Effects of Removing Background Soil Reflectance Pixels from Vegetative Index Maps for Characterization of Corn Responses to Experimental Treatments

In contrast to traditional data collection methods that require manual sampling, vegetative index (VI) maps derived from unmanned aerial vehicles (UAV) imagery are a potential tool to characterize temporal and spatial treatment effects in a more efficient and non-destructive way. Remotely-sensed reflectance data from a growing corn crop contains pixel values associated with the above-ground plant tissue (e.g., leaves, stalks, tassels) and the underlying soil features. Background soil reflectance data potentially reduces the effectiveness of VI for characterizing crop responses to experimental treatments. Removing background soil image pixels from the larger image dataset should improve that effectiveness. The objective of this study was compare the effectiveness of filtered and non-filtered VI maps in characterizing phenotypic responses of corn to fertilizer treatments. Three large scale field trials (12 to 20 ha) involving either sulfur or nitrogen fertilizer treatments were used for the study. Imagery was collected using a DJI Matrice 200 series UAV equipped with either a consumer RGB camera or a camera modified to capture NIR. Flights were conducted at corn growth stages V6, V10, and R4. The individual images were stitched into orthomosaic and image postprocessing was performed to calculate RGB (400-700 nm), and near-IR (700 to 1100 nm) based VIs. After performing image classification to separate plant from soil pixels, soil background was removed, and vegetative index values corresponding only to the plants were considered for the next steps. Analysis of variance and treatment contrasts were performed using filtered and non-filtered datasets. Furthermore, a regression analysis was performed to investigate the feasibility of VIs to estimate grain yield. Results suggest that removing soil background improves the characterization of corn responses to experimental treatments visually and statistically. R2 values between grain yield and VIs increased up to 0.4 after filtering soil background

Pre-Plant Anhydrous Ammonia Placement Consequences on No-Till Versus Conventional-Till Maize Growth and Nitrogen Responses

With the advent of precision guidance systems, maize (Zea mays L.) farmers in various tillage systems have more options in pre-plant nutrient banding relative to the intended crop rows. Anhydrous ammonia (NH3) placement during pre-plant application is of interest because of concerns for possible ammonia toxicity to maize seedlings when high NH3 rates are applied too close to the seed row and the need to improve plant-nitrogen (N) use efficiencies. Field studies were conducted between 2010 and 2012 near West Lafayette, IN, to compare traditional angled (diagonally) vs. precision-guided parallel NH3 applications (the latter was offset 15 cm from the future row) in no-till and conventional tillage systems. The NH3 was injected to depths of about 12 cm at N rates of 145 and 202 kg N ha−1. Maize was planted with additional starter N (20 kg N ha−1) within 6 d of NH3 application. Neither NH3 application placement resulted in significant maize seedling mortality. Conventional tillage increased mean grain yields across N rates and placement treatments from 10.6 to 11.6 Mg ha−1. Tillage did not impact reproductive-stage leaf chlorophyll content (SPAD), or whole-plant N content at maturity when NH3 was parallel applied, but these plant responses were significantly lower in no-till after diagonal application. Lowering the pre-plant N rate to 145 from 202 kg N ha−1 significantly lowered maize whole-plant biomass and N accumulation at maturity with diagonal application, but not when NH3 was parallel applied

EVALUATION OF GENOTYPE BY ENVIRONMENT INTERACTIONS FROM UNREPLICATED MULTI-ENVIRONMENTAL TRIALS OF HYBRID MAIZE

Diverse soils and varying weather conditions not only affect overall performance of hybrid maize in multi-environment field studies, but can also cause strong genotype by environment interactions (GEI). Modern maize breeding experiments utilize multilocation trials with augmented field designs to evaluate the performance of unreplicated test hybrids. Augmented designs are resource efficient; however, these designs do not efficiently quantify or test GEI variation in the test hybrids. New methods are being developed that use random regression models to incorporate multiple environmental effects into GEI models to increase their accuracy and predictive ability. Incorporation of varying weather and soil physical variables into these models can be used to determine the potential causal factors of GEI. The identification of causal factors can assist in developing clusters of locations where homogenous performance of hybrids can be expected. The utility of the proposed approach is demonstrated with a real data analysis

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

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

Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 \u3c 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions

Soil hydrologic grouping guide which soil and weather properties best estimate corn nitrogen need

Nitrogen fertilizer recommendations in corn (Zea mays L.) that match the economically optimal nitrogen fertilizer rate (EONR) are imperative for profitability and minimizing environmental degradation. However, the amount of soil N available for the crop depends on soil and weather factors, making it difficult to know the EONR from year-to-year and from field-to-field. Our objective was to explore, within the framework of hydrologic soil groups and drainage classifications (HGDC), which site-specific soil and weather properties best estimated corn N needs (i.e., EONR) for two application timings (at-planting and side-dress). Included in this investigation was a validation step using an independent dataset. Forty-nine N response trials conducted across the U.S. Midwest Corn Belt over three growing seasons (2014–2016) were used for recommendation model development, and 181 independent site-years were used for validation. For HGDC models, soil organic matter (SOM), clay content, and evenness of rainfall distribution before side-dress N application were the properties generally most helpful in predicting EONR. Using the validation data, model recommendations were within 34 kg N ha–1 of EONR for 37 and 42% of the sites with a root mean square error (RMSE) of 70 and 68 kg N ha–1 for at-planting and side-dress applications, respectively. Compared to state-specific recommendations, sites needing ha–1 or no N were better estimated with HGDC models. In contrast, for sites where EONR was \u3e150 kg N ha–1, HGDC models underestimated N needs compared to state specific. These results show HGDC groupings could aid in developing tools for N fertilizer recommendations

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 organic matter (SOM) \u3c 3.9 g kg−1, or C to N ratios \u3c 11.0:1; otherwise, PMNan tended to be greater in fertilized vs. unfertilized soil. Longer incubation lengths increased PMNan at all sites regardless of sampling methods. Since PMNan is sensitive to many factors (sample timing, N fertilization, incubation length, soil properties, and weather conditions), it is important to follow a consistent protocol to compare PMNan among sites and potentially use PMNan to improve corn N management