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

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... 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. Agronomy 2016, 6, 46 2 of 15 at the end of the season depend on many other factors, such as incidences and control of weeds, insects, diseases, and water availability either through rainfall or irrigation. Notwithstanding, the net return to the farmers depends on additional factors such as cotton fiber quality, seed composition, and market prices at the time of harvest (Bridge et al., 1971 [3]; Pettigrew and Dowd, 2011 [4]). Therefore, from a producer's point of view, a planting decision for optimum production and economic return should take all of these factors into account. Considerable research efforts have been invested into conducting field trials to understand and develop optimum planting dates for maximizing cotton production and returns in the MS Delta region. Wrather et al. (2008) [5] investigated effects of five planting dates from 22 April to 20 May on yield and fiber quality; Adams et al. (2013) [6] explored mid-April to early May planting-date effects on maturity and yield in cotton; and Pettigrew et al. (2009) [7], Pettigrew and Meredith (2009) [8], and Pettigrew and Dowd (2011) [4] investigated effects of varying planting dates on cotton growth and lint production, cotton seed composition, lint yield components, and fiber quality. In general, in all of these experiments, cotton was planted on three to four different dates between April and May and was repeated over three to four crop seasons. High variability in precipitation and temperatures experienced in the MS Delta across years confounded research outcomes to the extent that no definite patterns in cotton yield return appeared to emerge from these experiments for transferring the technology-or recommending definite planting windows and their associated risks-to producers for adoption. In a two-year study with four plantings between April and May, Berry et al. (2008) [9] recommended early plantings for cotton in the MS Delta region to avoid yield losses from tarnished plant bug attacks. Pettigrew et al. (2009) [7] planted cotton in the first week of April and first week of May for three years (2002-2005), and reported substantial (22%) increase in lint cotton yields from early plantings in one year but not in the other years. Pettigrew (2002) [10], (2010) [11] reported yield increases in four out of five years from a first-week-of-April planting compared to a first-week-of-May planting. In a four-year study from 2005 to 2008, Pettigrew and Dowd (2010) [12] observed a 35% decrease in lint yield from early plantings (first week of April) compared to a late planting (first week of May) under rainfed conditions, but in 2006 and 2007, early planting increased lint yield by 13% under irrigated, but not rainfed, conditions. One of the main factors contributing to the vacillating, and at times conflicting, outcomes from those experiments is the climate variability (precipitation amount and timing and air temperature) the crops are subjected to across the crop seasons. A solution to the problem would be to derive planting-date recommendations based on data collected-with frequent plantings spread uniformly across crop growth seasons over the long-term (30 years or more) to account for variations in precipitation and other climatic variables-at a location for deriving long-term averaged optimum planting dates for adoption. Such long-term experiments, obviously, are time-consuming and so not suited for deriving timely solutions keeping pace with the changes in technology in the cotton production scenario in the region. In this context, state-of-the-science agricultural system models provide a systems approach and a fast, scientifically sound method for extrapolating short-term field trials outside of the experimental period and climate by integrating crop simulation models with short-term field experiments and long-term weather data to account for long-term climate variability at the location (Hoogenboom et al., 1991 [13]; Ahuja et al., 2000 Ahuja et al., , 2014a; Saseendran et al., 2005 [17]; Garciay Garcia et al., 2010 [18]; Paz et al., 2012 [19]). However, process-oriented system models, such as Root Zone Water Quality Model (RZWQM2) and the Decision Support Systems for Agrotechnology Transfer (DSSAT) models, need to undergo a thorough calibration and validation process for ascertaining their abilities in reproducing the relevant crop responses in the climate and soil of the location for application (Jones et al., 2003 [20], Ahuja et al., 2014a,b [15,16]). In this context, the CSM-CROPGRO-cotton model available in the DSSAT cropping system simulation package was used extensively for simulating cotton for irrigation water management in the humid climate of southeastern USA (Guerra et al., 2007 [21]; Garcia y Garcia et al., 2010 [18]). However, studies on using the model for simulations of cotton in the humid MS Delta region are lacking. The CSM-CROPGRO-cotton model simulates growth and development of a cotton crop on a daily basis; however, photosynthesis