Evaluation of the Haney Soil Health Tool for corn nitrogen recommendations across eight Midwest states

Use and development of soil biological tests for estimating soil nitrogen (N) availability and subsequently corn (Zea mays L.) fertilizer N recommendations is garnering considerable interest. The objective of this research was to evaluate relationships between the Haney Soil Health Test (HSHT), also known as the Soil Health Tool or Haney test, and the economically optimum N rate (EONR) for corn grain yield at 17 sites in eight Midwest US states in 2016. Trials were conducted with a standard set of protocols that included a nonfertilized control plus six N rates applied at planting or as a split between planting and sidedress, soil samples for the HSHT prior to planting, and grain harvest at physiological maturity, and determination of EONR for each N application timing. Results indicated that HSHT recommendations with expected yield accounted for ≤28% of the variation in EONR among sites and N timings. Two components of the HSHT not directly used in the HSHT N recommendation for corn, the soil health calculation, or soil health score, and the Solvita carbon dioxide (CO2)-Burst lab test, accounted for the most variation in EONR. These two components were moderately related (R2 = 0.29 to 0.39) to soil organic matter (OM), highly related (R2 = 0.98) with each other, and subsequently both accounted for over one-half (R2 = 0.55) of the variation in EONR for N applied at planting or as a split. With additional research, these two components may help improve N recommendations for corn in the Midwest, especially Solvita CO2-Burst because it costs less to determine than the soil health calculation

Farmer Perceptions of Adopting Novel Legumes in Traditional Maize-Based Farming Systems in the Yucatan Peninsula

Intercropping constitutes the traditional farming system practice used in various forms for maize production in the Yucatan peninsula. Although practiced for centuries, problems persist with competition for water, nutrients and light between crop species in traditional farming systems. Furthermore, little is known about farmers’ perceptions regarding changes to traditional maize-legume intercropping systems and their interest in novel crop adoption to increase yields in the system while maintaining the practice. The objective of this study was to investigate the maize-based traditional cropping system by assessing the underlying motives and concepts of farmers to practice intercropping in the Yucatan Peninsula and to examine the association between farmers’ level of knowledge about legumes and decisions to adopt intercropping and related practices therein. Farmer surveys were conducted in nine different regions of the Yucatan Peninsula. We selected Xoy, Euan, Muna, Mama, Tahdziú (Yucatan), Becal, Hecelchacam, Dzitbalché and San Antonio Sahcabchén (Campeche) which are representative of agroecological small-scale farming systems. We used a mixed methods case study analysis involving key informant interviews in eight associations of farmers. A sample frame with 73 farmers was selected in total during February 2021 and April 2021. Basic information such as land use, labor inputs, agricultural production and farmer’s perceptions regarding their intercropping systems were collected. Our research shows that the primary motives for intercropping were due to the ability of intercropping to offer a more diversified range of food for human and animal consumption, as well as to take advantage of different harvest periods that this practice offers. The majority of respondents were likely to favor the idea of introducing new legume species in their maize-based cropping systems. Factors such as the type of cropping system (i.e., intercropping or monocropping), access to water and level of knowledge about legumes influenced their decision to adopt intercropping in their farming systems considerably. This paper contributes to the knowledge on the current state and farmers’ perceptions of intercropping systems in the Yucatan Peninsula

Active-Optical Reflectance Sensing Evaluated for Red and Red-Edge Waveband Sensitivity

Uncertainty exists with corn (Zea mays L.) N management due to year-to-year variation in crop N need, soil N supply, and N loss from leaching, volatilization, and denitrification. Active-optical reflectance sensing (AORS) has proven effective in some fields for generating N fertilizer recommendations that improve N use efficiency. However, various sensors utilize different wavebands of light to calculate N fertilizer recommendations making it difficult to know which waveband is most sensitive to plant health. The objective of this research was to evaluate across the US Midwest Corn Belt the performance and sensitivity of the red (R) and red-edge (RE) wavebands. Forty-nine N response trials were conducted across eight states and three growing seasons. Reflectance measurements were collected and topdress N rates (40 to 240 lbs N ac-1 on 40 lbs ac-1 increments) applied at approximately V9 corn development stage. Both R and RE wavebands were compared to the at-planting N fertilizer rate, V5 soil nitrate-N, and end-of-season calculated relative yield. In every comparison the RE waveband demonstrated higher coefficient of determination values over the R waveband. These findings suggest the RE waveband is most sensitive to variations in N management and would work best for in-season AORS management over a geographically-diverse soil and weather region

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest

Soil-nitrogen, potentially mineralizable-nitrogen, and field condition information marginally improves corn nitrogen management

Anaerobic potentially mineralizable nitrogen (PMN) combined with preplant nitrate test (PPNT) or pre-sidedress nitrate test (PSNT) may improve corn (Zea mays L.) N management. Forty-nine corn N response studies were conducted across the U.S. Midwest to evaluate the capacity of PPNT and PSNT to predict grain yield, N uptake, and economic optimal N rate (EONR) when adjusted by soil sampling depth, soil texture, temperature, PMN, and initial NH4–N from PMN analysis. Pre-plant soil samples were obtained for PPNT (0- to 30-, 30- to 60-, 60- to 90-cm depths) and PMN (0- to 30-cm depth) before corn planting and N fertilization. In-season soil samples were obtained at the V5 corn development stage for PSNT (0- to 30-, 30- to 60-cm depths) at 0 kg N ha−1 at-planting rate and for PMN when 0 and 180 kg N ha−1 was applied at planting. Grain yield, N uptake, and EONR were best predicted when separating soils by texture or sites by annual growing degree-days and including PMN and initial NH4–N with either NO3–N test. Using PSNT (mean R2 = .30)-instead of PPNT (mean R2 = .19)-based models normally increased predictability of corn agronomic variables by a mean of 11%. Including PMN and initial NH4–N with PPNT or PSNT only marginally improved predictability of grain yield, N uptake, and EONR (R2 increase ≤ .33; mean R2 = .35). Therefore, including PMN with PPNT or PSNT is not suggested as a tool to improve N fertilizer management in the U.S. Midwest

United States Midwest Soil and Weather Conditions Influence Anaerobic Potentially Mineralizable Nitrogen

Nitrogen provided to crops through mineralization is an important factor in N management guidelines. Understanding of the interactive effects of soil and weather conditions on N mineralization needs to be improved. Relationships between anaerobic potentially mineralizable N (PMNan) and soil and weather conditions were evaluated under the contrasting climates of eight US Midwestern states. Soil was sampled (0–30 cm) for PMNan analysis before pre-plant N application (PP0N) and at the V5 development stage from the pre-plant 0 (V50N) and 180 kg N ha−1 (V5180N) rates and incubated for 7, 14, and 28 d. Even distribution of precipitation and warmer temperatures before soil sampling and greater soil organic matter (SOM) increased PMNan. Soil properties, including total C, SOM, and total N, had the strongest relationships with PMNan (R2 ≤ 0.40), followed by temperature (R2 ≤ 0.20) and precipitation (R2 ≤ 0.18) variables. The strength of the relationships between soil properties and PMNan from PP0N, V50N, and V5180N varied by ≤10%. Including soil and weather in the model greatly increased PMNan predictability (R2 ≤ 0.69), demonstrating the interactive effect of soil and weather on N mineralization at different times during the growing season regardless of N fertilization. Delayed soil sampling (V50N) and sampling after fertilization (V5180N) reduced PMNan predictability. However, longer PMNan incubations improved PMNan predictability from both V5 soil samplings closer to the PMNan predictability from PP0N, indicating the potential of PMNan from longer incubations to provide improved estimates of N mineralization when N fertilizer is applied

Soil Sample Timing, Nitrogen Fertilization, and Incubation Length Influence Anaerobic Potentially Mineralizable Nitrogen

Understanding the variables that affect the anaerobic potentially mineralizable N (PMNan) test should lead to a standard procedure of sample collection and incubation length, improving PMNan as a tool in corn (Zea mays L.) N management. We evaluated the effect of soil sample timing (preplant and V5 corn development stage [V5]), N fertilization (0 and 180 kg ha−1) and incubation length (7, 14, and 28 d) on PMNan (0–30 cm) across a range of soil properties and weather conditions. Soil sample timing, N fertilization, and incubation length affected PMNan differently based on soil and weather conditions. Preplant vs. V5 PMNan tended to be greater at sites that received \u3c 183 mm of precipitation or \u3c 359 growing degree-days (GDD) between preplant and V5, or had soil C/N ratios \u3e 9.7:1; otherwise, V5 PMNan tended to be greater than preplant PMNan. The PMNan tended to be greater in unfertilized vs. fertilized soil in sites with clay content \u3e 9.5%, total C \u3c 24.2 g kg−1, soil organi

Predicting Economic Optimal Nitrogen Rate with the Anaerobic Potentially Mineralizable Nitrogen Test

Estimates of mineralizable N with the anaerobic potentially mineralizable N (PMNan) test could improve predictions of corn (Zea mays L.) economic optimal N rate (EONR). A study across eight US midwestern states was conducted to quantify the predictability of EONR for single and split N applications by PMNan. Treatment factors included different soil sample timings (pre-plant and V5 development stage), planting N rates (0 and 180 kg N ha−1), and incubation lengths (7, 14, and 28 d) with and without initial soil NH4–N included with PMNan. Soil was sampled (0–30 cm depth) before planting and N application and at V5 where 0 or 180 kg N ha−1 were applied at planting. Evaluating across all soils, PMNan was a weak predictor of EONR (R2 ≤ 0.08; RMSE, ≥67 kg N ha−1), but the predictability improved (15%) when soils were grouped by texture. Using PMNan and initial soil NH4–N as separate explanatory variables improved EONR predictability (11–20%) in fine-textured soils only. Delaying PMNan sampling from pre-plant to V5 regardless of N fertilization improved EONR predictability by 25% in only coarse-textured soils. Increasing PMNan incubations beyond 7 d modestly improved EONR predictability (R2 increased ≤0.18, and RMSE was reduced ≤7 kg N ha−1). Alone, PMNan predicts EONR poorly, and the improvements from partitioning soils by texture and including initial soil NH4–N were relatively low (R2 ≤ 0.33; RMSE ≥ 68 kg N ha−1) compared with other tools for N fertilizer recommendations

A Public–Industry Partnership for Enhancing Corn Nitrogen Research and Datasets: Project Description, Methodology, and Outcomes

Due to economic and environmental consequences of N lost from fertilizer applications in corn (Zea mays L.), considerable public and industry attention has been devoted to the development of N decision tools. Needed are research and databases and associated metadata, at numerous locations and years to represent a wide geographic range of soil and weather scenarios, for evaluating tool performance. The goals of this research were to conduct standardized corn N rate response field studies to evaluate the performance of multiple public-domain N decision tools across diverse soils and environmental conditions, develop and publish new agronomic science for improved crop N management, and train new scientists. The geographic scope, scale, and unique collaborative arrangement warrant documenting details of this research. The objectives of this paper are to describe how the research was undertaken, reasons for the methods, and the project’s anticipated value. The project was initiated in a partnership between eight U.S. Midwest land-grant universities, USDA-ARS, and DuPont Pioneer. Research using a standardized protocol was conducted over the 2014 through 2016 growing seasons, yielding a total of 49 sites. Preliminary observations of soil and crop variables measured from each site revealed a magnitude of differences in soil properties (e.g., texture and organic matter) as well as differences in agronomic and economic responses to applied N. The project has generated a valuable dataset across a wide array of weather and soils that allows investigators to perform robust evaluation of N use in corn and N decision tools

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest