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.

Abstract: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.

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.

Agricultural research involves study of the complex soil–plant–atmosphere–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.

We assessed the potential profitability of twin‐row (TR) planting geometry as a water productivity‐enhancing practice and of skip‐row irrigation (SRI) as a water‐conserving practice in furrow‐irrigated cotton (Gossypium hirsutum L.) and soybean (Glycine max L.). Data from agronomic experiments carried on Dundee silt loam soils indicate that, on average, TR planting increases profitability by US$344 ha−1 for all‐row irrigated (ARI), $401 ha−1 for SRI, and $334  ha−1 for rainfed (RF) cotton. For soybean, the gains were $178 ha−1 for ARI irrigated, $178 ha−1 for SRI, and $121 ha−1 for RF. Converting from ARI to SRI irrigation can conserve 88 mm of irrigation water on average for cotton and 91 mm of irrigation water on average for soybean, while improving cotton profits by $55–113 ha−1. Soybean growers can expect a reduction in profits from SRI of ∼$11 ha−1. Incentive payment for soybean growers of ∼$0.132 mm−1 of saved water would compensate farmers for the expected losses of adopting SRI in the Delta of Mississippi.