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Reducing methane emissions and water use is critical for combating climate change and declining aquifers on food production. Reductions in irrigation water use and methane emissions are known benefits of practicing alternate wetting and drying (AWD) over continuous flooding (CF) water management in lowland rice (Oryza sativa L.) production systems. In a two-year (2020 and 2021) study, methane emissions from large farm-scale (∼50 ha) rice fields managed under CF and AWD in soils dominated by Sharkey clay (Sharkey clay, clay over loamy, montmorillonitic non-acid, thermic Vertic halauepet) were monitored using the eddy covariance method (EC). In the EC system, an open-path laser gas analyzer was used to monitor air methane gas density in the constant flux layer of the atmosphere over the rice-crop canopies. Total water pumped into the field for floodwater management was higher in CF compared to AWD by 24 and 14% in 2020 and 2021, respectively. Considerable variations between seasons in the amount of methane emitted from the CF and AWD treatments were observed: CF emitted 29 and 75 kg ha(−1) and AWD emitted 14 and 34 kg ha(−1) methane in 2020 and 2021, respectively. Notwithstanding, the extent of reduction in methane emissions due to AWD over CF was similar for each crop season (52% in 2020 and 55% in 2021). Rice grain yield harvested differed by only ±2% between AWD and CF. This investigation of large-scale system-level evaluation, using the EC method, confirmed that by practicing AWD floodwater management in rice, water pumped from aquifers could be reduced by about a quarter and methane emissions from rice fields could be cut down by about half without affecting grain yields, thereby promoting sustainable water management and greenhouse gas emission reduction during rice production in the Lower Mississippi Delta.

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.

Planting winter cover crops (CC) in soybean cropping systems is expected to offer various environmental benefits including soil health and fertility besides enhanced cash crop productivity. In a three-year study (2018–2021) conducted on a Dundee silt loam, we assessed the impact of introducing rye (Secale cereale L.) CC during the winter fallow period on soil organic carbon (SOC), soil organic matter (SOM), soil total nitrogen (STN), bulk density (BD), saturated hydraulic conductivity (Kfs), soil penetration resistance (SPR), and water-stable aggregates (WSA). Three treatments evaluated were: i) no cover crop (NC), ii) winter rye as CC rolled when green and desiccated after soybean planting (GR), and iii) winter rye CC desiccated and rolled before planting soybean (BR) in a randomized complete block design with six replications. The depth of the soil sampling in 2019 was 0-15 and 15-30 cm while 0-10, 10-20 and 20-30 cm depth soil sampling was done in 2020 and 2021. Effects of BR and GR on soybean root growth characteristics (number of roots, root length and root angle) were measured using a CID 600 root scanner. The results showed that CC (both BR and GR) improved SOC by 7 to 12.5%, soil organic matter by 9 to 15%, STN by 13 to 29%, WSA by 26 to 68%, Kfs by 5 to 9% and reduced BD by 8% and SPR by 14 to 18% compared to NC (P

Considerable variations in farm productivity were reported across soils and climates when winter cover crops (CC) were rotated with summer main cash crops. Hence, a three-year field study (2019-2021) was conducted on Dundee silt loam in a humid climate to assess soybean growth and yield, weed control, and profitability under no-till conditions in response to (i) no CC (NC), (ii) winter rye CC rolled when green, followed by soybean planting and desiccation by paraquat (GR) and iii) winter rye CC desiccated using paraquat and rolled followed by soybean planting (BR). No differences in phenological growth stages of soybean were observed among the treatments. Measured leaf area index was comparable among the treatments across the three seasons. The rate of rye CC biomass decay estimated eight weeks after planting (WAP) was much higher than at four WAP. In 2019, at eight WAP plant residue ranged from 29.3% under NC to 52.9% under GR, indicating the paraquat desiccated natural winter vegetation decays faster than the desiccated rye CC biomass. The weed biomass was 72% higher at eight WAP (0.29 Mg ha-1) than that of four WAP (0.17 Mg ha-1) and NC plots had higher weed biomass at both four WAP and eight WAP over CC plots. Field established soybean stand in the GR plots were consistently higher than the NC plots by 8%, 30%, and 22% in 2019, 2020, and 2021, respectively. Soybean yield in NC plot was 13% higher than GR and 15% higher than BR plots in 2019. However, in 2020 and 2021, soybean yield from BR and GR plots was significantly higher than NC plots (10% and 13%, respectively). In the three-year study, net returns from soybean with rye CC (regardless of GR or BR) in the first year was negative. In the second and third year, net returns in GR and BR were positive and comparable to NC. There were no differences in soybean yield and net returns between rye CC rolled green (GR) and rye CC desiccated (BR) prior to planting. These results show that a rye CC–based soybean conservation production system could be an economically a viable choice after the first year with an invaluable potential for carbon sequestration, weed suppression and positive impact on summer soybean productivity.

Many options exist for developing and implementing monitoring systems for research and scientific applications. Commercially, available systems and devices, however, are usually built using proprietary tools and programming instructions, and often offer limited flexibility for end users. The use of open-source hardware and software has been embraced by the research and scientific communities and can be used to target unique data and information requirements. Development based on the Arduino microcontroller project has resulted in many successful applications, and the Arduino hardware and software environment continues to expand and become more powerful but can be intimidating for users with limited electronics or programming experience. The open-source Python language has gained in popularity and is being taught in schools and universities as an introduction to computer programming and software development due to its simple structure, ease of use, and large standard library of functions. A project called CircuitPython was developed to extend the use of Python to programming hardware devices such as programmable microcontrollers and maintains much of the original Python language and features, with additional support for accessing and controlling microcontroller hardware. The objective of the work reported here is to discuss the CircuitPython programming language and demonstrate its use in the development of research and scientific applications. Several open-source sensing and monitoring systems developed using open-source hardware and the open-source CircuitPython programming language are presented and described.

Soybean [Glycine max(L.) Merr.] and cotton (Gossypium hirsutumL.) are the major row crops in the USA, and growers are tending toward the twin-row system and irrigation to increase productivity. In a 2-year study (2018 and 2019), we examined the gas exchange and chlorophyll fluorescence parameters to better understand the regulatory and adaptive mechanisms of the photosynthetic components of cotton and soybean grown under varying levels of irrigations and planting geometries in a split-plot experiment. The main plots were three irrigation regimes: (i) all furrows irrigation (AFI), (ii) alternate or skipped furrow irrigation (SFI), and iii) no irrigation or rainfed (RF), and the subplots were two planting patterns, single-row (SR) and twin-row (TR). The light response curves at vegetative and reproductive phases revealed lower photosynthesis rates in the RF crops than in AFI and SFI. A higher decrease was noticed in RF soybean for light compensation point (LCP) and light saturation point (LSP) than that of RF cotton. The decrease in the maximum assimilation rate (Amax) was higher in soybean than cotton. A decrease of 12 and 17% in Amax was observed in RF soybean while the decrease is limited to 9 and 6% in RF cotton during the 2018 and 2019 seasons, respectively. Both stomatal conductance (gs) and transpiration (E) declined under RF. The moisture deficit stress resulted in enhanced operating quantum efficiency of PSII photochemistry (ΦPSII), which is probably due to increased photorespiration. The non-photochemical quenching (NPQ), a measure of thermal dissipation of absorbed light energy, and quantum efficiency of dissipation by down-regulation (ΦNPQ) increased significantly in both crops up to 50% under RF conditions. The photochemical quenching declined by 28% in soybean and 26% in cotton. It appears soybean preferentially uses non-photochemical energy dissipation while cotton uses elevated electron transport rate (ETR) under RF conditions for light energy utilization. No significant differences among SR and TR systems were observed for LCP, LSP, AQE, Amax, gs, E, ETR, and various chlorophyll fluorescence parameters. This study reveals preferential use of non-photochemical energy dissipation in soybean while cotton uses both photochemical and non-photochemical energy dissipation to protect PSI and PSII centers and ETR, although they fall under C3 species when exposed to moisture limited environments.

Addressing the challenges of dwindling groundwater resources and ever-increasing demands for water necessitate enhancing water use efficiency (WUE) in irrigated agriculture. In a 2-year study, we examined the effects of different levels of irrigation and PG on lint yield and WUE of furrow irrigated cotton in a Dundee silt loam in the Mississippi Delta. The main plots were three irrigation regimes: irrigating every furrow (FI), alternate furrow (HI), and no irrigation (RF) and subplots were two planting geometries (PG): single-row (SR) and twin-row (TR). Across FI and HI no significant differences were observed in plant height and biomass yield at flowering, but chlorophyll content index and leaf area index (LAI) were positively affected. Canopy closure in TR planting occurred earlier than SR leading to higher leaf areas available for harvesting more light during photosynthesis. Averaged across the irrigation regimes, the TR planting enhanced lint yield by 10.6% in 2018 and 17.6% in 2019 compared to SR. The average lint yield in SR and TR were: 1779 and 2028 kg ha-1 under FI, 1803 and 2082 kg ha-1 under HI, and 1573 and 1788 kg ha-1 under RF treatments, respectively. In FI and HI treatments, TR had higher lint yield than RF treatment by 13.8% and 16.5%, respectively. Lint yield in HI with TR had the highest irrigation WUE (3.4 kg ha-1 mm-1) followed by HI with SR (2.7 kg ha-1 mm-1). These results demonstrated that cotton grown in TR with HI could reduce irrigation water demand in silt loams.

A variety of sensing and monitoring systems have been developed based on the concept of open-source and on open-source hardware and software components. Availability and relatively low cost of hardware components and availability and ease of use of software components allow access to sensing and monitoring technologies that were previously unattainable to many potential users. Advances in electronic monitoring and evolving cellular communications technologies are increasingly offering more, simpler, and less expensive options for remote monitoring. Due to the near-future cessation of 2G and 3G cellular network services, however, many existing monitoring systems will need to be redesigned to operate on alternative cellular networks. A soil-moisture monitoring system was developed incorporating updated open-source Arduino microcontrollers and the recently introduced LTE Cat-M1 cellular network to transmit sensor measurements via the cellular network for access on an internet website. The monitoring system costs approximately US$130 to construct the electronic circuitry and less than US$1 per month for cellular network access and data transmission. Data were transmitted with a 95% success rate, and the monitoring system operated continuously throughout an entire crop growing season with no battery recharge or maintenance requirements. The design and operation of the monitoring system can serve as a basis for other remote monitoring systems.

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.

The objective of this study was to test the effects of N rates and tillage radish (Raphanus sativus var. longipinnatus) cover crop (TRCC) on soil water and cotton (Gossypium hirsutum L) yield. In three years of the investigation, the treatments were N rates at 84 kg ha−1 and 140 kg ha−1 with and without TRCC. Soil water contents were measured using soil water sensors. Results showed that cotton yield was not significantly (p &gt, 0.05) influenced by TRCC. Compared to N rate at 84 kg ha−1, 140 kg N ha−1 increased lint yield by 2.0%, 7.4%, 18.4% in 2017, 2018, and 2019, respectively, but the increase was significant only in 2019 (p &lt, 0.02). Interactions between TRCC and nitrogen rate on yield were significant (p &lt, 0.03) only in 2017. TRCC increased soil water infiltration capacity, resulting in higher soil water content. Use of TRCC did not affect the cotton yield, which could be due to the high inputs of water and high rates of N neutralizing the positive contributions to the cotton growth expected from the TRCC. Sub-optimum winter temperatures hampered the establishment and subsequent growth of TRCC, which also possibly contributed to its minimum impacts on cotton crop performance in the following season.

Livestock producers often resort to either baling or grazing of crop residues due to high hay prices and reduced supply of other forages and silage in the markets. Soil-water-crop management practices can affect residue nutrient qualities for its use as cattle feedstock. A two-year study (2018–2019) was conducted to investigate the effects of irrigation (AI, all row-irrigation, ARI, alternate row irrigation, and RF, rainfed) and planting pattern, PP (SR, single row, and TR, twin-row) on soybean (maturity group IV cv. 31RY45 Dyna-Gro) post-grain harvest residue quality such as crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), acid detergent lignin (ADL), net energy for maintenance (NEM), net energy for gain (NEG), net energy for lactation (NEL), total digestible nutrients (TDN), and relative feed value (RFV). Irrigation has a significant effect on CP, ADF, NDF, and TDN while PP affected only NDF. All the above parameters were significantly affected except NEM by the contrasting climate conditions, particularly during July through August coinciding with early crop reproductive stages and maturity. The RFV values ranged from 70.4 to 81.6 and this lower range was attributable to nutrient translocation to seeds and higher lignification during plant senescence towards the grain filling stage of the crop as good quality hay records over 120 RFV. These results indicate that both irrigation and weather during soybean seed development can alter post-grain harvest residue quality parameters, thereby playing critical roles in its RFV.

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.

The use of fossil fuels for energy needs increases atmospheric greenhouse gas (GHG) concentrations to levels that can significantly exacerbate the climate on earth. Assessing the vulnerability of regional crop production systems to such an altered climate in the future is essential for implementing appropriate adaptation and mitigation strategies for sustainable agriculture. We investigated the possible impacts of climate change (CC) projected by multiple global climate models (GCMs) on rainfed and irrigated corn (Zea mays L., a C4 plant) in the Lower Mississippi Delta region (LMD), USA. The CSM-CROPGRO-Maize v4.6 module in the RZWQM2 model (hereafter referred to as the “corn model”) was previously calibrated and validated for modeling corn at Stoneville, Mississippi, a representative location in the LMD was used. The CC scenarios considered in this study were ensembles of climate projections of multiple GCMs (97 ensemble members) that participated in the Climate Model Inter-comparison and Improvement Program 5. These CC scenarios were bias-corrected and spatially downscaled (BCSD) at the location for the years 2050 and 2080. Four representative GHG concentration pathways (RCP) 2.6, 4.5, 6.0, and 8.5 drove these CC scenarios. Under both irrigated and rainfed conditions, corn yield responses to enhanced CO2 were weak; thus, yield declined significantly in response to the enhanced air temperatures under all the RCP scenarios in both 2050 and 2080. The yield declines across RCPs ranged between 10 and 62% under irrigated conditions, and between 9 and 60% under rainfed conditions, mainly due to increased frequency of extreme temperatures and reduced crop durations. Water use efficiency declined between 22 and 150% under irrigated, and 8 and 54% under rainfed management. As an adaptation measure, planting corn up to 9 weeks earlier in the season, in general, failed to boost yields from increased crop duration and reduction in upper extreme air temperatures, as incidences of lower extreme temperatures also increased alarmingly. Development of cultivars that are more heat tolerant and produce higher yields under extreme temperatures would be required to combat corn yield decline in the region from climate change.

International audience; Smallholder farmers in sub-Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low-input systems is currently lacking. We evaluated the impact of varying [CO2], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi-arid Rwanda, hot subhumid Ghana and hot semi-arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in-season soil water content from 2-year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low-input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.

Quantification of evapotranspiration (ETc) from crops is critical in irrigation scheduling in agriculture. In a pioneering study, in the Mississippi (MS) Delta region, we quantified ETc from soybean (Glycine max L.) using the eddy covariance (EC) approach (ETe). We also monitored ETc using a residual energy balance (EB) approach (ETb) and compared the fluxes. The unclosed energy fluxes in the EC were post-analysis closed using the Bowen ratio (BR) and latent heat (LH) methods. The measurements were conducted in a 35-ha clay soil planted to irrigated soybean in the lower MS Delta in 2016. The crop reached physiological maturity in 126 days after emergence (DAE). Maximum LAI was 5.7 and average grain yield was 4900 kg ha−1. The EC showed an energy balance closure of about 88% on a 30 min and 90% on a daily flux accumulation. The ETe was 18.2, 6.8, and 15.9% lower than ETb, and ETe corrected using BR (ETebr) and LH (ETele) approaches, respectively. Average soybean seasonal ETe, ETb, ETebr, and ETele were 422, 499, 451, and 490 mm, respectively. Seasonal reference-crop evapotranspiration for alfalfa (ETo) and grass (ETr) were 470 and 547 mm, respectively. Daily ETe, ETb, ETebr, ETele, ETo, and ETr averaged across the whole season were 4.4, 5.2, 4.7, 5.1, 4.9, and 5.7 mm, respectively. For scheduling irrigations, based on grass and alfalfa reference crop ET calculated from weather data, averages of the ETe, ETb, ETebr, and ETele daily estimates were used in deriving crop coefficients (Kc). The Kc for grass reference varied between 0.56 and 1.29 and for alfalfa reference varied between 0.56 and 1.02. The information developed will be useful for scheduling irrigations in the MS Delta region, and the methodology developed can be adapted for generating similar information elsewhere.

Rapid advances in electronics and communications technologies offer continuously evolving options for sensing and awareness of the physical environment. Many of these advances are becoming increasingly available to “non-professionals,” that is, those without formal training or expertise in disciplines such as electronic engineering, computer programming, or physical sciences, via the open-source concept. The open-source concept of collaboration and sharing of ideas offers advantages including low cost, ease of use, extensive array of electronic technologies offered, and technical and programming support. Expansion of communications infrastructure, including wireless, cellular, and internet networks, continues to provide greater ability to be connected and share information over any distance in real time. A basic data-collection platform using open-source hardware and software and internet cloud components was developed and discussed. The simple and inexpensive platform was used to develop and implement an instrument system to remotely monitor soil-moisture status in agricultural fields. The monitoring system transferred data regularly from the field to an internet website via the cellular communications network. The system performed reliably over an entire growing season with no maintenance requirements. The basic platform can be modified to suit a user’s specific requirements, and offers options for automated collection, viewing, and sharing of remotely sensed data.

Accurate quantification and management of crop evapotranspiration (ET) are critical to optimizing crop water productivity for both dryland and irrigated agriculture, especially in the semiarid regions of the world. In this study, four weighing lysimeters in Bushland, Texas, were planted to maize in 1994 with two fully irrigated and two non-irrigated for measuring crop ET. The Root Zone Water Quality Model (RZWQM2) was used to evaluate soil water balance and crop production with potential evapotranspiration (PET) estimated from either the Shuttleworth-Wallace method (PTSW) or the ASCE standardized alfalfa reference ET multiplied by crop coefficients (PTASCE). As a result, two water stress factors were defined from actual transpiration (AT) and were tested in the model against the lysimeter data, i.e., AT/PTSW and AT/PTASCE. For both water stress factors, the simulated daily ET values were reasonably close to the measured values, with underestimated ET during mid-growing stage in both non-irrigated lysimeters. Root mean squared deviations (RMSDs) and relative RMSDs (RMSD/observed mean) values for leaf area index, biomass, soil water content, and daily ET were within simulation errors reported earlier in the literature. For example, the RMSDs of simulated daily ET were less than 1.52 mm for all irrigated and non-irrigated lysimeters. Overall, ET was simulated within 3% of the measured data for both fully irrigated lysimeters and undersimulated by less than 11% using both stress factors for the non-irrigated lysimeters. Our results suggest that both methods are promising for simulating crop production and ET under irrigated conditions, but the methods need to be improved for dryland and non-irrigated conditions. Keywords: ET, RZWQM modeling, Stress factor, Weighing lysimeter.

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.

Agricultural management practices control soil organic carbon (SOC) content in croplands. Long-term cropping system experiments offer a great opportunity to understand the magnitude and direction of SOC change in response to management practices. Such information is very limited from the southeastern US, a region with warm and humid climatic conditions that typically favor SOC decomposition over accumulation. Therefore, this study was conducted to assess the effect of 39 years of chisel plow (CP), disc plow (DP), moldboard plow (MP), no-tillage (NT), NT with winter wheat (Triticum aestivum L.) cover crop (NTW), and NT with wheat-soybean (Glycine max L.) double crop (NTWD) on total SOC and SOC fractions including permanganate oxidizable C (POXC), water extractable C (WEC), resistant C (RC), and aggregate-associated SOC in a continuous soybean system. Additionally, aggregate size distribution, mean weight diameter (MWD), and wet aggregate stability (WAS) were determined. Results showed that NTW and NTWD significantly increased SOC and POXC compared to MP with mean SOC (g kg⁻¹ soil) of 12.2 (NTW) ≥10.9 (NTWD) >7.2 (MP) and mean POXC (mg kg⁻¹ soil) of 465 (NTWD) ≥418 (NTW) >252 (MP). The WEC and RC fractions did not differ among treatments. Across the treatments, the greatest aggregate-associated SOC concentration was found in microaggregates (0.053–0.25 mm) and the lowest in clay- and silt-size particles ( 0.6, p

Agricultural research involves study of the complex soil&ndash, plant&ndash, atmosphere&ndash, water system, and data relating to this system must be collected under often-harsh outdoor conditions in agricultural environments. Rapid advancements in electronic technologies in the last few decades, as well as more recent widespread proliferation and adoption of electronic sensing and communications, have created many options to address the needs of professional, as well as amateur, researchers. In this study, an agricultural research project was undertaken to collect data and examine the effects of different agronomic practices on yield, with the objectives being to develop a monitoring system to measure soil moisture and temperature conditions in field plots and to upload the data to an internet website. The developed system included sensor nodes consisting of sensors and electronic circuitry to read and transmit sensor data via radio and a cellular gateway to receive node data and forward the data to an internet website via cellular infrastructure. Microcontroller programs were written to control the nodes and gateway, and an internet website was configured to receive and display sensor data. The battery-powered sensor nodes cost $170 each, including electronic circuitry and sensors, and they were operated throughout the cropping season with little maintenance on a single set of batteries. The solar-powered gateway cost $163 to fabricate, plus an additional cost of $2 per month for cellular network access. Wireless and cellular data transmissions were reliable, successfully transferring 95% of sensor data to the internet website. Application of open-source hardware, wireless data transfer, and internet-based data access therefore offers many options and advantages for agricultural sensing and monitoring efforts.

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 CO

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 1.6 t ha− 1 for rainfed soybean and 4.9 t ha− 1 for irrigated soybean. The magnitudes of diurnal NEE (~ 2-weeks average) reached seasonal peak values of − 23.18 and − 34.78 μmol m− 2 s− 1 in rainfed and irrigated soybean, respectively, approximately two months after planting (i.e., during peak growth). Similar thresholds of air temperature (Ta, slightly over 30 °C) and vapor pressure deficit (VPD, ~ 2.5 kPa) 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 9.95 g C m− 2 d− 1 in rainfed soybean and − 7.48, 18.13, and 14.93 g C m− 2 d− 1 in irrigated soybean, respectively. The growing season (DOY 132–243) NEE, GPP, and ER totals were − 54, 783, and 729 g C m− 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 45 days after planting) were − 291, 1239, and 948 g C m− 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.

Networks of remotely monitored research sites are increasingly the tool used to study regional agricultural impacts on carbon and water fluxes. However, key national networks such as the National Ecological Observatory Network and AmeriFlux lack contributions from the Lower Mississippi River Basin (LMRB), a highly productive agricultural area with opportunities for soil carbon sequestration through conservation practices. The authors describe the rationale to create the new Delta-Flux network, which will coordinate efforts to quantify carbon and water budgets at seventeen eddy covariance flux tower sites in the LMRB. The network structure will facilitate climate-smart management strategies based on production-scale and continuous measurements of carbon and water fluxes from the landscape to the atmosphere under different soil and water management conditions. The seventeen instrumented field sites are expected to monitor fluxes within the most characteristic landscapes of the target area: row-crop fields, pasture, grasslands, forests, and marshes. The network participants are committed to open collaboration and efficient regionalization of site-level findings to support sustainable agricultural and forestry management and conservation of natural resources.

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.

Anthropogenic activities continue to emit potential greenhouse gases (GHG) into the atmosphere leading to a warmer climate over the earth. Predicting the impacts of climate change (CC) on food and fiber production systems in the future is essential for devising adaptations to sustain production and environmental quality. We used the CSM-CROPGRO-cotton v4.6 module within the RZWQM2 model for predicting the possible impacts of CC on cotton (Gossypium hirsutum) production systems in the lower Mississippi Delta (MS Delta) region of the USA. The CC scenarios were based on an ensemble of climate projections of multiple GCMs (Global Climate Models/General Circulation Models) for climate change under the CMIP5 (Climate Model Inter-comparison and Improvement Program 5) program, that were bias-corrected and spatially downscaled (BCSD) at Stoneville location in the MS Delta for the years 2050 and 2080. Four Representative Concentration Pathways (RCP) drove these CC projections: 2.6, 4.5, 6.0, and 8.5 (these numbers refer to radiative forcing levels in the atmosphere of 2.6, 4.5, 6.0, and 8.5 W·m−2), representing the increasing levels of the greenhouse gas (GHG) emission scenarios for the future, as used in the Intergovernmental Panel on Climate Change-Fifth Assessment Report (IPCC-AR5). The cotton model within RZWQM2, calibrated and validated for simulating cotton production at Stoneville, was used for simulating production under these CC scenarios. Under irrigated conditions, cotton yields increased significantly under the CC scenarios driven by the low to moderate emission levels of RCP 2.6, 4.5, and 6.0 in years 2050 and 2080, but under the highest emission scenario of RCP 8.5, the cotton yield increased in 2050 but declined significantly in year 2080. Under rainfed conditions, the yield declined in both 2050 and 2080 under all four RCP scenarios; however, the yield still increased when enough rainfall was received to meet the water requirements of the crop (in about 25% of the cases). As an adaptation measure, planting cotton six weeks earlier than the normal (historical average) planting date, in general, was found to boost irrigated cotton yields and compensate for the lost yields in all the CC scenarios. This early planting strategy only partially compensated for the rainfed cotton yield losses under all the CC scenarios, however, supplemental irrigations up to 10 cm compensated for all the yield losses.

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.

Areas of North America with high density dryland farming include the Canadian Prairies, U.S. and Mexican Great Plains, and the inland pacific northwest of the U.S, with wheat (Tritcum aestivum L.) being the dominant crop. Dryland farming is less dense but important in nearly every state in the western U.S and in northern and central Mexico. In addition to wheat, North American dryland farming is important for the production of maize (Zea maize L.), sorghum (Sorghum bicolor L.), pulses, and oilseeds. The traditional and still prevalent cropping system is a two-year rotation of wheat and summer fallow. In this traditional practice, shallow tillage is used during fallow periods to control weeds and help store moisture in the soil. Sustainability of this practice is limited by soil degradation and erosion and poor water use efficiency. Where adopted, no-till practices improve precipitation storage and use efficiency, which has led to crop intensification and diversification and improvements in soil properties. This chapter highlights some current issues for dryland cropping in North America including integrated pest management for herbicide resistant weeds, diversification of crop rotations, soil carbon dynamics and residue management, and the application of models to aid decision making. Sustaining the dryland cropping systems of North America depends on research and application of practices that reverse past soil degradation, increase cropping system diversity, and apply integrated pest management strategies. Both experimental and modelling approaches are needed to address these challenges.

layers with both CT and NT in WF in western Nebraska and concluded that cropping intensification would be The Root Zone Water Quality Model (RZWQM) is a comprehennecessary to reverse the decline. Studies oriented toward sive agricultural system model with the capacity to predict crop–environmental response to varying soil and crop management systems. amelioration of adverse impacts of WF(CT) on soil qualOur objective was to evaluate RZWQM for its ability to simulate a ity and productivity increased substantially throughout 2-yr winter wheat (Triticum aestivum L.)–fallow (WF) rotation and a the Great Plains in recent years. Numerous research more complex wheat–corn (Zea mays L.)–fallow (WCF) rotation un- efforts emphasized developing better cropping and tillder tilled and no-till (NT) conditions on a Weld silt loam soil in semi- age practices for optimum use of available rainfall and arid northeastern Colorado. Measured data from all phases of both minimal environmental impact (Halvorson, 1990; Anrotations were compared with simulated values using root mean derson et al., 1999). To develop environmentally sound square error (RMSE) values to quantify the agreement. Soil water croppingsystemsasalternativestoWF(CT),fieldexperin different layers, total soil profile (180 cm) water contents, and grain iments were established in 1990 on a Weld silt loam soil yield were accurately predicted with RMSEs ranging between 0.055

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.

Climate change is expected to alter a multitude of factors important to agricultural systems, including pests, diseases, weeds, extreme climate events, water resources, soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration ([CO2]), temperature, andwater (CTW) will be the primary drivers of change in crop growth and agricultural systems. Therefore, establishing the CTW-change sensitivity of crop yields is an urgent research need and warrants diverse methods of investigation. Crop models provide a biophysical, process-based tool to investigate crop responses across varying environmental conditions and farm management techniques, and have been applied in climate impact assessment by using a variety of methods (White et al., 2011, and references therein). However, there is a significant amount of divergence between various crop models’ responses to CTW changes (R¨otter et al., 2011). While the application of a site-based crop model is relatively simple, the coordination of such agricultural impact assessments on larger scales requires consistent and timely contributions from a large number of crop modelers, each time a new global climate model (GCM) scenario or downscaling technique is created. A coordinated, global effort to rapidly examine CTW sensitivity across multiple crops, crop models, and sites is needed to aid model development and enhance the assessment of climate impacts (Deser et al., 2012)...

Unique, variable summer climate of the lower Mississippi (MS) Delta region poses a critical challenge to cotton producers in deciding when to plant for optimized production. Traditional 2–4 year agronomic field trials conducted in this area fail to capture the effects of long-term climate variabilities in the location for developing reliable planting windows for producers. Our objective was to integrate a four-year planting-date field experiment conducted at Stoneville, MS during 2005–2008 with long-term climate data in an agricultural system model and develop optimum planting windows for cotton under both irrigated and rainfed conditions. Weather data collected at this location from 1960 to 2015 and the CSM-CROPGRO-Cotton v4.6 model within the Root Zone Water Quality Model (RZWQM2) were used. The cotton model was able to simulate both the variable planting date and variable water regimes reasonably well: relative errors of seed cotton yield, aboveground biomass, and leaf area index (LAI) were 14%, 12%, and 21% under rainfed conditions and 8%, 16%, and 15% under irrigated conditions, respectively. Planting windows under both rainfed and irrigated conditions extended from mid-March to mid-June: windows from mid-March to the last week of May under rainfed conditions, and from the last week of April to the end of May under irrigated conditions were better suited for optimum yield returns. Within these windows, rainfed cotton tends to lose yield from later plantings, but irrigated cotton benefits; however, irrigation requirements increase as the planting windows advance in time. Irrigated cotton produced about 1000 kg·ha−1 seed cotton more than rainfed cotton, with irrigation water requirements averaging 15 cm per season. Under rainfed conditions, there is a 5%, 14%, and 27% chance that the seed cotton production is below 1000, 1500, and 2000 kg·ha−1, respectively. Information developed in this paper can help MS farmers in decision support for cotton planting.