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For sustainable irrigated agriculture, scheduling irrigations based on accurate estimates of crop water requirements (ETc, crop evapotranspiration) are critical. ETc was estimated as a product of a reference crop evapotranspiration computed from weather data and a crop coefficient (Kc) in weather-based irrigation scheduling. In this investigation, an eddy covariance (EC) method was used for quantifying soybean (cv. Asgro 46X4) Kc in a farmer’s field under a humid climate. ETc quantified using the EC method was used for developing Kc for alfalfa (Kcr) and grass (Kco) reference crops computed from measured weather data. Experiments were conducted during three crop seasons (2017–2019) in a 500-ha furrow-irrigated soybean field—planted in silt loam soil in late April to early May and harvested in September. Harvested soybean yields were 4771, 5783, and 4909 kg ha−1, consuming 584, 640, and 593 mm ETc (average 605 mm), respectively, in 2017, 2018, and 2019. Monthly averaged daily ETc across the crop seasons varied between 2.1 mm in May 2019 to 6.2 mm in June 2018. Seasonally averaged daily ETc across the three crop seasons varied between 4.3 and 5.2 mm with an average of 4.8 mm. Across the crop seasons, ETc was 22% less and 2% greater than computed grass (ETo) and alfalfa (ETr) reference crop evapotranspiration. Monthly averaged daily Kco varied between 0.79 and 1.18, and Kcr ranged between 0.65 and 0.97. The Kc established can help develop soybean irrigation schedules, across climates and soils, based on ETo or ETr computed from real-time weather data.

Highlights Over the last 40 years the amount of irrigation water used by cotton in the United States has decreased while yields have increased leading to a large increase in crop water productivity (CWP). Many factors have contributed to improved CWP, such as improvements in water delivery systems. Irrigation scheduling technologies have also contributed to improved CWP; however, farmer adoption of advanced scheduling technologies is still limited and there is significant room for improvement. Increased yields from improved cultivars without an increase in water requirements has also been important for CWP. Continued developments in sensor technologies and improved crop simulation models are two examples of future strategies that should allow the U.S. cotton industry to continue an upward trend in CWP. Abstract. Over the last 40 years the amount of irrigation water used by cotton in the United States has decreased while yields have increased. Factors contributing to higher water productivity and decreased irrigation water use include migration of cotton out of the far western U.S. states to the east where more water requirements are met by rainfall; improved irrigation delivery systems with considerable variation in types and adoption rates across the U.S.; improved irrigation scheduling tools; improved genetics and knowledge of cotton physiology, and improved crop models that can help evaluate new irrigation strategies rapidly and inexpensively. The considerable progress over the last 40 years along with the promise of emerging technologies suggest that this progress will continue. Keywords: Cotton, Crop water productivity, Irrigation, Sustainability, Water use efficiency.

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.

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.

Background Cotton fiber quality and seed composition play vital roles in the economics of cotton production systems and the cottonseed meal industry. This research aimed to examine the effects of different irrigation levels and planting geometries on fiber quality and seed composition of cotton (Gossypium hirsutum L.). We conducted a 2-year study in 2018 and 2019 in a warm, humid area in the Southeast United States on Dundee silt loam soil. There were three irrigation treatments in the study. The treatments included irrigating every furrow, or full irrigation (FI), every alternate furrow, or half irrigation (HI), and no irrigation, or rain-fed (RF). Planting geometries were on ridges spaced 102 cm apart and either a single-row (SR) or twin-rows (TR). Results The results of high-volume instrument (HVI), advanced fiber information systems (AFIS) and near-infrared reflectance spectroscopy (NIRS) showed that irrigation and planting treatments played a significant role in fiber quality and seed composition. Across irrigation treatments, significant differences were seen in fiber properties, including fineness, maturity ratio, micronaire, neps, short fiber, strength, uniformity, upper half mean length (UHML), upper quartile length by weight (UQLw), and yellowness (+b). Irrigation and planting geometry (PG) had a significant effect on micronaire, strength, and UHML while their interaction was significant only for micronaire. The micronaire was negatively affected by irrigation as FI-SR, FI-TR, HI-SR, and HI-TR recorded 11% ~ 12% lower over the RF-SR and TR treatments. The PG played a minor role in determining fiber quality traits like micronaire and nep count. Irrigation treatments produced significantly lower (3% ~ 4%) protein content than rain-fed, while oil content increased significantly (6% ~ 10%). Conclusions The study results indicate a potential for improving cotton fiber and seed qualities by managing irrigation and planting geometries in cotton production systems in the Mississippi (MS) Delta region. The HI-TR system appears promising for lint and seed quality.

This study investigates the effect of irrigation (FI, all rows-irrigation; HI, alternate row irrigation; RF, rainfed) and planting geometry (PG) (SR, single-row; TR, twin-row) on soybean seed constituents. Results showed that most of these seed components were significantly affected by crop season due to contrasting precipitation and solar radiation patterns, particularly during July-August, coinciding with early reproductive and seed development stages. Both seed protein and oil levels responded positively to irrigation, while most of the amino acids were nonresponsive. The protein content ranged between 36.3 and 37.6% in 2018, while it was between 36.4 and 38.3% in 2019. Total seed oil content varied between 24.2 and 26.1% in 2018 and between 25.3 and 26.5% in 2019. Among amino acids, glycine, alanine, valine, and methionine levels were significantly higher in both FI and HI treatments. Among sugars, only sucrose was higher in response to the RF treatment, and irrigation did not affect both stachyose and raffinose. Oleic acid was higher in RF, while no significant differences were observed for linolenic and linoleic acids. Similarly, seasonal variation was significant for stearic acid content, but the 2019 season had relatively higher accumulation (stearic acid: between 4.1 and 4.5% in 2018 and from 4.6 to 4.9% in 2019). These results indicate that both irrigation and climate during seed development can alter some seed composition constituents and play critical roles in determining seed nutritional qualities.

Accurate quantification of crop evapotranspiration (ET) is critical to optimizing irrigation water productivity, especially, in the semiarid regions of the world where limited rainfall is supplemented by irrigation for crop production. In this context, cropping system models are potential tools for predicting ET or crop water requirements in agriculture across soils and climates and assist in developing decision support tools for irrigation. The objective of this study was to evaluate the accuracy of RZWQM2 simulated ET for fully irrigated silage (2006 and 2007) and grain corn (1990) against measured crop water use and soil evaporation with large weighing lysimeters in the Texas High Plains. An extended Shuttleworth and Wallace method was used to estimate potential crop ET (E and T) demand in RZWQM2. The Nimah and Hanks approach was used for crop water uptake and Richard’s Equation for soil water redistribution modeling. Simulations of biomass, leaf area index, soil water storage, and ET were reasonably close to the measured data. Root Mean Squared Deviation (RMSD) for corn biomass was between 1 and 2.1 MT ha−1, LAI between 0.33 and 0.88, water in the soil between 2 and 2.9 cm for a 190 cm soil profile, and actual daily crop ET between 1.0 to 1.5 mm across the three years of measured data. Arithmetic mean deviation (MD) for ET ranged from −0.10 to 0.40 mm. Fallow soil evaporation before and after corn planting was simulated within MD of −0.03–0.003 mm. The crop coefficients (Kc) calculated with measured ET and the short grass or alfalfa crop reference ET methods varied from year to year. The Kc values obtained by using the simulated ET and alfalfa reference ET were close to Kc values using measured ET, within RMSD of 0.17, and could be used to obtain long-term average Kc values for scheduling irrigation.

Accurate quantification of consumptive water requirements (ETc, evapotranspiration) of cropping systems is a critical prerequisite for sustainable irrigation water management applications. For applying the ETc for irrigation scheduling across soils and climates other than the location in which it was measured, it is also critical to develop crop coefficients (Kc) that link a reference crop evapotranspiration computed from local weather data to ETc. A systematic study for deriving Kc for cotton (Gossypium hirsutum L.) - representing its growth stages from planting to harvest - in humid climates is lacking in the literature. In this study, we used an eddy covariance (EC) method to quantify ETc from irrigated cotton (Gossypium hirsutum L.) in a 250 ha field with a Tunica clay soil, in 2017 and 2018. In the EC experiment, an open-path infrared gas analyzer and a sonic 3-D anemometer were deployed in the constant flux layer above the cotton canopy for collecting crop-canopy water flux data. Using the measured ETc, Kc were derived for alfalfa (Kcr) and grass (Kco) reference crop ET computed from weather data. Cotton cv. Delta Pine Land 1522 was planted in the first week of May and harvested in the second week of September in both the years. Lint yield was 1269 kg ha−1 in 2017 and 1569 kg ha−1 in 2018. Measured monthly averaged daily ETc ranged between 2.5 mm in May/September to 4 mm in July in 2017, and between 2.9 mm in May and 4.4 mm in August in 2018. Maximum daily ETc in 2017 and 2018 crop seasons were 5.6 and 6.7 mm, respectively. Seasonal total ETc was 367 mm and 439 mm (on average 402 mm), respectively. Alfalfa (ETr) and grass reference crop ET (ETo) computed were 664 and 546 mm, respectively. Averaged across the two years, average daily Kcr ranged between 0.45 in May to 0.80 in August, and Kco ranged from 0.54 in May and 0.99 in August. On average, seasonal ETr was 18 % more than ETo. Seasonal ETr and ETo were, respectively, 39 % and 22 % more than ETc. The Kc data developed will be useful for irrigation scheduling in cotton grown in similar climates and soils.

Conference: International Crop Modelling Symposium on Crop Modelling for Agriculture and Food Security under Global Change (iCropM) - Proceedings Paper Berlin, GERMANY 2016; This study assesses the ability of 21 crop models to capture the impact of elevated CO2 concentration ([CO2]) on maize yield and water use as measured in a 2-year Free Air Carbon dioxide Enrichment experiment conducted at the Thünen Institute in Braunschweig, Germany (Manderscheid et al., 2014). Data for ambient [CO2] and irrigated treatments were provided to the 21 models for calibrating plant traits, including weather, soil and management data as well as yield, grain number, above ground biomass, leaf area index, nitrogen concentration in biomass and grain, water use and soil water content. Models differed in their representation of carbon assimilation and evapotranspiration processes. The models reproduced the absence of yield response to elevated [CO2] under well-watered conditions, as well as the impact of water deficit at ambient [CO2], with 50% of models within a range of +/−1 Mg ha−1 around the mean. The bias of the median of the 21 models was less than 1 Mg ha−1. However under water deficit in one of the two years, the models captured only 30% of the exceptionally high [CO2] enhancement on yield observed. Furthermore the ensemble of models was unable to simulate the very low soil water content at anthesis and the increase of soil water and grain number brought about by the elevated [CO2] under dry conditions. Overall, we found models with explicit stomatal control on transpiration tended to perform better. Our results highlight the need for model improvement with respect to simulating transpirational water use and its impact on water status during the kernel-set phase.

Agricultural system simulation models are key tools for assessment of possible impacts of climate change on crop production and environmental quality. In this study, the CERES-Wheat 4.0 module in the RZWQM2 model was calibrated and validated for simulating spring wheat grown under elevated CO2 conditions in the FACE (Free Air CO2 Enrichment) experiments conducted at Maricopa, Arizona, USA from 1992 to 1997. The validated model was then used to simulate the possible impacts of climate change on the crop for a 16-year period centered on 2050 with a projected atmospheric CO2 concentration of 550 ppm. Sixteen General Circulation Model (GCM) projections of climate in response to this CO2 concentration were used for this purpose. In the FACE experiment, the crops were grown under ambient (365–370 ppm) and elevated (∼550 ppm) CO2 concentrations with two irrigation treatments (wet and dry) in 1992–1993 and 1993–1994, and two nitrogen (N) treatments (high and low N) in 1995–1996 and 1996–1997 crop seasons. The model simulated crop growth and grain yield, and soil water responses to CO2 reasonably well, reproducing variations due to the treatments. Under ambient CO2 in 1992–1993 and 1995–1996, biomass was simulated better in the dry and low N treatments with root mean square difference (RMSD) of 181 and 161 kg ha−1, respectively, compared to the wet and high N treatments with RMSD of 259 and 268 kg ha −1 , respectively. The effects of water and N treatments were higher than those of CO2, and the model reproduced these effects well. Elevated CO2 effects on crop growth were counterbalanced by temperature effects, and projected precipitation had little effect on the simulated crop. The model results provide reasonable confidence for simulations of possible impacts of projected climate change on wheat crop growth in the region, within normal field data uncertainties.

Corn (Zea mays L.) production in northeastern Colorado is constrained by a frost-free period averaging 11 May to 27 September. For optimization of yield, planting at the appropriate time to fit the hybrid maturity length and growing season is critical. Crop models could be used to determine optimum planting windows for a locality. We calibrated the plant parameters of the Root Zone Water Quality Model (RZWQM) and genetic coefficients for the CERES-Maize model and validated their performance against experimental data of three corn hybrids varying in days to maturity, planted on three planting dates in 2 yr at Akron, CO, under irrigation. Both models could be calibrated to predict leaf area index, soil water content, crop water use, and yield with similar levels of accuracy. Both models simulated the observed decline in yield with delayed planting date, but CERES-Maize simulated the yield from the latest planting date much more accurately for all three hybrids than did RZWQM (13% underpredicted by CERES-Maize; 50% overpredicted by RZWQM). Using the long-term Akron weather record, the latest planting dates for the short-, mid-, and long-season hybrids to have a 50% chance of achieving a break-even yield under irrigation were 13 May, 20 May, and 6 May, respectively. Long-term simulations also revealed that the longer maturity length hybrids lose yield faster than short maturity length hybrids with planting delay. The information generated by either RZWQM or CERES-Maize can be useful for making both planting and replanting decisions for corn hybrids of varying maturity length in northeastern Colorado.