he used for corn (Zea mays L.) production to identify N responsive and nonresponsive sites, predict the economic optimum fertilizer N rate, and improve the current fetilizer N recommendation procedure. The PSNT gave a useful and defined critical level of 21 ppm nitrate-N, delineating N responsive from nonresponsive sites. The success rate for making the correct decision about N responsiveness was 84%, indicating a good relationship between the PSNT and the ability of the soil to supply N [...]

Simulation of Corn Yield by a Water Management Model for a Coastal Plain Soil in Virginia

ABSTRACT DRAINMOD, a water management simulation model for artificially-drained soils, was evaluated for a Virginia Coastal Plain soil by comparing predicted and measured water table depths from a subirrigation/controlled-drainage site over a period of 3 years. Water table evaluations predicted by the model were in good agreement with measured water table elevations, with an average deviation of 9.47 cm for the 3 years of record. The YIELD version of DRAINMOD was used to predict relative corn yield for the subirrigation/controlled-drainage site. Average corn yield predictions by the model agreed reaonably well with the observed average corn yield, with relative errors of 19.2, 4.4, and 8.1% for 1984, 1985, and 1986, respectively. Additionally, corn yields predicted for a conventional drainage system were considerably lower than those predicted for subirrigation/controlled-drainage, reaffirming the need for irrigation of corn in the Coastal Plain region of Virginia. Research indicate that DRAINMOD is a powerful tool for the design and evaluation of subirrigation/controlled-drainage practices in the Virginia Coastal Plain. Simualations were performed for a 20-year period to determine the effects of system design on corn yield. An economic analysis was performed to determine optimal system design for maximizing profits from corn production. A drain spacing of 21.30 m, a drain depth of 1.10 m, and a 0.65 m weir depth were the design parameters recommended as optimal for maximizing profits from subirrigation/controlled-drainage of corn on a Myatt fine sandy loam soil in the Virginia Coastal Plain.

Weather Forecasts as a Control Input for Water Table Management in Coastal Areas

ABSTRACT FOUR water table management methods were compared in computer simulations of a controlled-drainage and subirrigation system using soils and climate conditions of the Mississippi Delta region. No significant difference in the predicted relative corn yield was obtained among the four management methods when the operation for each method was adjusted to maximize yield. However, stopping subirrigation pumping whenever the rainfall probability index (RPI) exceeded 55% reduced pumping requirements for three of the methods 12 to 21% compared to the continuous subirrigation method. The optimal management of soil-water for agricultural cropland in humid, coastal areas of the U.S. often involves complex daily control of the water table because of the erratic spatial and temporal distribution of rainfall. Periods of excess and deficit soil-water conditions are common within the same growing season. Thus, water management requires both controllable drainage and irrigation facilities. A water table management system popular in the humid region, especially in the southern coastal plains, uses a subsurface draintube system for both controlled-drainage and subirrigation. Controlled-drainage permits storage of rainfall and irrigation water in the soil profile below the depth of an overflow pipe at the drain outlet. Free'' drainage to the full depth of the drainline is often needed during periods of extended or heavy rainfall to reduce the duration of excess water in root zone. During subirrigation, the water level at the drain outlet is maintained just below the overflow pipe by pumping from an external source. A sump-type structure for controlling the water level at the drain outlet for a water table management system is illustrated in Fig. 1. The check-valve, activated by a double-float, allows fast subsurface drainage during peak flow events, functioning like the two-stage weir described by Fouss, et al. (1987b). It is often necessary to pump from a sump in level and low lying topography of the coastal plains, where subsurface drainage by gravity flow is not possible. Water table management with this type of system has a high potential for achieving maximum crop production and water use efficiency if properly controlled to compensate for changes in weather conditions. The timing of changes needed in controlled-drainage and subirrigation to manage optimally the water table depth is a major problem for farmers, especially in coastal areas with fine textured soils. In the Mississippi Delta frequent occurrences of rainfall can cause large variations in water table depth because of the small, 3 to 8%, drainable soil porosity. Results from computer simulation of four management methods for water table control are presented for soils and climate conditions of the Mississippi Delta. The primary research objective was to determine the best operational parameters that efficiently utilized rainfall during the growing season without creating periods of severe excess soil-water conditions, and which minimized the need for pumping drainage and subirrigation water. Special attention was given to the potential use of rainfall probability information from the daily weather forecast as a system control input to minimize subirrigation pumping.

Influence of Spatially Variable Soil Hydraulic Properties on Predictions of Water Stress

AbstractModels are often used to predict drainage system effects on crop production. Variability of soil hydraulic properties on predictions made by many models have not been evaluated. This study evaluated several methods for predicting water transport properties and corn (Zea mays L.) stress as influenced by variable soil hydraulic properties in a field of Portsmouth sandy loam (Typic Umbraquults). Upflux, drainage volume, and infiltration parameters as functions of water table depth were predicted using hydraulic conductivity and soil water retention functions for three soil horizons measured at 150 locations in a field. Crop stress due to both deficient and excess soil water conditions and relative crop yield were estimated using DRAINMOD, a water management simulation model, for three selected methods of averaging soil property inputs. Small differences existed among the three approaches for the 30‐yr average relative corn yield. Large differences in relative corn yield occurred in dry years indicating that the variability of the soil properties was important to consider in predicting crop stress during relatively dry years. More information for the field soil‐drainage response was obtained using the individual locations method which allowed soil property inputs to vary from location to location at each of the 150 points in the field. However, the field averages approach is more practical because fewer data are required to perform the necessary computations and only a 3% difference in the 30‐yr relative yield resulted between the individual location and field average methods.

Time Available for Field Work on Two Ohio Soils

ABSTRACT SIMULATIONS were conducted using DRAINMOD, simulation model, to estimate the days available for field work, compute an indicator of excess water and determine a relative corn yield index. Results are based on 62 simulations for two soils, three locations, several threshold soil water contents and numerous drainage systems. The number of days available for field work for the April through November period was found to vary widely depending on soil, weather location, drainage system and threshold soil water content for field operations. Data is reported on the specific conditions simulated. The relative corn yield index includes factors for excess water, deficit water and delayed planting. Simulations gave average relative yield indexes from 20 to 77 percent. The paper reports days available for field work and corn yield indexes for a number of specific conditions, but additional data for other poorly drained Ohio soils will be necessary to produce broad generalizations from the results..

Crop Production Function Determinations as Influenced by Irrigation and Nitrogen Fertilization Using a Continuous Variable Design

AbstractContinuous variable (CVD) and randomized block, split plot (RBSD) designs were used to produce data from which production functions were developed relating corn yield to soil water and nitrogen fertilizer. Data were collected at Logan, Utah in 1972 and Farmington, Utah in 1973.The CVD water and nitrogen treatments, respectively, were sequential or continuous and not randomized. The design is compact but has some statistical uncertainty. The CVD had 7 or 8 soil water (W) levels and 22 or 24 nitrogen (N) levels compared to 4 W and 5 N levels for RBSD. The area used for the RBSD was 3 or 4× that used for the CVD.The data analyzed for the CVD led to the same conclusions as the analysis of the RBSD. The production function (regression equation) at Logan was different from that at Farmington except when comparisons were made on a relative basis.

Effect of Plant‐Available Stored Soil Moisture on Corn Yield: II. Variable Climatic Conditions

AbstractAgronomists have long been interested in how variation in soils and climate are related to crop yields. An experiment was conducted to quantify the effect of weekly plant available stored soil moisture (PASSM) during 10 weeks of the growing season on corn (Zea mays L.) yield under constant and variable climatic conditions. The objective of this study was to determine how much of the variation in corn yield was due to variations in soils and climate and to develop a model relating corn yield to soil, rainfall, and temperature which could be used to predict corn yields for other locations not studied. Plot yields from four Illinois locations (farms) for 3 years (1969–1971) were correlated with weekly PASSM, rooting depth, or available water holding capacity in the rooting zone and climatic data (rainfall and temperature). Experimental plots had high fertility and management. Weekly PASSM, rainfall, and temperature values were summed by a method similar to Fisher's polynomial technique as previously modified.Differences in corn yields due to soils, locations, and among years within locations are due to differences in climate (rainfall and temperature) and differences the ability of the soil to supply water. When correlated with rainfall and temperature data PASSM determined on a weekly basis was slightly less efficient (R2 = 0.80) than was rooting depth (R2 = 0.83) or amount of preseason water available in the rooting zone (R2 = 0.81) in explaining variations in corn yield. Equations fitted to the data should be useful models in predicting corn yields for other locations and soil conditions not studied. The model utilizing the amount of preseason water available in the rooting zone is particularly easy to utilize in predicting corn yield for other locations from soils and climatic data.