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

Relationships between crop yield and landscape features

Background: Sound agronomic recommendations are crucial for today's agronomists as they strive for improved yields, profits, and sustainability. Determining the spatial relationships between yield and landscape variation including soil properties, soil texture, and terrain attributes may improve management decisions, particularly with regards to proper nitrogen application for minimizing both costs to farmers and environmental impacts. Methods: Here we investigate relationships between landscape features and corn yield as part of a preliminary study to model corn yield with variations in landscape attributes, soil properties, and weather. We used yield monitor data collected from 2010- 2015 at a 12 ha field at the Davis Purdue University Agricultural Research Center in Randolph County, IN, USA We obtained 15 digital elevation-based models of terrain attributes that describe morphometric and hydrologic characteristics of the field. For each year we used the random forest method to select terrain attributes that were most important for predicting corn yield across the field. We performed cluster analysis with these variables to select the terrain attributes for our spatial regression models. Models, either the spatial error or the spatial lag model, were selected based on the lowest Akaike Information Criterion (AIC) score for the model. Results: The most important terrain attributes for predicting corn yield were topographic wetness index, topographic position index, relative slope position, catchment slope, and catchment area. Discussions: These results demonstrate that models for predicting corn yield in Indiana need to include landscape features for increased model performance. Conclusion: This analysis met one objective of a larger investigation that will incorporate soil properties, soil texture, and weather patterns into models of corn yield across Indiana landscapes

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

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

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

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

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

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