The U.S. Midwest, a global hub for corn–soybean production, is becoming increasingly vulnerable to climate variability, nutrient losses, and the unsustainable use of surface and groundwater resources. This review examines how agro-hydrological modeling can help address these challenges and support sustainable agriculture in the region. We evaluate major crop models (e.g., DSSAT, APSIM, AquaCrop, EPIC) and hydrological models (e.g., SWAT, DRAINMOD, MODFLOW, VIC), applied separately and in combination, and summarize their use in assessing water quality and quantity, managing irrigation and drainage, and quantifying nutrient losses. Case studies using coupled systems, including DRAINMOD–DSSAT, APEX–HUMUS–SWAT, FEST-C coupled with EPIC–WRF–CMAQ–SWAT, and DSSAT–MODFLOW, show that integrated frameworks can simultaneously evaluate yields, evapotranspiration (ET), tile drainage, groundwater trends, and nitrate loads, thereby providing stronger decision support than stand-alone models. However, broader application in the Midwest remains constrained by the limited availability of field-scale calibration and validation data, complex calibration, equifinality, and simplified representations of key processes (such as tile hydraulics, capillary rise, and socio-economic feedbacks). Recent advances in artificial intelligence (AI), remote sensing (RS), and data assimilation (DA) offer new opportunities but are not yet widely incorporated into Midwest-specific decision-support systems. Overall, this review clarifies the limitations and added value of integrated crop–hydrological modeling and outlines priorities for more reliable, decision-oriented tools for the Corn Belt and Great Lakes regions.

Agricultural runoff losses of nitrate-nitrogen (NO3-N) can be treated by woodchip denitrifying bioreactors, but phosphorus (P) removal by this practice has been inconsistent. This study was conducted to test the NO3-N and P removal performance of a novel, modular, portable, downflow bioreactor design. One-meter cubic containers were packed for saturated downflow; influent passed through a layer of P-sorbing media followed by corncobs and woodchips. Three treatments for P removal included steel slag, crushed recycled concrete, and fragmented limestone. Potassium acetate (KCH3CO2) was used as an external carbon source to enhance denitrification. The field experiment continued for two seasons: May-December 2016 and March-July 2017. Mean hydraulic retention time (HRT) was 5.6 h during 2016 and 2.9 h in 2017. The mean annual N removal rate (NRR) among the three P-treatment materials was 2.84 g N m-3 d-1 in 2016 and 1.79 g N m-3 d-1 in 2017. During 2016 and 2017, when acetate was added to the bioreactors, NO3-N load reduction averaged 22.3 % and 14.1 %, respectively. Mean P removal rate (PRR) among the three P-treatment materials was 230 mg P m-3 d-1 in 2016 and 21.9 mg P m-3 d-1 in 2017. During some months bioreactors acted as a P source exhibiting a negative PRR and percent P loss. Overall, P treatment by bioreactors reduced P loss by 28.9 % in 2016 and increased P loss by 14.1 % in 2017 (mean P loss of 12.4 % across years). Results show that this bioreactor design, which may be installed directly under a drainage outlet, can be used as an alternative to classical denitrifying bioreactor beds for subsurface drainage water treatment of N and P, but the P sorbing materials would require removal and replacement on an annual basis.

Accurate prediction of soil moisture (SM) is crucial for applications in agriculture, hydrology, and climate modeling. Traditional data-driven machine learning (ML) approaches often require extensive labeled datasets and fail to incorporate the physical principles governing SM dynamics. In this study, we propose a novel science-guided learning framework to predict SM in the top 20 cm of soil using deep neural networks (DNNs). By integrating physical equations, such as Richards' equation, into the learning process, our approach ensures scientifically consistent predictions while improving model generalizability. Our empirical results show that the proposed graph-based model outperforms traditional ML approaches by more than 30% in accuracy, while predicting SM with an error of less than 5% compared to ground-truth in-situ measurements.

Accurate and cost-effective quantification of the agroecosystem carbon cycle at decision-relevant scales is essential for climate mitigation and sustainable agriculture. However, both transfer learning and the exploitation of spatial variability in this field are challenging, as they involve heterogeneous data and complex cross-scale dependencies. Conventional approaches often rely on location-independent parameterizations and independent training, underutilizing transfer learning and spatial heterogeneity in the inputs, and limiting their applicability in regions with substantial variability. We propose FTBSC-KGML (Fine-Tuning-Based Site Calibration-Knowledge-Guided Machine Learning), a pretraining- and fine-tuning-based, spatial-variability-aware, and knowledge-guided machine learning framework that augments KGML-ag with a pretraining-fine-tuning process and site-specific parameters. Using a pretraining-fine-tuning process with remote-sensing GPP, climate, and soil covariates collected across multiple midwestern sites, FTBSC-KGML estimates land emissions while leveraging transfer learning and spatial heterogeneity. A key component is a spatial-heterogeneity-aware transfer-learning scheme, which is a globally pretrained model that is fine-tuned at each state or site to learn place-aware representations, thereby improving local accuracy under limited data without sacrificing interpretability. Empirically, FTBSC-KGML achieves lower validation error and greater consistency in explanatory power than a purely global model, thereby better capturing spatial variability across states. This work extends the prior SDSA-KGML framework.

Estimating runoff at ground-mounted solar photovoltaic (PV) installations is challenging because of the disconnected nature of impervious solar panels and the pervious ground surface underneath and between panel rows. There is a need for improved tools to estimate how low impact development practices at these solar installations affect stormwater runoff. The objective of this study was to develop an innovative spreadsheet-based runoff calculator that rapidly estimates stormwater runoff from ground-mounted solar PV sites. The calculator is built on a 2-D hydrologic model (Hydrus-2D/3D) calibrated and validated using experimental data from five commercial solar farms in Colorado, Georgia, Minnesota, New York, and Oregon. The Hydrus-2D/3D hydrologic model was then used to generate nomographs for stormwater runoff that were incorporated into an easy-to-use Excel-based solar farm runoff calculator. This calculator allows for rapid estimation of NRCS stormwater runoff curve number (CN) values at solar farms by considering several complex factors unique to PV installations including: soil and topographic characteristics, surface cover, disconnected impervious surface factors associated with various solar panel designs, and climatic factors. The solar farm runoff calculator quickly estimates runoff CN for pre- and post-construction scenarios, and can estimate actual depth of runoff based on a user-specified 24-h design storm depth. Factors that have the most significant impact on stormwater runoff include design storm return frequency, soil texture, soil bulk density, and soil depth. Ground surface cover has a moderate impact on stormwater runoff, and factors that have a lesser impact on stormwater runoff include slope and array size, spacing and orientation on the landscape. The runoff calculator allows for accurate estimates of runoff generated by disconnected impervious surfaces and low impact development practices at solar farms as affected by a wide range of site-specific conditions.

AbstractGround‐mounted photovoltaic sites are often treated as impervious surfaces in stormwater permits. This ignores the pervious soils beneath and between solar arrays and leads to an overestimation of runoff. Our objective was to improve solar farm stormwater hydrology models by explicitly considering the disconnected impervious nature of solar design and site characteristics. Experimental sites established on utility scale solar farms in Colorado, Georgia, Minnesota, New York, and Oregon had perennial vegetative plantings with mean precipitation ranging from 40.6 to 124.5 cm, and soil texture ranging from loamy sand to clay. Soil moisture measurements were collected beneath arrays, under drip edges, and in the vegetated area between arrays at each site. Hydrus‐3D models for soil moisture and stormwater hydrology were developed that accounted for precipitation falling on solar panels, drip edge redistribution of rainfall, infiltration, and runoff in the pervious areas between solar arrays and beneath panels. Drip edge runoff averaged 3‐ to 10‐times incident precipitation at the New York and Minnesota sites, respectively. Root mean square error values between measured sub‐hourly soil moisture and predicted moisture for large measured single storm events averaged 0.029 across all five sites. Predicted runoff depths were strongly affected by precipitation depth, soil texture, soil profile depth, and soil bulk density. Runoff depths across the five experimental sites averaged 13%, 25%, and 45% of the 2‐, 10‐, and 100‐year design storm depths, clearly showing that these solar farms do not behave like impervious surfaces, but rather as disconnected impervious surfaces with substantial infiltration of runoff in the vegetated areas between and beneath solar arrays.

The soybean aphid (SBA), Aphis glycines Matsumura (Hemiptera: Aphididae), is a significant insect pest of soybean, Glycine max (L.) Merrill (Fabales: Fabaceae), and field treatment decisions for this pest are based on average field populations. Previous studies indicated that ground- and drone-based red-edge and near-infrared remote sensing can be used to detect plant stress caused by SBA infestations in soybean. However, it remains to be determined if remote sensing for SBA can be expanded to field or landscape scale using satellite-based platforms. Thus, this research was conducted in three steps to determine the potential of using Sentinel-2 satellite data for the classification of SBA infestations in soybean fields using simulated and actual Sentinel-2 satellite spectral reflectance. In the first step, as a proof of concept, hyperspectral data from cage studies were used to simulate Sentinel-2 bands and vegetation indices (VIs), conducted in nine trials at multiple locations between 2013 and 2021. The effects of SBA from caged plants on simulated data were evaluated with random intercept linear mixed models. The satellite simulation indicated a significant effect of SBA on the spectral reflectance of caged soybean plants (p < 0.05) for four satellite bands (5, 6, 7, and 8A) and five VIs (NDVI, GNDVI, SAVI, OSAVI, and NDRE). In the second step, actual Sentinel-2 spectral reflectance and corresponding aphid counts of commercial soybean fields, collected from 2017 to 2019, were obtained. The relationship between SBA counts and Sentinel-2 spectral reflectance from commercial soybean fields were evaluated with general linear models. A significant effect of SBA was observed for three satellite bands (6, 7, and 8A) and three VIs (NDVI, SAVI, and OSAVI). In the third step, linear support vector machine (LSVM) models for the classification of SBA infestations as above or below a previously determined economic threshold of 250 aphids per plant were developed using simulated Sentinel-2 bands and VIs from the caged plots, and were tested on actual Sentinel-2 data from commercial soybean fields. The best LSVM model for the classification of aphids in soybean reached 91% accuracy, 85.7% sensitivity, and 93.3% specificity. Thus, simulations with caged plots can be used as an indication of the potential of using satellite data for the detection of plant stresses on a larger scale. Furthermore, this study advances decision-making for SBA, and the developed LSVM model can be used to update regional and local monitoring for the management of SBA.

The olive tree holds great cultural, environmental, and economic significance in the Mediterranean region. In particular, Morocco has been making dedicated investments over $10 billion since 2008 to fuel the transition from cereal to olive production. Understanding the spatial extent of this large-scale land conversion is critical for a variety of socioeconomic purposes. In response to this demand, we conducted a study to map individual olive trees in northern Morocco using satellite imagery and deep learning techniques at a sub-national scale. This study utilized cloud-free, very-high-resolution DigitalGlobe imagery collected between 2018 and 2022 to identify each individual olive tree in six northern Morocco provinces. We compared various deep learning models, including both transformer-based and CNN-based models, to generate patch-level spatial constraints and pixel-level tree identification. We found that transformer-based models outperformed CNN-based models in both tasks. Additionally, spatially constraining the pixel-level results improved olive tree mapping accuracy to varying degrees, depending on the initial performance of the model. The evaluation of the olive map generated from this study shows high accuracy in both surveyed and unsampled regions. This research represents the first-of-its-kind individual olive tree mapping at the sub-national scale that can help monitor the large-scale land conversions such as about 110,000 ha of olive plantings in the six Moroccan provinces studies here. Meanwhile it demonstrates a cost-effective and efficient prototype approach that can be adapted to identify similar tree crop expansion occurring in other parts of the world.

Accurate and timely crop mapping is essential for yield estimation, insurance claims, and conservation efforts. Over the years, many successful machine learning models for crop mapping have been developed that use just the multi-spectral imagery from satellites to predict crop type over the area of interest. However, these traditional methods do not account for the physical processes that govern crop growth. At a high level, crop growth can be envisioned as physical parameters, such as weather and soil type, acting upon the plant leading to crop growth which can be observed via satellites. In this paper, we propose Weather-based Spatio-Temporal segmentation network with ATTention (WSTATT), a deep learning model that leverages this understanding of crop growth by formulating it as an inverse model that combines weather (Daymet) and satellite imagery (Sentinel-2) to generate accurate crop maps. We show that our approach provides significant improvements over existing algorithms that solely rely on spectral imagery by comparing segmentation maps and F1 classification scores. Furthermore, effective use of attention in WSTATT architecture enables detection of crop types earlier in the season (up to 5 months in advance), which is very useful for improving food supply projections. We finally discuss the impact of weather by correlating our results with crop phenology to show that WSTATT is able to capture physical properties of crop growth.

One of the challenges in site-specific phosphorus (P) management is the substantial spatial variability in plant available P across fields. To overcome this barrier, emerging sensing, data fusion, and spatial predictive modeling approaches are needed to accurately reveal the spatial heterogeneity of P. Seven spatially variable fields located in Ontario, Canada are clustered into two zones; four fields are located in eastern Ontario and three others are located in western Ontario. This study compares Bayesian Additive Regression Trees (BART), Support Vector Machine regressor (SVM), and Ordinary Kriging (OK), along with novel data fusion concepts, to analyze integrated high-density spatial data layers related to spatial variability in soil available P. Feature selection and interaction detection using BART variable selection and Recursive Feature Elimination (RFE) for SVM were applied to 42 predictors, including soil-vegetation indices derived from PlanetScope multispectral imagery, high-density apparent soil electrical conductivity (ECa), and high-resolution topographic attributes derived from DUALEM-21S and a Real-Time Kinematic (RTK) global navigation satellite systems (GNSS) receiver, respectively. Modeling spatial heterogeneity of soil available P with BART showed higher accuracy than SVM and OK in both zones of this study when trained and tested on ground truth data from clusters of farms. A BART variable selection approach resulted in six auxiliary predictors of soil available P in the eastern zone, while only four predictors were selected to predict P in the western zone. RFE for SVM resulted in models with 15 and 12 auxiliary predictors in the eastern and western Ontario zones. Topographic elevation was the most influential predictor of soil available P in both zones. Compared with the SVM and OK methods, BART exhibited lower average RMSE values for individual fields of 1.86 ppm and 3.58 ppm across the eastern and western Ontario zones, respectively, along with higher R2 values of 0.85 and 0.83, respectively. In contrast, SVM had RMSE values for individual fields in the eastern and western Ontario zones, respectively, averaging 5.04 ppm and 7.51 ppm and R2 values of 0.27 and 0.43. RMSE values for soil available P in individual fields across the eastern and western Ontario zones averaged 4.77 ppm and 7.81 ppm, respectively, with the OK method, while R2 values averaged 0.19 and 0.44. The selection of suitable auxiliary predictors and data fusion, combined with BART spatial machine learning algorithms, have potential to be a useful tool to accurately estimate spatial patterns in soil available P for agricultural fields in Ontario, Canada.

A suitable nitrogen (N) application rate (NAR) and ideal planting period could improve upland rice productivity, enhance the soil water utilization, and reduce N losses. This study was conducted for the assessment and application of the EPIC model to simulate upland rice productivity, soil water, and N dynamics under different NARs and planting windows (PWs). The nitrogen treatments were 30 (N30), 60 (N60), and 90 (N90) kg N ha−1 with a control (no N applied −N0). Planting was performed as early (PW1), moderately delayed (PW2), and delayed (PW3) between September and December of each growing season. The NAR and PW impacted upland rice productivity and the EPIC model predicted grain yield, aboveground biomass, and harvest index for all NARs in all PWs with a normalized good–excellent root mean square error (RMSEn) of 7.4–9.4%, 9.9–12.2%, and 2.3–12.4% and d-index range of 0.90–0.98, 0.87–0.94, and 0.89–0.91 for the grain yield, aboveground biomass, and harvest index, respectively. For grain and total plant N uptake, RMSEn ranged fair to excellent with values ranging from 10.3 to 22.8% and from 6.9 to 28.1%, and a d-index of 0.87–0.97 and 0.73–0.99, respectively. Evapotranspiration was slightly underestimated for all NARs at all PWs in both seasons with excellent RMSEn ranging from 2.0 to 3.1% and a d-index ranging from 0.65 to 0.97. A comparison of N and water balance components indicated that PW was the major factor impacting N and water losses as compared to NAR. There was a good agreement between simulated and observed soil water contents, and the model was able to estimate fluctuations in soil water contents. An adjustment in the planting window would be necessary for improved upland rice productivity, enhanced N, and soil water utilization to reduce N and soil water losses. Our results indicated that a well-calibrated EPIC model has the potential to identify suitable N and seasonal planting management options.

There is growing interest in studying the impact of alternative agricultural management practices on runoff and soil loss under future climate change scenarios. In order to address this interest, it is important to demonstrate that runoff and soil loss can be accurately simulated under existing climates based on comparisons between modeled and experimental results. This study calibrates and validates the Water Erosion Prediction Project (WEPP) model to quantify the accuracy of predicting growing season runoff and soil erosion in agricultural hillslopes based on comparisons with experimental data from five Minnesota hydrologic unit code 12 watersheds. In order to accurately predict runoff and soil erosion in each watershed, the baseline effective hydraulic conductivity ( K be ), interrill and rill erodibility ( E IR and E R ), and monthly precipitation standard deviations ( P stdev ) were calibrated in WEPP using observed runoff and total suspended solids data from five Minnesota Discovery Farms field sites. Before calibration, Nash–Sutcliffe model efficiency (NSE) and percent bias (PBIAS) values for predicted versus measured monthly average total runoff ( R avg‐T ), runoff ratios (RR T ), and total soil loss were generally not in acceptable ranges. After calibration, the NSE values showed very good fits between measured and predicted monthly R avg‐T (0.64–0.98), RR T (0.66–0.93), and soil loss (0.58–0.80). PBIAS values were also within acceptable ranges for R avg‐T and RR T (±25%) and soil loss (±55%), except for RR T at site BE1. NSE and PBIAS values during validation were within acceptable ranges, except for RR T at site BE1. These findings suggest that the WEPP hillslopes calibrated in this study are sufficiently robust to accurately predict monthly runoff and soil erosion in Minnesota agricultural fields during the growing season.

Several newly released crop varieties, including the perennial intermediate wheatgrass (grain marketed as Kernza®), and the winter hardy oilseed crop camelina, have been developed to provide both economic return for farmers and reduced nutrient losses from agricultural fields. Though studies have indicated that these crops could reduce nitrate-nitrogen (N) leaching, little research has been done to determine their effectiveness in reducing nitrate-N loading to surface waters at a watershed scale, or in comparing their performance to more traditional perennial crops, such as alfalfa. In this study, nitrate-N losses were predicted using the Soil and Water Assessment Tool (SWAT) model for the Rogers Creek watershed located in south-central Minnesota, USA. Predicted looses of nitrate-N under three perennialized cropping systems were compared to losses given current cropping practices in a corn ( Zea mays L.)-soybean ( Glycine max L. Merr.) rotation. The perennialized systems included three separate crop rotations: intermediate wheatgrass (IWG) in rotation with soybean, alfalfa in rotation with corn, and winter camelina in rotation with soybean and winter rye. Model simulation of these rotations required creation of new crop files for IWG and winter camelina within SWAT. These new crop files were validated using measured yield, biomass, and nitrate-N data. Model results show that the IWG and alfalfa rotations were particularly effective at reducing nutrient and sediment losses from agricultural areas in the watershed, but smaller reductions were also achieved with the winter camelina rotation. From model predictions, achieving regional water-quality goals of a 30% reduction in nitrate-N load from fields in the watershed required converting approximately 25, 34, or 57% of current corn-soybean area to the alfalfa, IWG, or camelina rotations, respectively. Results of this study indicate that adoption of these crops could achieve regional water quality goals.

Nitrogen (N) deficiency can limit rice productivity, whereas the over- and underapplication of N results in agronomic and economic losses. Process-based crop models are useful tools and could assist in optimizing N management, enhancing the production efficiency and profitability of upland rice production systems. The study evaluated the ability of CSM–CERES–Rice to determine optimal N fertilization rate for different sowing dates of upland rice. Field experimental data from two growing seasons (2018–2019 and 2019–2020) were used to simulate rice responses to four N fertilization rates (N30, N60, N90 and a control–N0) applied under three different sowing windows (SD1, SD2 and SD3). Cultivar coefficients were calibrated with data from N90 under all sowing windows in both seasons and the remaining treatments were used for model validation. Following model validation, simulations were extended up to N240 to identify the sowing date’s specific economic optimum N fertilization rate (EONFR). Results indicated that CSM–CERES–Rice performed well both in calibration and validation, in simulating rice performance under different N fertilization rates. The d-index and nRMSE values for grain yield (0.90 and 16%), aboveground dry matter (0.93 and 13%), harvest index (0.86 and 7%), grain N contents (0.95 and 18%), total crop N uptake (0.97 and 15%) and N use efficiencies (0.94–0.97 and 11–15%) during model validation indicated good agreement between simulated and observed data. Extended simulations indicated that upland rice yield was responsive to N fertilization up to 180 kg N ha−1 (N180), where the yield plateau was observed. Fertilization rates of 140, 170 and 130 kg N ha−1 were identified as the EONFR for SD1, SD2 and SD3, respectively, based on the computed profitability, marginal net returns and N utilization. The model results suggested that N fertilization rate should be adjusted for different sowing windows rather than recommending a uniform N rate across sowing windows. In summary, CSM–CERES–Rice can be used as a decision support tool for determining EONFR for seasonal sowing windows to maximize the productivity and profitability of upland rice production.

The Daily Erosion Project (DEP) enables daily estimation of sheet and rill erosion across a large area in the US Midwest. However, DEP currently omits a potentially important source of erosion in the region by not considering wind erosion. In the work outlined here, we incorporated wind erosion into DEP by linking it with the Single-event Wind Erosion Evaluation Program (SWEEP). In this new integrated model known as DEP-SWEEP (Figure 1), daily predictions are made for rill, sheet, and wind erosion from farm fields in the US Midwest, given differing land-cover, weather, and land management conditions. Daily wind erosion estimates can be generated for one year‘s worth of climate and management conditions in DEP-SWEEP. Some parameters from DEP are used directly as input parameters into SWEEP (e.g. soil textural parameters), however others (including soil surface and aggregate parameters) needed to be calculated. Equations used in these calculations were based primarily on the theoretical processes of the NRCS‘s Wind Erosion Prediction (WEPS) model. Other important calculations added to the DEP-SWEEP model allow for estimation of residue and growing plant and plant biomass cover. Initially, DEP-SWEEP was run for three Hydrologic Unit Code 12 (HUC12) watershed test sites located in Nebraska and Minnesota on a daily basis for the year 2021. Soil types for these HUC12s included silty-loam and sandy-loam soils, and all HUC12s were planted in a corn-soybean crop rotation, with high-mulch tillage, and no wind barriers present. Results of these simulations showed realistic calculation of some soil surface parameters; however, wind erosion was only generated in one HUC 12 on one day. This result may not be representative of actual conditions given poor calculation of some key soil aggregate parameters. Additional planned work on the DEP-SWEEP model includes updating algorithms to account for climate and management practices for soil aggregate parameters, parameterization to allow for simulation of more field management practices, and simulation of more HUC12s in the US Midwest region.

Perennial grain crops are a potential alternative source of staple foods and animal forage that can also provide additional environmental benefits over annual crops. Intermediate wheatgrass (IWG; Thinopyrum intermedium ) is a new perennial dual-use crop for grain and forage, with growing interest among stakeholders as it produces grain in a more environmentally sound manner than current annual crops. DSSAT model simulations were performed for maize and a new DSSAT model for IWG based on data collected from field studies conducted during 2013–2015 at three different locations, i.e., Lamberton, Waseca and Crookston using low (zero), medium (60–80 kg ha −1 ) and high fertilizer nitrogen (N) rates (120–160 kg ha −1 ). The DSSAT CERES-Maize and CROPGRO-PFM models used as the basis for simulating IWG were calibrated at the high N rate to predict the yield/biomass, soil water balance, and soil nitrogen balance in maize and IWG, respectively, for the medium and low N rate treatments. Model predictions for maize yield and IWG biomass (0.89 >= Nash Sutcliffe Efficiency >= 0.58), soil profile moisture (0.81 >=NSE>=0.53) ranged from very good to satisfactory for maize and the high N rate in IWG, with nearly satisfactory accuracy for IWG under the medium and zero N rates. Simulation results indicate that low, medium and high N rates produced an average IWG biomass of 7.8, 9.7, and 10.5 t ha −1 , in addition to observed grain yield of 0.36, 0.49, and 0.45 t ha −1 , respectively. The corresponding N rates produced 5.9, 7.9, and 8.7 t ha −1 maize yield. Soil profile moisture under IWG and maize averaged 0.25 and 0.29 m 3 m −3 , respectively. Averaged over N rates and locations, IWG and maize had values for crop evapotranspiration (ET c ) of 592 vs. 517 mm; deep percolation of 100.8 vs. 154.5 mm; and nitrate-N leaching losses of 2.6 vs. 17.9 kg ha −1 , respectively. Results indicate that perennial IWG not only produced high biomass under rainfed conditions, but also reduced deep percolation by efficiently using soil profile moisture, leading to nitrate-N leaching losses six to seven times lower than for maize.

2-Methyl-4-chlorophenoxyacetic acid (MCPA) is a highly mobile herbicide that is frequently detected in global potable water sources. One potential mitigation strategy is the sorption on biochar to limit harm to unidentified targets. However, irreversible sorption could restrict bioefficacy thereby compromising its usefulness as a vital crop herbicide. This research evaluated the effect of pyrolysis temperatures (350, 500 and 800°C) on three feedstocks; poultry manure, rice hulls and wood pellets, particularly to examine effects on the magnitude and reversibility of MCPA sorption. Sorption increased with pyrolysis temperature from 350 to 800°C. Sorption and desorption coefficients were strongly corelated with each other (R2 = 0.99; P < .05). Poultry manure and rice hulls pyrolyzed at 800°C exhibited irreversible sorption while for wood pellets at 800°C desorption was concentration dependent. At higher concentrations some desorption was observed (36% at 50 ppm) but was reduced at lower concentrations (1-3% at < 5 ppm). Desorption decreased with increasing pyrolysis temperature. Sorption data were analyzed with Langmuir, Freundlich, Dubinin-Radushkevich and Temkin isotherm models. Freundlich isotherms were better predictors of MCPA sorption (R2 ranging from 0.78 to 0.99). Poultry manure and rice hulls when pyrolyzed at higher temperatures (500 and 800°C) could be used for remediation efforts (such as spills or water filtration), due to the lack of desorption observed. On the other hand, un-pyrolyzed feedstocks or biochars created at 350°C could perform superior for direct field applications to limit indirect losses including runoff and leaching, since these materials also possess the ability to release MCPA subsequently to potentially allow herbicidal action.

Multiple critical N dilution curves [CNDCs] have been previously developed for potato; however, attempts to directly compare differences in CNDCs across genotype [G], environment [E], and management [M] interactions have been confounded by non-uniform statistical methods, biased experimental data, and lack of proper quan-tification of uncertainty in the critical N concentration [%Nc]. This study implements a partially-pooled Bayesian hierarchical method to develop CNDCs for previously published and newly reported experimental data, sys-tematically evaluates the difference in %Nc [Delta%Nc] across G x E x M effects, and directly compare CNDCs from the Bayesian framework to CNDCs from conventional statistical methods. The partially-pooled Bayesian hier-archical method implemented in this study has the advantage of being less susceptible to inferential bias at the level of individual G x E x M interactions compared to alternative statistical methods that result from insuffi-cient quantity and quality of experimental datasets (e.g., unbalanced distribution of N limiting and non-N limiting observations). This method also allows for a direct statistical comparison of differences in %Nc across levels of the G x E x M interactions. Where found to be significant, Delta%Nc was hypothesized to be related to variation in the timing of tuber initiation (e.g., maturity class) and the relative rate of tuber bulking (e.g., planting density) across G x E x M interactions. In addition to using the median value for %Nc (i.e., CNDC), the lower and upper boundary values for the credible region (i.e., CNDClo and CNDCup) derived using the Bayesian framework should be used in calculation of N nutrition index (and other calculations) to account for uncertainty in %Nc. Overall, this study provides additional evidence that%Nc is dependent upon G x E x M interactions; therefore, evaluation of crop N status or N use efficiency must account for variation in %Nc across G x E x M interactions.

Highlights Four irrigation scheduling methods were compared for maize yield vs nitrate-N losses. Yield was similar with SM, CB, and EPIC, and lower for IMA irrigation scheduling. Nitrate-N leaching losses were highly dependent on precipitation as well as on the rate and frequency of irrigation. IMA and EPIC methods should be adopted with further calibration. ABSTRACT. Coarse-textured soils in the Great Lakes states have limited water holding capacity, which makes irrigation essential to obtain optimum yields for maize; however, excess irrigation has the potential to contaminate ground and surface water resources through deep seepage of applied fertilizers, making irrigation management critical. The objective of this study was to evaluate the impact of different strategies for agricultural irrigation water management on maize grain yield and nitrate leaching losses. In this three-year study (2019-2021), the following four irrigation scheduling strategies were compared under continuous maize cropping systems at two sites in Central Minnesota, Becker and Westport: (1) soil moisture monitoring using soil moisture sensors (SM), (2) checkbook method of irrigation scheduling (CB), (3) irrigation management assistant tool (IMA), and (4) EPIC model auto-irrigation (EPIC). Overall, in comparison to the CB method (highest irrigation method), the SM, IMA, and EPIC methods recommended 2.58%, 51.05%, and 9.27% less water in irrigation, respectively. In terms of grain yield, no significant differences were observed between irrigation treatments at the Becker site. The average maize grain yield at the Becker site was 12.02, 11.97, 11.76, and 10.62 Mg/ha, for SM, CB, EPIC, and IMA treatments, respectively. However, in the drier 2021 season, a grain yield loss of 30%, 27%, and 21% was observed under IMA when compared to SM, CB, and EPIC methods, respectively, at the Becker site. At the Westport site, on average, no significant difference in yield was obtained between SM, CB, and EPIC, however, yield under IMA treatment was significantly lower (12%) than CB. The results suggest that nitrate leaching losses can be significantly reduced by altering the rate and frequency of irrigation. IMA treatment resulted in significantly lower nitrate leaching (62%) than the CB method, reducing nitrate leaching from 35.86 kg/ha to 13.71 kg/ha at the Westport site. No significant differences in nitrate leaching were observed at the Becker site because the impact of precipitation superseded the impact of irrigation. Results from this study can aid in the adoption and further development of less labor-intensive and more accurate methods of irrigation scheduling in coarse-textured soils of Great Lakes states. Keywords: Coarse textured soils, Crop evapotranspiration, Irrigation scheduling methods, Maize grain yield, Nitrate-N leaching, Water quality, Water quantity.