Climate trends and maize production nexus in Mississippi: empirical evidence from ARDL modelling

Climate change poses a significant threat to agriculture. However, climatic trends and their impact on Mississippi (MS) maize (Zea mays L.) are unknown. The objectives were to: (i) analyze trends in climatic variables (1970 to 2020) using Mann-Kendall and Sen slope method, (ii) quantify the impact of climate change on maize yield in short and long run using the auto-regressive distributive lag (ARDL) model, and (iii) categorize the critical months for maize-climate link using Pearson's correlation matrix. The climatic variables considered were maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), precipitation (PT), relative humidity (RH), and carbon emissions (CO2). The pre-analysis, post-analysis, and model robustness statistical tests were verified, and all conditions were met. A significant upward trend in Tmax (0.13 °C/decade), Tmin (0.27 °C/decade), and CO2 (5.1 units/decade), and a downward trend in DTR ( - 0.15 °C/decade) were noted. The PT and RH insignificantly increased by 4.32 mm and 0.11% per decade, respectively. The ARDL model explained 76.6% of the total variations in maize yield. Notably, the maize yield had a negative correlation with Tmax for June, and July, with PT in August, and with DTR for June, July, and August, whereas a positive correlation was noted with Tmin in June, July, and August. Overall, a unit change in Tmax reduced the maize yield by 7.39% and 26.33%, and a unit change in PT reduced it by 0.65% and 2.69% in the short and long run, respectively. However, a unit change in Tmin, and CO2 emissions increased maize yield by 20.68% and 0.63% in the long run with no short run effect. Overall, it is imperative to reassess the agronomic management strategies, developing and testing cultivars adaptable to the revealed climatic trend, with ability to withstand severe weather conditions in ensuring sustainable maize production.

Effect of continuous sugarcane bagasse-derived biochar application on rainfed cotton (Gossypium hirsutum L.) growth, yield and lint quality in the humid Mississippi delta

Optimizing soil health through soil amendments is a promising strategy for enhancing rainwater efficiency for stabilizing crop production. Biochar, obtained by torrefaction of sugarcane bagasse, a byproduct from sugar mills, has a high potential for its use as a soil amendment, which can boost crop yields, but needs further field trials for its adoption in farming systems. A field study was conducted during 2019-2021 at Stoneville, Mississippi, to assess rainfed cotton (Gossypium hirsutum L.) production under four biochar levels (0, 10, 20, and 40 t ha-1) on Dundee silt loam soil. The effects of biochar on cotton growth and lint yield and quality were examined. Biochar levels had no significant impact on cotton lint and seed yield for the first two years. Still, in the third year, a significant increase in lint yield by 13 and 21.7% was recorded at 20 and 40 t ha-1 biochar levels, respectively. In the third year, lint yields were 1523, 1586, 1721, and 1854 kg ha-1 at 0, 10, 20 and 40 t ha-1 biochar levels, respectively. Similarly, cotton seed yield increased by 10.8% and 13.4% in 20 and 40 t ha-1 biochar plots. This study demonstrated that successive biochar applications at 20 or 40 t ha-1 can enhance cotton lint and seed yields under rainfed conditions. These improved yields with biochar did not produce increased net returns due to the increased production costs. Many lint quality parameters were unaffected except for micronaire, fiber strength and fiber length. However, potential long-term benefits of enhanced cotton production from biochar application beyond the length of the study merit further investigation. Additionally, biochar application is more relevant when accrued carbon credits through carbon sequestration outweigh the increased production costs due to biochar application.

A Review on Interpretable and Explainable Artificial Intelligence in Hydroclimatic Applications

This review focuses on the use of Interpretable Artificial Intelligence (IAI) and eXplainable Artificial Intelligence (XAI) models for data imputations and numerical or categorical hydroclimatic predictions from nonlinearly combined multidimensional predictors. The AI models considered in this paper involve Extreme Gradient Boosting, Light Gradient Boosting, Categorical Boosting, Extremely Randomized Trees, and Random Forest. These AI models can transform into XAI models when they are coupled with the explanatory methods such as the Shapley additive explanations and local interpretable model-agnostic explanations. The review highlights that the IAI models are capable of unveiling the rationale behind the predictions while XAI models are capable of discovering new knowledge and justifying AI-based results, which are critical for enhanced accountability of AI-driven predictions. The review also elaborates the importance of domain knowledge and interventional IAI modeling, potential advantages and disadvantages of hybrid IAI and non-IAI predictive modeling, unequivocal importance of balanced data in categorical decisions, and the choice and performance of IAI versus physics-based modeling. The review concludes with a proposed XAI framework to enhance the interpretability and explainability of AI models for hydroclimatic applications.

Water in the West: Trends, production efficiency, and a call for open data

Climate change is projected to transform US agriculture, particularly in places reliant on limited irrigation water resources. As water demand and scarcity increase simultaneously over the coming decades, water managers and growers will need to optimize water use on their irrigated lands. Understanding how growers maintain high yields in arid, water stressed places, while conserving water, is of key importance for the future of US agriculture in the West. We explore water use management and trends in irrigated agriculture in the Western US using operator-level USDA-NASS Farm and Ranch Irrigation Survey/Irrigation and Water Management Survey data aggregated for the first time to the county-scale. In this exploration, we build the first county-level, openly accessible dataset linking farm(er) characteristics to irrigation behaviors in the West. We find notable spatial and temporal variability in Western irrigation practices, with neighboring counties exhibiting large differences in efficiency, water use, and crop yields, as well as in the sources of information, scheduling methods, and technological improvements employed. To produce effective management initiatives in the West, we call for the express and open dissemination of USDA irrigation data at sub-state scales. These data will contribute to our understanding of irrigated production and could support a pathway that will prepare growers for a more resilient agricultural future.

Seed Germination Behavior, Growth, Physiology and Antioxidant Metabolism of Four Contrasting Cultivars under Combined Drought and Salinity in Soybean

Drought and salinity stresses are persistent threat to field crops and are frequently mentioned as major constraints on worldwide agricultural productivity. Moreover, their severity and frequency are predicted to rise in the near future. Therefore, in the present study we investigated the mechanisms underlying plant responses to drought (5, 10 and 15% polyethylene glycol, PEG-6000), salinity (50, 100, and 150 mM NaCl), and their combination, particularly at the seed germination stage, in terms of photosynthesis and antioxidant activity, in four soybean cultivars, viz., PI408105A (PI5A), PI567731 (PI31), PI567690 (PI90), and PI416937 (PI37). Results showed that seed germination was enhanced by 10% PEG and decreased by 15% PEG treatments compared to the control, while seed germination was drastically decreased under all levels of NaCl treatment. Furthermore, combined drought and salinity treatment reduced plant height and root length, shoot and root total weights, and relative water content compared with that of control. However, the reductions were not similar among the varieties, and definite growth retardations were observed in cultivar PI5A under drought and in PI37 under salinity. In addition, all treatments resulted in substantially reduced contents of chlorophyll pigment, anthocyanin, and chlorophyll fluorescence; and increased lipid peroxidation, electrolyte leakage, and non-photochemical quenching in all varieties of soybean as compared to the control plants. However, proline, amino acids, sugars, and secondary metabolites were increased with the drought and salinity stresses alone. Moreover, the reactive oxygen species accumulation was accompanied by improved enzymatic antioxidant activity, such as that of superoxide dismutase, peroxidase, catalase, and ascorbate peroxidase. However, the enhancement was most noticeable in PI31 and PI90 under both treatments. In conclusion, the cultivar PI31 has efficient drought and salinity stress tolerance mechanisms, as illustrated by its superior photosynthesis, osmolyte accumulation, antioxidative enzyme activity, and secondary metabolite regulation, compared to the other cultivars, when stressed.

Water Use Efficiencies of Different Maturity Group Soybean Cultivars in the Humid Mississippi Delta

Introducing alternative cultivars with enhanced water use efficiencies can help alleviate pressure on groundwater for crop irrigations in Mississippi (MS) Delta. A two-year field study was conducted in 2019–2020 to compare the water use efficiencies (WUE) of recently released and pre-released soybean {Glycine max (L.) Merr.} cultivars in maturity group (MG) III (‘P37A78’, ‘LG03-4561-14’), IV (‘Dyna-gro 4516x’, ‘DS25-1, DT97-4290’), and V (‘S12-1362’, ‘S14-16306’) in the MS Delta. The experimental design was a split-plot with cultivar as the first factor and the second factor was water variant irrigation (IR) and no irrigation (RF, rainfed), replicated three times. The MG IV cultivar Dyna-gro 4516x recorded the highest grain yield and WUE: grain yields were 4.58 Mg ha−1 and 3.89 Mg ha−1 under IR and RF, respectively in 2019, and 4.74 Mg ha−1 and 4.35 Mg ha−1 in 2020. The WUE were 7.2 and 6.9 kg ha−1 mm−1, respectively, in 2019 under IR and RF, and 13.4 and 16.9 kg ha−1 mm−1 in 2020. The data reveals that ‘Dyna-gro 4516x’ (MG IV), ‘LG03-4561-14’ (MG III), and ‘P37A78’ (MG III) are best adapted to the early soybean production system (ESPS) in MS Delta region for sustainable production for conserving water resources.

Exploring the Regional Dynamics of U.S. Irrigated Agriculture from 2002 to 2017

The United States has a geographically mature and stable land use and land cover system including land used as irrigated cropland; however, changes in irrigation land use frequently occur related to various drivers. We applied a consistent methodology at a 250 m spatial resolution across the lower 48 states to map and estimate irrigation dynamics for four map eras (2002, 2007, 2012, and 2017) and over four 5-year mapping intervals. The resulting geospatial maps (called the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset or MIrAD-US) involved inputs from county-level irrigated statistics from the U.S. Department of Agriculture, National Agricultural Statistics Service, agricultural land cover from the U.S. Geological Survey National Land Cover Database, and an annual peak vegetation index derived from expedited MODIS satellite imagery. This study investigated regional and periodic patterns in the amount of change in irrigated agriculture and linked gains and losses to proximal causes and consequences. While there was a 7% overall increase in irrigated area from 2002 to 2017, we found surprising variability by region and by 5-year map interval. Irrigation land use dynamics affect the environment, water use, and crop yields. Regionally, we found that the watersheds with the largest irrigation gains (based on percent of area) included the Missouri, Upper Mississippi, and Lower Mississippi watersheds. Conversely, the California and the Texas–Gulf watersheds experienced fairly consistent irrigation losses during these mapping intervals. Various drivers for irrigation dynamics included regional climate fluctuations and drought events, demand for certain crops, government land or water policies, and economic incentives like crop pricing and land values. The MIrAD-US (Version 4) was assessed for accuracy using a variety of existing regionally based reference data. Accuracy ranged between 70% and 95%, depending on the region.

The importance of slow canopy wilting in drought tolerance in soybean

Slow canopy wilting (SW) is a water conservation trait controlled by quantitative trait loci (QTLs) in late maturity group soybeans [Glycine max (L.) Merr.]. Recently, two exotic (landraces) plant introductions (PI 567690 and PI 567731) were identified as new SW lines in early maturity groups. Here, we show that the two PIs share the same water conservation strategy of limited maximum transpiration rates as PI 416937. However, in contrast to PI 416937, the transpiration rates of these PIs were sensitive to an aquaporin inhibitor, indicating an independence between limited maximum transpiration and the lack of silver-sensitive aquaporins. Yield tests of selected recombinant inbred lines from two elite/exotic crosses provide direct evidence to support the benefit of SW in drought tolerance. Four SW QTLs mapped in a Pana×PI 567690 cross at multiple environments were found to be co-located with previous reports. Moreover, two new SW QTLs were mapped on chromosomes 6 and 10 from a Magellan×PI 567731 cross. These two QTLs explain the observed relatively large contributions of 20-30% and were confirmed in a near-isogenic background. These findings demonstrate the importance of SW in yield protection under drought and provide genetic resources for improving drought tolerance in early maturity group soybeans.

Maize Yield and Irrigation Applied in Conservation and Conventional Tillage at Various Plant Densities

Maize is one of the most highly produced crops around the world. Factors such as population density, solar radiation, temperature, availability of nutrients and water, and proper tillage method can have favorable results in increasing yield. This project began in 2015, at two different locations in Georgia (Tifton and Camilla), GA, USA, and has been evaluated for three consecutive maize growing seasons. In each location, a different irrigation method was applied; the University of Georgia (UGA) maize checkbook method was utilized in Tifton and a soil moisture sensor-based method was utilized in Camilla. The different treatments consisted of two tillage methods, conservation and conventional, and four plant densities (69K, 88K, 99K, and 133K plants/ha). The purpose of the project was the evaluation of water requirements by population and tillage method for achieving high maize yields. Soil moisture information was collected hourly in both fields. The results showed that higher plant densities do not necessarily require higher irrigation amounts and do not always have the best results in terms of yield. Conventional tillage had slightly better yield results but not statistically different from conservation tilled plots. Additionally, the results showed that applied irrigation can help in higher maize production. However, increasing the amount of water does not directly equate to higher yield results.

Simulating Potential Weekly Stream and Pond Water Available for Irrigation in the Big Sunflower River Watershed of Mississippi Delta

Groundwater storage and level have declined rapidly in the Big Sunflower River Watershed (BSRW) of Mississippi Delta in recent years. Farmers in this region are beginning to save groundwater resources by increasing surface water usage for agricultural irrigation. In this study, we estimated the weekly amount of surface water available in ponds and streams, determined if the weekly surface water resources are sufficient for major crop irrigation demand, and assessed how much surface water can replace groundwater for agriculture irrigation in the BSRW. The SWAT (Soil and Water Assessment Tool) model was employed to simulate the weekly water resources for 23 sub-basins from the BSRW. Results showed that weekly stream water resources (SWR), stream evaporation (SE) and water loss from the channel via transmission through the side and bottom of the channel (stream transmission, referred as ST) for BSRW during the growing seasons ranged from 20.4 to 29.4 mm, 7.4 to 14.4 mm, 1.6 to 4.5 mm and 1.1 to 1.6 mm, while pond water resources (PWR) and pond evaporation (PE) ranged from 1.9 to 2.1 mm and 0.3 to 0.5 mm. The value of SWR − (ST + SE) and PWR − PE were positive in all sub-basins, indicating that there are net surface water resources available in this region. The percentages of total groundwater usage for irrigation that could be replaced by surface water in each sub-basin every week of each month from May to September ranged from 10% to 87%. Our studies suggested that the conjunctive use of surface and groundwater for agriculture irrigation is a feasible method for groundwater sustainable management in the Mississippi Delta.

Quantifying water and CO2 fluxes and water use efficiencies across irrigated C3 and C4 crops in a humid climate

Underground aquifers that took millions of years to fill are being depleted due to unsustainable water withdrawals for crop irrigation. Concurrently, atmospheric warming due to anthropogenic greenhouse gases is enhancing demands for water inputs in agriculture. Accurate information on crop-ecosystem water use efficiencies [EWUE, amount of CO2 removed from the soil-crop-air system per unit of water used in evapotranspiration (ET)] is essential for developing environmentally and economically sustainable water management practices that also help account for CO2, the most abundant of the greenhouse gases, exchange rates from cropping systems. We quantified EWUE of corn (a C4 crop) and soybean and cotton (C3 crops) in a predominantly clay soil under humid climate in the Lower Mississippi (MS) Delta, USA. Crop-ecosystem level exchanges of CO2 and water from these three cropping systems were measured in 2017 using the eddy covariance method. Ancillary micrometeorological data were also collected. On a seasonal basis, all three crops were net sinks for CO2 in the atmosphere: corn, soybean, and cotton fixed -31,331, -23,563, and -8856 kg ha-1 of CO2 in exchange for 483, 552, and 367 mm of ET, respectively (negative values show that CO2 is fixed in the plant or removed from the air). The seasonal NEE estimated for cotton was 72% less than corn and 62% less than soybean. Half-hourly averaged maximum net ecosystem exchange (NEE) from these cropping systems were -33.6, -27.2, and -14.2 kg CO2 ha-1, respectively. Average daily NEE were -258, -169, and -65 kg CO2 ha-1, respectively. The EWUE in these three cropping systems were 53, 43, and 24 kg CO2 ha-1 mm-1 of water. Results of this investigation can help in adopting crop mixtures that are environmentally and economically sustainable, conserving limited water resources in the region.

Methane Emission Reductions from the Alternate Wetting and Drying of Rice Fields Detected Using the Eddy Covariance Method

Rice cultivation contributes 11% of the global 308 Tg CH₄ anthropogenic emissions. The alternate wetting and drying (AWD) irrigation practice can conserve water while reducing CH₄ emissions through the deliberate, periodic introduction of aerobic soil conditions. This paper is the first to measure the impact of AWD on rice field CH₄ emissions using the eddy covariance (EC) method. This method provides continuous, direct observations over a larger footprint than in previous chamber-based approaches. Seasonal CH₄ emissions from a pair of adjacent, production-sized rice fields under delayed flood (DF) and AWD irrigation were compared from 2015 to 2017. Across the 2 fields and 3 years, cumulative CH₄ emissions in the production season were in the range of 7.1 to 31.7 kg CH₄-C ha^-1 for the AWD treatment and in the range of 75.7-141.6 kg CH₄-C ha^-1 for the DF treatments. Correcting for field-to-field differences in CH₄ production, the AWD practice reduced seasonal CH₄ emissions by 64.5 ± 2.5%. The AWD practice is increasingly implemented for water conservation in the mid-south region of the United States; however, based on this study, it also has great potential for reducing CH₄ emissions.

Performance Assessment of Five Different Soil Moisture Sensors under Irrigated Field Conditions in Oklahoma

Meeting the ever-increasing global food, feed, and fiber demands while conserving the quantity and quality of limited agricultural water resources and maintaining the sustainability of irrigated agriculture requires optimizing irrigation management using advanced technologies such as soil moisture sensors. In this study, the performance of five different soil moisture sensors was evaluated for their accuracy in two irrigated cropping systems, one each in central and southwest Oklahoma, with variable levels of soil salinity and clay content. With factory calibrations, three of the sensors had sufficient accuracies at the site with lower levels of salinity and clay, while none of them performed satisfactorily at the site with higher levels of salinity and clay. The study also investigated the performance of different approaches (laboratory, sensor-based, and the Rosetta model) to determine soil moisture thresholds required for irrigation scheduling, i.e., field capacity (FC) and wilting point (WP). The estimated FC and WP by the Rosetta model were closest to the laboratory-measured data using undisturbed soil cores, regardless of the type and number of input parameters used in the Rosetta model. The sensor-based method of ranking the readings resulted in overestimation of FC and WP. Finally, soil moisture depletion, a critical parameter in effective irrigation scheduling, was calculated by combining sensor readings and FC estimates. Ranking-based FC resulted in overestimation of soil moisture depletion, even for accurate sensors at the site with lower levels of salinity and clay.

Growing season variability in carbon dioxide exchange of irrigated and rainfed soybean in the southern United States

Measurement of carbon dynamics of soybean (Glycine max L.) ecosystems outside Corn Belt of the United States (U.S.) is lacking. This study examines the seasonal variability of net ecosystem CO₂ exchange (NEE) and its components (gross primary production, GPP and ecosystem respiration, ER), and relevant controlling environmental factors between rainfed (El Reno, Oklahoma) and irrigated (Stoneville, Mississippi) soybean fields in the southern U.S. during the 2016 growing season. Grain yield was about 1.6tha^-1 for rainfed soybean and 4.9tha^-1 for irrigated soybean. The magnitudes of diurnal NEE (~2-weeks average) reached seasonal peak values of -23.18 and -34.78μmolm^-2s^-1 in rainfed and irrigated soybean, respectively, approximately two months after planting (i.e., during peak growth). Similar thresholds of air temperature (T_a, slightly over 30°C) and vapor pressure deficit (VPD, ~2.5kPa) for NEE were observed at both sites. Daily (7-day average) NEE, GPP, and ER reached seasonal peak values of -4.55, 13.54, and 9.95gCm^-2d^-1 in rainfed soybean and -7.48, 18.13, and 14.93gCm^-2d^-1 in irrigated soybean, respectively. The growing season (DOY 132-243) NEE, GPP, and ER totals were -54, 783, and 729gCm^-2, respectively, in rainfed soybean. Similarly, cumulative NEE, GPP, and ER totals for DOY 163-256 (flux measurement was initiated on DOY 163, missing first 45days after planting) were -291, 1239, and 948gCm^-2, respectively, in irrigated soybean. Rainfed soybean was a net carbon sink for only two months, while irrigated soybean appeared to be a net carbon sink for about three months. However, grain yield and the magnitudes and seasonal sums of CO₂ fluxes for irrigated soybean in this study were comparable to those for soybean in the U.S. Corn Belt, but they were lower for rainfed soybean.