The rapid adoption of artificial intelligence and machine learning (AI/ML) in hydrology has delivered notable gains in prediction, gap filling, and data integration. Recently, these methods have increasingly been applied to estimate FAO and ASCE Standardized Reference Evapotranspiration (ETref), a trend that warrants careful reconsideration. ETref is not a directly observable natural flux but a deliberately defined climatic index, constructed to represent atmospheric evaporative demand over a fixed reference surface using physically based relationships among solar radiation, air temperature, humidity, and wind speed. Treating ETref as a learnable target conflates the estimation of a physical phenomenon with the reproduction of a standardized physical calculation. Although AI/ML-based ETref estimates may achieve high statistical accuracy, these methods risk undermining the intended purpose of ETref standardization by embedding local covariances, obscuring error attribution, limiting transferability across space and time, and weakening the institutional knowledge that anchors scientific evapotranspiration (ET) practice to physical principles and crop coefficient frameworks. Moreover, reduced-input AI/ML formulations may inadvertently weaken the case for expanding and maintaining high-quality reference weather networks that accurate ETref depends on. We argue that AI/ML adds the most value in ET science when used to support, rather than replace, the FAO/ASCE ETref physical equation. Acceptable AI/ML uses include improvement of standardized ETref frameworks' meteorological datasets, reference-condition screening, estimating missing weather data and estimation of actual evapotranspiration (ETa, not ETref) where the modeling complexity truly justifies the flexibility of the AI/ML tools.

Highlights Crop N removal response to N inputs shows diminishing N removal beyond 163.08 kg ha-1. N inputs at which diminishing returns are observed have increased. N inputs at which diminishing returns are observed are specific to crop belts. Proportion of counties that show N inputs exceeding the optimal level has increased. ABSTRACT. The response of nitrogen (N) removal by crops to an increase in fertilization strongly determines the profitability and sustainability of agricultural systems and informs nutrient management decisions at the producer level. These response functions are analyzed for agronomic and economic optima achieved at field scales for evaluating production, economic, and environmental goals. However, such assessments are lacking for entire regional agroecosystems to allow understanding of the response of N removal collectively across all crops grown during the year (N removal ) to total N fertilization (i.e., all manageable N sources, N in ) historically. Here, we address this knowledge gap by leveraging a large-scale N budget and statistical techniques to characterize space-time variability and trends in historical (1987–2016) county-level N removal , N in , and nitrogen use efficiency (NUE) across the conterminous U.S. (CONUS). We intend to evaluate crop belt-specific characteristics of diminished returns in N removal to N in response and change over time. N in , N removal , and NUE were subject to drastic spatial variation in long-term mean values, interannual variability, and long-term change, which were quantified and mapped to understand their spatiotemporal distributions. Pooled across all counties and years, N removal shows diminished returns when N in reached 163 kg ha -1 (N in, bp ). Upon quantifying and analyzing year-specific diminished returns, we found that N in, bp has increased during 1987–2016, and so has the NUE achieved prior to attaining diminished returns. The proportion of counties (6%–22%) where N in exceeds N in, bp also increased, and counties that repeatedly demonstrated such exceedance during 1987–2016 were identified. Values of N in,bp are specific to crop belts within the U.S., the majority of which also show increased N in, bp over time. Specifically, barley, beans, and sugarbeets (198 kg ha -1 ), and alfalfa and barley (190 kg ha -1 ) belts showed notably greater N in,bp relative to the national mean (163 kg ha -1 ), while N in,bp for corn grain and soy belts was similar to the national mean. Overall, these findings represent a comprehensive assessment of how systems-level N removal across U.S. agriculture has historically responded to change in N in , a prerequisite for guiding mitigation and adaptation policy and efforts. Keywords: Fertilizer, Manure, Nitrogen cycle, Nitrogen use efficiency, Yield.

The pyfao56 Python package provides an open-source, evapotranspiration-based water balance model that is useful for irrigation management and water resource planning. Originally, pyfao56 required users to input explicit information on the timing and depth of irrigation; however, more recent applications called for an automated method to determine irrigation schedules in the model. The objectives of this research were to develop a new pyfao56 methodology for computation of irrigation schedules based on 25 user-specified parameters and to demonstrate the approach using data from a 2018 cotton field study at Maricopa, Arizona. The pyfao56 autoirrigation methodology involves a new software object (“AutoIrrigate”) for specifying associated parameters and an algorithm that determines whether or not irrigation is indicated on each daily timestep. Parameterization of the pyfao56 model for the conditions of the 2018 cotton field study demonstrated that the model could respond to irrigation management variability, with root mean square errors of 8–15 mm between measured and modeled root zone soil water depletion. A variety of autoirrigation scenarios were devised to evaluate pyfao56 outcomes for the 2018 Arizona cotton season with irrigation scheduling based on different management criteria related to soil water status, plant water stress, evapotranspiration replacement, practical constraints, and precipitation forecasts. Additional scenarios tested the ability of the autoirrigation algorithm to replicate the actual 2018 field schedule and to mimic schedules for production-scale Arizona cotton production with furrow flood, overhead sprinkler, and subsurface drip irrigation systems. The resulting irrigation schedules were widely variable with number of irrigation events ranging 8–103, total seasonal irrigation ranging 642–1110 mm, and maximum daily irrigation ranging 10–108 mm. The pyfao56 autoirrigation methodology provides a flexible tool for simulating realistic irrigation management schedules that have practical relevance for real field applications with different irrigation methods, system constraints, and management philosophies.

This research investigated soybean soil water dynamics under different irrigation levels in three different soil types in the same field concurrently. Treatments imposed in each soil type were: (i) variable-rate irrigation (VRI), (ii) fixed-rate full irrigation (FRI-1 '' or FRI-25.4 mm) and (iii) fixed-rate limited irrigation (FRI-0.75 '' or FRI-19 mm). In 2018, VRI received 75% less water than FRI-1 '' and received 49% less water than FRI-0.75 ''. In 2019, VRI received 100% more irrigation than FRI-1 '' and 41% less than FRI-0.75 ''. Soil water dynamics of each treatment in the same soil and between the soils exhibited substantial interannual variations. Soil type had substantial and greater impact on soil moisture dynamics than irrigation treatments. Total available water (TAW), dry spell and antecedent soil moisture were impacted to a greater extent by the spatial soil properties than irrigation treatments. The range of field capacity (FC), permanent wilting point (PWP), TAW, dry spell soil moisture and antecedent soil moisture quantified for each soil type spatially and temporally in the same research field with respect to soil moisture dynamics and impacts on irrigation requirements for different irrigation management strategies provide a beneficial scope of understanding the effects of these spatially variable soil properties on water management. The research also provides substantial evidence in terms of the critical importance of detailed quantification, analyses and understanding of the soil properties that must be considered for successful implementation of VRI technology.

Highlights NUE response to N addition is dependent on N source (fertilizer, manure, and biological fixation). Random forest models captured 71% and 47% NUE variance for CONUS and global croplands. Contribution from biological N fixation was most important for explaining NUE variance, followed by manure and fertilizer contributions. ABSTRACT. Nitrogen use efficiency (NUE) is a useful indicator of the tradeoffs among cropland harvest nitrogen (N) and total N fertilization. Total N fertilization can be fulfilled by different sources depending on local availability, livestock production, land use and crop distribution, and economics, all of which change drastically in space and time. While NUE assesses crop harvest N response to total N fertilization, it typically does not distinguish between N fertilization sources, and thus little is known on how varying contributions from diverse N inputs impact NUE achieved in a region and year. Here, we use long-term (1961–2020) N budgets combined with random forest modeling to address this knowledge gap for global croplands, with a finer spatial emphasis on conterminous United States (CONUS) croplands. Random forest models using fractional fertilization contributions (F fert , F manure , and F bnf for synthetic fertilizers, livestock manure, and biological N fixation, respectively) and captured 71% and 47% of space-time variance in NUE for CONUS and global croplands, respectively. F bnf was the most important predictor for explaining variance in county/country-year NUE, followed by F manure , and F fert . Contributions from each of the input sources exerted distinct controls on NUE through its observed ranges and these controls were visualized using partial dependence plots for NUE. The models establish that regions and years where a higher proportion of total N fertilization is met by biological N fixation (relative to fertilizers and manure) have higher NUE. Overall, our findings improve understanding of how NUE may be optimized by managing diverse N sources with the aim of meeting economic and sustainability goals. Keywords: Chemical fertilizers, Livestock, Nitrogen cycle, Nutrient budgets, Nutrients.

Palmer amaranth (Amaranthus palmeri S. Watson) is a major biotic constraint in agronomic cropping systems in the United States. While crop-weed competition models offer a beneficial tool for understanding and predicting crop yield losses, within these models, certain weed biological characteristics and their responses to the environment are unknown. This limits understanding of weed growth in competition with crops under different irrigation methods and how competition for soil moisture affects crop growth parameters. This research measured the effect of center-pivot irrigation (CPI) and subsurface drip irrigation (SDI) on the actual evapotranspiration (ETa) of A. palmeri grown in maize (Zea mays L.), soybean [Glycine max (L.) Merr.], and fallow subplots. Twelve A. palmeri plants were alternately transplanted 1 m apart in the middle two rows of maize, soybean, and fallow subplots under CPI and SDI in 2019 and 2020 in south-central Nebraska. Maize, soybean, and fallow subplots without A. palmeri were included for comparison. Soil-moisture sensors were installed at 0-0.30, 0.30-0.60, and 0.60-0.90-m soil depths next to or between three A. palmeri and crop plants in each subplot. Soil-moisture data were recorded hourly from the time of A. palmeri transplanting to crop harvest. The results indicate differences in A. palmeri ETa between time of season (early, mid-, and late season) and crop type across 2019 and 2020. Although irrigation type did not affect subplot data, the presence of A. palmeri had an impact on subplot ETa across both years, which can be attributed to the variable relationship between volumetric soil water content (VWC) and ETa throughout the growing season due to advancing phenological stages and management practices. This study provides important and first-established baseline data and information about A. palmeri evapotranspiration and its relation to morphological features for future use in mechanistic crop-weed competition models.

Altered evaporative demand is a global phenomenon observed over recent decades, however, such change has not been attributed explicitly to specific meteorological drivers, hampering consensus on what has caused such change. Here we investigate exactly how much individual drivers have contributed to long-term grass-reference evapotranspiration (ETo) change within conterminous United States (CONUS), with an emphasis on agricultural croplands. Using scenarios that constrain individual drivers i.e., air temperatures (T), relative humidity (RH), solar radiation (R-s), and wind speeds (U-2) to their climatologies, we determined their relative contribution toward ETo change at monthly and annual scales. Annual ETo increased by 111 mm, or >2 standard deviations (SD) relative to the 1981-2000 baseline, accompanied by strong increase in R-s (2.7 SD), U-2 (2.5 SD), T (1.1 SD), and decreased RH (2.3 SD) in regions that account for one-third of calories produced in the U.S. Annual ETo increase was attributed primarily to T (relative contribution of 36%), followed by R-s (29%), U-2 (18%), and RH (17%) with significant spatial and seasonal variability. During agriculturally critical summer months, R-s was the dominant driver with a 40%-50% relative contribution, and other three drivers were roughly equally important. These findings address demand-side of agricultural water use and imply long-term change in crop functions and performance, water security, and planning across aridity gradients.

Highlights Hourly sap flow measured in co-located and identically managed maize, sorghum, and soybean closed canopies. PAR, VPD, and ET r were strongly correlated to transpiration (T) normalized by LAI. Negative response of T to high VPD (3-4 kPa) was observed for maize and sorghum. Counterclockwise hysteresis observed for diurnal T-VPD and T-PAR. MLR models were developed to estimate T using VPD and PAR. Abstract. Transpiration (T) dominates terrestrial hydrological fluxes and is strongly coupled with vegetation productivity and water use efficiency across different biomes, including agricultural systems. Studying how T in field crops responds to environmental variability has important implications to inform and predict agroecosystems’ response to a changing environment. However, comparative T rates among major field crops remain unknown in many regions where drought severity and limited freshwater availability are projected, such as the Central U.S. Plains. We address this knowledge gap by monitoring and characterizing hourly T for field-grown maize, grain sorghum, and soybean crops under the same weather, soil, and management regimes using sap flow sensors. The relationships among crop-specific T and air temperature (Tair), relative humidity (RH), wind speed (u2), vapor pressure deficit (VPD), incoming shortwave radiation (Rs), photosynthetically active radiation (PAR), net radiation (Rn), and grass- and alfalfa-reference evapotranspiration (ETo, ETr) were investigated. T normalized by leaf area index (T LAI-1) was most correlated with PAR (r=0.88), ETr (r=0.84), and VPD (r=0.81). Mean sensitivity of T LAI-1 to unit change in Tair, Rs, PAR, Rn, u2, RH, VPD, and ETr for maize and sorghum was 88% and 59% greater than that of soybean, respectively. All crops showed non-linear T LAI-1 response to increasing VPD, and a negative response of T LAI-1 to VPD was observed in the 3.0-4.0 kPa VPD range for maize and sorghum. Each crop demonstrated a counterclockwise hysteresis effect to diurnal T-VPD and T-PAR, which was 177% and 87% greater (for T-VPD) and 44% and 17% greater (for T-PAR) in maize and sorghum, respectively, than soybean. Transpiration has rarely been measured in row crops, especially in a comparative fashion, and thus, the concurrent T dynamics and their environmental controls characterized in this research are of critical importance. These data can be instrumental for quantitatively assessing change in true crop water use (transpiration) and thus crop suitability under projected environmental change. Keywords: Hysteresis, Photosynthetically active radiation, Reference evapotranspiration, Sapflow, Vapor pressure deficit.

Volunteer corn (Zea mays L.) is a competitive weed in corn-based cropping systems. Scientific literature does not exist about the water use of volunteer corn grown in different crops and irrigation systems. The objectives of this study were to characterize the growth and evapotranspiration (ETa) of volunteer corn in corn, soybean [Glycine max (L). Merr.], and sorghum [Sorghum bicolor (L.) Moench] under center-pivot irrigation (CPI) and subsurface drip irrigation (SDI) systems. Field experiments were conducted in south-central Nebraska in 2021 and 2022. Soil moisture sensors were installed at depths of 0 to 0.30, 0.30 to 0.60, and 0.60 to 0.90 m to track soil water balance and quantify seasonal total ETa. Corn was the most competitive, as volunteer corn had the lowest biomass, leaf area, and plant height compared with the fallow. Soybean was the least competitive with volunteer corn, as the plant height, biomass, and leaf area of volunteer corn in soybean were similar to fallow at 15, 30, 45, and 60 d after transplanting (DATr). Averaged across crop treatments, irrigation type did not affect volunteer corn growth at 15 to 45 DATr. Soil water depletion and ETa were similar across crop treatments with and without volunteer corn, as water was not a limiting factor in this study. The ETa of volunteer corn was the highest in soybean (623 mm), followed by sorghum (622 mm), and corn (617 mm) under CPI. The SDI had higher irrigation efficiency, because without affecting crop yield, it had 3%, 6%, and 8% lower ETa in soybean (605 mm), sorghum (585 mm), and corn (571 mm), respectively. Although soil water use did not differ with volunteer corn infestation, a soybean yield loss of 27% was observed, which suggests that volunteer corn may not compete for moisture under fully irrigated conditions; however, it can impact the crop yield potential due to competition for factors other than soil moisture.

Total evaporative demand or atmospheric thirst is a primary determinant of agroecosystems’ water use and an indispensable input to scientifically based irrigation design and management. However, despite its extensive use to represent agricultural environments, it has not been assessed for its extreme behavior. Prolonged exposure to extreme evaporative demand conditions a.k.a thirstwaves can be especially stressful for agricultural output, water use, and management, but remain uninvestigated owing to lack of meaningful metrics for quantifying and reporting ‘extreme thirst exposure’. In this letter, I present spatial (county-level) and temporal (1981–2021) changes in exposure to extreme thirst during the agricultural growing season across the conterminous U.S. (CONUS). Using a fully physical metric of evaporative demand, i.e., standardized short crop reference evapotranspiration (ET _o ), I define two novel measures: cumulative extreme thirst exposure (thirst _cum ) and average extreme thirst anomaly (thirst _anom ) to represent the seasonal-level severity of thirstwaves. Both metrics showed significant spatiotemporal variation with long-term averages of 12 mm (thirst _cum ) and 0.66 mm d ^−1 (thirst _anom ) for CONUS. Distinct spatial patterns were revealed for extreme thirst exposure that had little in common with those observed for total ET _o . Spatially, hotspots of high extreme thirst exposure were co-located with the Midwest and High Plains aquifer regions, that account for 64% of total acreage and 28% of irrigated acreage nationally, respectively. Critical for food and water security, these regions have experienced the highest extreme thirst exposure nationally, hence necessitating reevaluation of regional disparities in water stress. While thirst _cum and thirst _anom have increased by 5.6 mm and 0.21 mm d ^−1 on an average in CONUS, worsening of extreme thirst exposure is especially concerning for the High Plains aquifer region (12.6 mm and 0.54 mm d ^−1 , respectively). The emergence of previously unrealized hotspots in regions critical for water security uncover potential pitfalls for planning and adaptation that may result from overlooking extreme measures of evaporative demand.

AbstractSoil nutrient concentrations are often expressed as parts per million (ppm) in soil test reports. For incorporation into nutrient management decisions, ppm‐based concentrations have to be converted into pounds per acre, and a conversion factor (multiplier) of 2.0 is typically recommended universally to do so. However, this conversion factor stems from an assumed value of bulk density (ρb) corresponding to silt loam soil and is invariant to any deviation beyond assumed ρb. Here, we quantify and evaluate the potential ramifications of assuming a constant ρb value on calculating soil nitrogen credits. A true dynamic conversion factor that is sensitive to variation in ρb ranges between 1.28 and 2.68 for soils across US cropland. Failure to account for this dynamic conversion factor was shown to result in an underestimation of soil N credits by up to 40%. In addition to spatial variation, management‐induced changes in ρb are also important to incorporate into the conversion factor.

The pyfao56 software package is a Python-based implementation of the standardized evapotranspiration (ET) methodologies described in Irrigation and Drainage paper No. 56 of the Food and Agriculture Organization of the United Nations, commonly known as FAO-56. This update improved pyfao56 by 1) adding an optional surface runoff methodology, 2) adding an extensive algorithm for automating the computation of irrigation schedules, 3) considering irrigation losses due to irrigation system inefficiencies, 4) adding an optional method to compute the transpiration reduction coefficient (Ks) based on the curvilinear approach from AquaCrop, 5) incorporating a module for computing 15 goodness-of-fit statistics between simulated and measured data, and 6) computing a cumulative seasonal water balance summary. Most of these updates arose from user requests to add new features or options, and the collaborations demonstrated the value of community-based development for rapid improvement and generalization of scientific software.

Grain yield response to consumptive water use a.k.a. evapotranspiration (ETc) is a critical dimension of food-water nexus in irrigated agroecosystems. With the largest water footprint, irrigated corn production in the U.S. must be assessed for its ETc and water use efficiency (WUE) at a scale meaningful for producers and water managers. Field experiments significantly inform our understanding of how corn yield responds to ETc at the field/plot-level, but their collective synergism remains untapped. This analysis uses a literature synthesis of existing measured data from nationwide experiments that measured corn ETc, to build models that elucidate and predict corn ETc and WUE under diverse environment and on-farm management. The resulting synthesis (n = 1,362) captured wide ranging conditions of mean aridity, environmental variability, management, and crop outcomes, providing an unprecedented opportunity to train data-driven models. Random forest models effectively predicted seasonal total ETc and seasonal mean WUE using a handful of environmental and management-based predictors, demonstrating RMSEs of 33 mm (5.1%) and 0.28 kg m-3 (10.4%), respectively. Irrigation depth was the most important followed by total precipitation received during the growing season in determining ETc and WUE. Trained on the largest compilation of experimental data sets of corn ETc, these data-driven models represent nuanced corn water production functions that also account for weather and agronomy besides describing economic yield-ETc response. Measured corn ETc & water use efficiency (WUE) and underlying environmental and management conditions across all experimental research in the U.S. were synthesized Irrigation depth was the most important predictor of ETc and WUE followed by total precipitation received during the growing season Presented nuanced crop water production functions that account for weather and management besides describing yield-ETc response

A recent study by Ambika and Mishra (2022, https://doi.org/10.1029/2021EF002642) asserted using simulated evidence that a shift towards drip irrigation from channel irrigation in the Indo-Gangetic Plains will result in alleviation of moist heat stress and water savings. The assumptions, parameterizations, and approach adopted in this study do not adequately represent fundamentals of irrigation science, management, and engineering, as well as realities of local agricultural water management in the Indo-Gangetic Plains. This comment attempts to highlight these inadequacies of the paper and related literature, by focusing on four major aspects related to irrigation process, local land-use, timing of irrigation and moist heat stress, and water accounting. These weaknesses in irrigation processes modeling result in overlooking of indispensable aspects that should be accounted for to comprehensively assess impacts of irrigation method choice on regional weather and water resources.

Studying historical response of crops to weather conditions at a finer scale is essential for devising agricultural strategies tailored to expected climate changes. However, determining the relationship between crop and climate in Mississippi (MS) remains elusive. Therefore, this research attempted to i) estimate climate trends between 1970 and 2020 in MS during the soybean growing season (SGS) using the Mann-Kendall and Sen slope method, ii) calculate the impact of climate change on soybean yield using an auto-regressive distributive lag (ARDL) econometric model, and iii) identify the most critical months from a crop-climate perspective by generating a correlation between the detrended yield and the monthly average for each climatic variable. Specific variables considered were maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), precipitation (PT), carbon dioxide emissions (CO2), and relative humidity (RH). All required diagnostic-tests i.e., pre-analysis, post-analysis, model-sensitivity, and assessing the models' goodness-of-fit were performed and statistical standards were met. A positive trend in Tmin (+0.25 °C/decade), and a negative trend in DTR (−0.18 °C/decade) was found. Although Tmax, PT, and RH showed non-significant trends, numerical changes were noted as +0.11 °C/decade, +3.03 mm/decade, and −0.06 %/decade, respectively. Furthermore, soybean yield was positively correlated with Tmin (in June and September), PT (in July and August), and RH (in July), but negatively correlated with Tmax (in July and August) and DTR (in June, July, and August). Soybean yield was observed to be significantly reduced by 18.11 % over the long-term and by 5.51 % over the short-term for every 1 °C increase in Tmax. With every unit increase in Tmin and CO2 emissions, the yield of soybeans increased significantly by 7.76 % and 3.04 %, respectively. Altogether, soybeans in MS exhibited variable sensitivity to short- and long-terms climatic changes. The results highlight the importance of testing climate-resilient agronomic practices and cultivars that encompass asymmetric sensitivities in response to climatic conditions of MS.

Appropriate representation of environmental conditions via meaningful metrics is critical for their effectiveness in modeling crop physiological and resource use processes. Vapor pressure deficit (VPD) is a primary determinant of crop water use efficiency, commonly reported as daily average. However, VPD can vary substantially during the day in relation to diurnal cycle of transpiration, in which case daily average VPD may not suffice. In such scenarios, integrating VPD over the course of diurnal transpiration cycle into a weighted VPD (VPDw) can be physiologically meaningful as a metric. The goal of this research is to characterize the fractional coefficient (Frac) that can be used to effectively calculate VPDw from daily temperature and relative humidity data without the need for sub-daily environmental data. Site-specific and seasonal estimates of Frac, which represent the relative positioning of VPDw to diurnal extremes of saturation vapor pressure are currently lacking for semi-arid regions. To address this, we quantified Frac at 101 sites in the U.S. Central Plains using long-term (1982-2019) hourly and daily radiation and VPD datasets. Frac varied substantially across the region, with growing season mean values ranging from 0.59 to 0.92. Summer months had the highest Frac values with relatively lower uncertainty as compared to lower Frac during winter. Local observations of Frac deviated significantly from previously assumed stationary estimates implying that site-specific and month-specific Frac may be necessary for robust water use estimations. VPDw measured at an experimental site in Central U.S. Plains explained greater variance in daytime crop transpiration than daily mean VPD in maize, soybean, and sorghum canopies. Sitespecific Frac was also assessed to perform better than commonly used Frac estimate of 0.75 to explain day-today variation in transpiration, demonstrating effective use of long-term mean Frac values presented here to estimate VPDw using daily datasets.

Vapor pressure deficit (VPD) is the difference between saturation and actual water vapor pressures and is driven by interactions between air temperature and humidity. Globally, VPD observations indicate drying of the atmosphere, while air temperature (T) and relative humidity (RH) have changed asymmetrically during daytime versus nighttime. However, whether daytime or nighttime T (T-day and T-night, respectively) and RH (RHday and RHnight, respectively) have been more important to VPD change is unknown, and so is the seasonal variation of this importance. This research determines the relative contribution of T-day, T-night, RHday, and RHnight to VPD change observed during 1981-2021 across the conterminous United States. T-day contributed 41% to driving a VPD increase of 2.1 standard deviations (SD), followed by contributions of 25%, 22%, and 12% from RHday, RHnight, and T-night, respectively. Regions with significant VPD increase have seen increase in T-day (by 1 SD) and T-night (by 0.9 SD) and a decrease in RHday (by 1 SD) and RHnight (by 3.4 SD). Diurnal asymmetry in warming and drying trends was true for most months, with greater rates of change during the nighttime than daytime. T-day was the dominant driver of the annual mean VPD rise for 70% of the counties, followed by RHday (18%), RHnight (11%), and T-night (1%). We conclude that warming and drying during the days and nights together explain increased VPD across the US croplands and that climate stressors during days and nights should be viewed independently due to their crop-specific impacts that vary spatially and seasonally.

Abstract Robust assessment of crop water availability requires effective integration of soil moisture data within the range of field capacity (θFC) to permanent wilting point (θPWP). Emerging needs for spatiotemporally dynamic θFC and θPWP are difficult to achieve with lab determinations. Therefore, we used long‐term data from 182 sites across the United States to evaluate whether soil moisture extremes defined by 95th and 5th percentiles represent θFC and θPWP, respectively. Soil moisture extremes and lab‐measured θFC and θPWP were well correlated (R2 = 0.71−0.92), however, both 95th and 5th percentiles overestimated θFC and θPWP at most depths (RMSE = 6%–16% vwc). Percentiles of soil moisture distribution that corresponded to lab‐determined θFC and θPWP varied widely and were a function of precipitation received at the site and site‐ and soil‐depth specific clay content. These findings imply that while θFC and θPWP may not be broadly represented by soil moisture extremes (95th and 5th percentiles), there may be potential to statistically infer the positioning of θFC and θPWP within long‐term soil moisture distributions using biophysical determinants such as aridity and soil characteristics.

Atmospheric dryness has been recognized as a dominant driver for agroecosystem functioning due to its limiting controls on stomatal behavior, even under sufficient soil moisture. Due to its relative importance in influencing carbon and water fluxes, atmospheric dryness has emerged as an important element of global change, jeopardizing food security and agricultural profitability via negative impacts on crop yields. Although negative impacts of atmospheric dryness are well recognized at various scales, it is unknown how heterogeneity in soils' ability to retain water for plant uptake affects crop yields' response to increasing atmospheric dryness. To address this, we analyzed how soil available water capacity (AWC) affects the response of crop yields to space and time variation in summer (June-July-August) vapor pressure deficit (VPDJJA) across the conterminous U.S. (CONUS) during 1980-2020 using 77,701 and 51,190 county-year records for maize and soybean, respectively. A distinct co-distribution of VPDJJA and AWC regimes is experienced by maize and soybean acreage and is subject to significant yield variation. Accounting for entire AWC heterogeneity in CONUS, both crops showed a positive yield response to increasing VPDJJA until 0.8 kPa, thereafter showing a gradually increasing negative response to VPDJJA increase. Upon soil-specific investigation of yield response to VPD, the yield sensitivity parameter to VPDJJA became less negative as soil AWC increased, implying buffering of dryness impacts on yields as the ability of soils to retain water increased. For VPDJJA increase between 1.0 kPa and 1.4 kPa, mean VPDJJA impacts on maize yields in soils with 10 %10 % and 15 % for maize and soybean, respectively. Possible physical mechanisms responsible for this buffering are improved plant physiological function and fulfilment of evaporative demand due to additional soil water resulting from higher AWC, given sufficient water supply. This empirical large-scale evidence of mediating role of AWC for crop yield impacts from a dryer atmosphere underscores the critical nature of soils to support climate-smart agricultural practices via building soil organic matter as well as providing co-benefits for improved crop performance to meet current and future population's food and fiber needs.

Weeds compete with crops for soil moisture, along with other resources, which can impact the germination, growth, and seed production of weeds; however, this impact has not been systematically recorded and synthesized across diverse studies. To address this knowledge gap, a global meta-analysis was conducted using 1,196 paired observations from 86 published articles assessing the effect of water stress on weed germination, growth characteristics, and seed production. These studies were conducted and published during 1970 through 2020 across four continents (Asia, Australia, Europe, and North America). Imposed water stress was expressed as solution osmotic potential (psi(solution)), soil water potential (psi(soil)), or soil moisture as percent field capacity. Meta-analysis revealed that water stress inhibits weed germination, growth, and seed production, and the quantitative response intensified with increasing water stress. A psi(solution) greater than -0.8 MPa completely inhibits germination of both grass and broadleaf weeds. A psi(solution) from -0.09 to -0.32 MPa reduces weed germination by 50% compared with the unstressed condition. Moderate soil water stress, equivalent to 30% to 60% field capacity, inhibits growth characteristics (branches or tillers per plant, leaf area, leaves per plant, plant height, root, and shoot biomass) by 33% and weed seed production by 50%. Severe soil water stress, below 30% field capacity, inhibits weed growth by 51% and seed production by 88%. Although water stress inhibits weed growth, it does not entirely suppress the ability to germinate, grow, and produce seeds, resulting in weed seedbank accumulation. This creates management challenges for producers, because weed seeds can survive in the soil for many years, depending on weed species and environmental conditions. Quantitative information compiled in this meta-analysis can be instrumental to model the weeds' multidimensional responses to water stress and designing integrated weed management strategies for reducing the weed seedbank.