Clyde W. Fraisse

Management Zone Analyst (MZA)

different management zones within a field? Two, how can information be processed into unique management Producers using site-specific crop management (SSCM) have a units (i.e., procedures for classification)? And three, how need for strategies to delineate areas within fields to which management can be tailored. These areas are often referred to as management many unique zones should a field be divided into? Quick, zones. Quick and automated procedures are desirable for creating efficient, and automated procedures are needed that admanagement zones and for testing the question of the number of zones dress these questions. to create. A software program called Management Zone Analyst A number of information sources have been used to (MZA) was developed using a fuzzy c-means unsupervised clustering delineate subfield management zones for SSCM. Tradialgorithm that assigns field information into like classes, or potential tional soil surveys often provide estimates of crop promanagement zones. An advantage of MZA over many other software ductivity for each soil map unit. In the USA, county programs is that it provides concurrent output for a range of cluster soil surveys report the average yield of major crops and numbers so that the user can evaluate how many management zones various soil properties by soil map unit; but the spatial should be used. Management Zone Analyst was developed using Microscale of county soil surveys has often been found inadesoft Visual Basic 6.0 and operates on any computer with Microsoft Windows (95 or newer). Concepts and theory behind MZA are prequate for use in SSCM (Mausbach et al., 1993). Digital sented as are the sequential steps of the program. Management Zone elevation data collected using global positioning systems Analyst calculates descriptive statistics, performs the unsupervised (GPS) or total station surveys have been used for classifuzzy classification procedure for a range of cluster numbers, and profying a field into management zones (McCann et al., vides the user with two performance indices [fuzziness performance 1996; Lark, 1998; MacMillan et al., 1998; van Alphen and index (FPI) and normalized classification entropy (NCE)] to aid in Stoorvogel, 1998). Fleming et al. (2000) used aerial phodeciding how many clusters are most appropriate for creating managetographs of bare soil along with landscape position and ment zones. Example MZA output is provided for two Missouri claythe management experience of the producer to delinpan soil fields using soil electrical conductivity, slope, and elevation as eate within-field management zones. Because of the clustering variables. Management Zone Analyst performance indices relationship of bulk soil apparent electrical conductivity indicated that one field should be divided into either two (using NCE) or four (using FPI) management zones and the other field should be (ECa) to productivity on some soils (Kitchen et al., 1999, divided into four (using NCE or FPI) management zones. 2003), it has been used in the delineation of management units. Sudduth et al. (1996) and Fraisse et al. (2001a) used a combination of topographic attributes and soil ECa to delineate management zones. Long et al. (1994) S crop management promotes the concept investigated the accuracy and precision of field manageof identification and management of regions within ment maps created from several sources [e.g., soil survey the geographic area defined by field boundaries. Often map, aerial photograph, overlaying class values of kriged referred to as management zones, these subfield regions point data in a geographic information system (GIS)]. typically represent areas of the field that are similar based They concluded that aerial photographs of growing crops on some quantitative measure(s) (e.g., topography, yield, were the most accurate and precise for classifying a field and soil-test nutrients). Determination of subfield areas into management units to predict grain yield. Imagery is difficult due to the complex combination of soil, biotic, of a growing crop and yield data collected in the same and climate factors that may affect crop yield. These year would be highly correlated and thus an accurate factors dynamically interact, further complicating the representation of crop production potential for that spedecisions of how to manage by zones. Three questions typically arise when considering managing by zones. One, cific year (Boydell and McBratney, 1999). what information should be used as a basis for creating Delineating zones based on topographic attributes and/or soil physical properties most often captures yield J.J. Fridgen, ITD/Spectral Visions, 20407 South Neil Street Suite 2, variability due to differences in plant available water and Champaign, IL 61820; N.R. Kitchen, K.A. Sudduth, and S.T. Drumthus, crop production potential (McCann et al., 1996; mond, USDA–ARS, Cropping Syst. and Water Quality Res. Unit, van Alphen and Stoorvogel, 1998; Fraisse et al., 2001a). Columbia, MO 65211; W.J. Wiebold, Dep. of Agron., University of Missouri, Columbia, MO 65211; and C.W. Fraisse, Agric. and Biol. Eng. Dep., Univ. of Florida, Gainesville, FL 32611. Received 22 Aug. Abbreviations: ECa, apparent soil electrical conductivity; FPI, fuzzi2002. *Corresponding author (kitchenn@missouri.edu). ness performance index; GIS, geographic information systems; ISODATA, Iterative Self-Organizing Data Analysis Technique; MZA, Published in Agron. J. 96:100–108 (2004).  American Society of Agronomy Management Zone Analyst; NCE, normalized classification entropy; SSCM, site-specific crop management. 677 S. Segoe Rd., Madison, WI 53711 USA

CALIBRATION OF THE CERES–MAIZE MODEL FOR SIMULATING SITE–SPECIFIC CROP DEVELOPMENT AND YIELD ON CLAYPAN SOILS

Crop simulation models have historically been used to predict field average crop development and yield under alternative management and weather scenarios. The objective of this research was to calibrate and test a new version of the CERES–Maize model, modified to improve the simulation of site–specific crop development and yield. Seven sites within a field located in central Missouri were selected based on landscape position, elevation, depth to a claypan soil horizon, and past yield history. Detailed monitoring of crop development and soil moisture during the 1997 season provided data for calibration and evaluation of model performance at each site. Mid–season water stress caused a large variation in measured yield with values ranging from 2.6 Mg ha–1 in the eroded side–slope areas to 10.1 Mg ha–1 in the deeper soils located in the low areas of the field. The model was calibrated against measured data for root zone soil moisture content, leaf area index, and grain yield. The results demonstrated that modifications included in the model to simulate root growth and development are important in soils with a high–clay restrictive layer such as the claypan soils. Although the model performed well in simulating yield variability, simulated leaf area indices were below measured values at five out of seven monitoring sites, suggesting a need for model improvements. Results showed that accurate simulation of crop growth and development for areas of the study field that receive run–on or subsurface flow contributions from upland areas will require enhancement of the model to account for the effects of these processes.

Warming up to climate change: a participatory approach to engaging with agricultural stakeholders in the Southeast US

Within the context of a changing climate, scientists are called to engage directly with agricultural stakeholders for the coproduction of relevant information that will support decision making and adaptation. However, values, beliefs, identities, goals, and social networks shape perceptions and actions about climate change. Engagement processes that ignore the socio-cultural context within which stakeholders are embedded may fail to guide adaptive responses. To facilitate dialog around these issues, the Southeast Climate Consortium and the Florida Climate Institute formed a climate learning network consisting of row crop farmers, agricultural extension specialists, researchers, and climate scientists working in the Southeast US. Regional in scope, the learning network engages researchers and practitioners from Alabama, Georgia, and Florida as partners in adaptation science. This paper describes the ongoing interactions, dialog, and experiential learning among the network’s diverse participants. We illustrate how participatory tools have been used in a series of workshops to create interactive spaces for knowledge coproduction. For example, historical timelines, climate scenarios, and technology exchanges stimulated discussions about climate-related risk management. We present findings from the workshops related to participants’ perspectives on climate change and adaptation. Finally, we discuss lessons learned that may be applicable to other groups involved in climate education, communication, and stakeholder engagement. We suggest that the thoughtful design of stakeholder engagement processes can become a powerful social tool for improving decision support and strengthening adaptive capacity within rural communities.

IMPACT OF CLIMATE INFORMATION ON REDUCING FARM RISK BY OPTIMIZING CROP INSURANCE STRATEGY

Predictability of seasonal climate variability associated with the El Nino Southern Oscillation (ENSO) suggests a potential to reduce farm risk by selecting crop insurance products with the purpose of increasing farm income stability. A hypothetical 50% peanut, 50% cotton, non-irrigated, 40 ha (100 ac) north Florida farm was used to study the interactions of different crop insurance products with ENSO-based climate information and levels of risk aversion under uncertain conditions of climate and prices. Crop yields simulated by the DSSAT suite of crop models using multiyear weather data combined with historical series of prices were used to generate long series of stochastic income distributions in a whole-farm model portfolio. The farm model optimized planting dates and simulated uncertain incomes for 50 alternative crop insurance combinations for different levels of risk aversion under different planning horizons. Results suggested that incomes are greatest and most stable for low risk-averse farmers when catastrophic (CAT) insurance for cotton and 70% or 75% actual production history (APH) for peanut are selected in all ENSO phases. For high risk-averse farmers, the best strategy depends on the ENSO phase: (1) 70% crop revenue coverage (CRC) or CAT for cotton and 65% APH for peanut during EL Nino years; (2) CAT for cotton and 65%, 70%, or 75% APH for peanut during neutral years; and (3) 65% to 70% APH, or CAT for cotton and 70% APH for peanut during La Nina years. Optimal planting dates varied for all ENSO phases, risk aversion levels, and selected crop insurance products.

Use of Global Sensitivity Analysis for CROPGRO Cotton Model Development

Crop models range in complexity from simple ones with a few state variables to complex ones having a large number of model parameters and state variables. Determining and understanding how sensitive the output of a model is with respect to model parameters is a guiding tool for model developers. A new cotton model is being developed using the Cropping System Model (CSM)-CROPGRO crop template that allows the introduction of a new crop and its integration with other modules such as soil and weather without changing any code. The main goal of this study was to investigate whether global sensitivity analysis would provide better information on the importance of model parameters than the simpler and commonly used local sensitivity analysis method. Additionally, we were interested in determining the most important crop growth parameters in predicting development and yield and if the model sensitivity to these parameters would vary under irrigated and rainfed conditions. Sensitivity analyses were performed on dry matter yield and length of season model responses for a wet cropping season (year 2003) and a dry cropping season (year 2000) under irrigated and rainfed conditions. Results indicated that global sensitivity analysis improved our understanding of the importance of the model parameters on model output relative to local sensitivity analysis. Results from global sensitivity analysis indicated that the specific leaf area under standard growth conditions (SLAVR) was the most important model parameter influencing cotton yield under both irrigated and rainfed conditions when taking into account its range of uncertainty. Results from local sensitivity analysis indicated that the light extinction coefficient (KCAN) was the most influencing model parameter. In both global and local sensitivity analyses, the duration between first seed and physiological maturity (SD-PM) was the most important parameter for season length response. The differences obtained for global vs. local sensitivity analysis can be explained by the inability of local sensitivity analysis to take into consideration the interactions among parameters, their ranges of uncertainty, and nonlinear responses to parameters.

Analysis of the Inter-Annual Variation of Peanut Yield in Georgia Using a Dynamic Crop Simulation Model

It is common practice to use crop simulation models and long-term weather data to study the impact of climate variability on yield. Simulated yields mainly reflect the weather variability but not the adoption of new technologies; both sources of variation are reflected in long-term observed yields. Therefore, long-term observed yields, if available, cannot be readily used for evaluation of crop models. The objectives of this study were to analyze the impact of climate variability on long-term historical peanut yield in Georgia obtained with a dynamic crop simulation model and to assess the applicability of using long-term average county yield determined from statistical estimates for evaluation of the simulated yield. Observed yields obtained from state variety trials as well as yield estimates from the USDA-NASS for three counties in the Georgia peanut belt from 1934 to 2003 were used for evaluating simulated yield series. Simulated yields based on the CSM-CROPGRO-Peanut model were categorized into three technological periods (TP). A weighted average based on the acreage of the soil type, the peanut type, and the irrigated land in each county was calculated to obtain a unique simulated yield. Then yields and weather data of the 70-year period were grouped with respect to El Nino Southern Oscillation phases and TPs. Pearsons coefficient of correlation, the least significant difference (LSD), and the t-test were used to evaluate the results. When compared with observed yields, NASS estimates failed to estimate the weather variability at the beginning of the period, but simulated yields clearly reflected that variability during the 70-year period. NASS yield estimates seemed to be useful for evaluating simulated yields from the mid-1970s. The results showed that crop models can be useful in understanding the inter-annual variation of yield due to climate variability if appropriate adjustments are made to account for changes and improvements in agrotechnology.

Optimizing Crop Insurance under Climate Variability

Abstract This paper studies the selection of optimal crop insurance under climate variability and fluctuating market prices. A model was designed to minimize farmers’ expected losses (including insurance costs) while using the conditional-value-at-risk measure to acquire the risk-aversion level. The application of the model was illustrated by studying a farm with two crops (cotton and peanut) in Jackson County, Florida. The climate variability was caused by ENSO phenomenon. Crop-insurance contracts with minimized losses were 75% actual production history (APH) during El Nino and neutral years and 65% APH during La Nina years for peanut and 75% APH in all ENSO phases for cotton. In addition, risk-averse farmers could select 75% APH for peanut during La Nina years as a means of attaining less expected loss.

Utilization of the cropgro-soybean model to estimate yield loss caused by Asian rust in cultivars with different cycle

In recent years, crop models have increasingly been used to simulate agricultural features. The DSSAT (Decision Support System for Agrotechnology Transfer) is an important tool in modeling growth; however, one of its limitations is related to the unac- counted-for effect of diseases. Therefore, the goals of this study were to calibrate and validate the CSM CROPGRO-Soybean for the soybean cultivars M-SOY 6101 and MG/BR 46 (Conquista), analyze the performance and the effect of Asian soybean rust on these cultivars under the environmental conditions of Vicosa, Minas Gerais, Brazil. The experimental data for the evaluation, testing, and adjustment of the genetic coefficients for the cultivars, M-SOY 6101 and MG/BR 46 (Conquista), were obtained during the 2006/2007, 2007/2008 and 2009/2010 growing seasons. GLUE (Generalized Likelihood Uncertainty Estimation) was used for the estimation of the genetic coefficients, and pedotransfer functions have been utilized to estimate the physical characteristics of the soil. For all of the sowing dates, the early season cultivar, M-SOY 6101, exhibited a lower variance in yield, which represents more stability with regard to the interannual climate variability, i.e., the farmers who use this cultivar will have in 50% of the crop years analyzed, a higher yield than a late-season cultivar. The MG/BR 46 (Conquista) cultivar demonstrated a greater probability of obtaining higher yield in years with favorable weather conditions. However, in the presence of the Asian soybean rust, yield is heavily affected. The early cultivar, M-SOY 6101, showed a lower risk of being affected by the rust and consequently exhibited less yield loss considering the scenario D90 (condensation on the leaf surface occurs when the relative humidity is greater than or equal to 90%), for a sowing date of November 14.

Optimal crop planting schedules and financial hedging strategies under ENSO-based climate forecasts

This paper investigates the impact of ENSO-based climate forecasts on optimal planting schedules and financial yield-hedging strategies in a framework focused on downside risk. In our context, insurance and futures contracts are available to hedge against yield and price risks, respectively. Furthermore, we adopt the Conditional-Value-at-Risk (CVaR) measure to assess downside risk, and Gaussian copula to simulate scenarios of correlated non-normal random yields and prices. The resulting optimization problem is a mixed 0–1 integer programming formulation that is solved efficiently through a two-step procedure, first through an equivalent linear form by disjunctive constraints, followed by decomposition into sub-problems identified by hedging strategies. With data for a representative cotton producer in the Southeastern United States, we conduct a study that considers a wide variety of optimal planting schedules and hedging strategies under alternative risk profiles for each of the three ENSO phases (Niña, Niño, and Neutral.) We find that the Neutral phase generates the highest expected profit with the lowest downside risk. In contrast, the Niña phase is associated with the lowest expected profit and the highest downside risk. Additionally, yield-hedging insurance strategies are found to vary significantly, depending critically on the ENSO phase and on the price bias of futures contracts.

Reduction in greenhouse gas emissions due to the use of bio-ethanol from wheat grain and straw produced in the south-eastern USA.

Biofuels can reduce greenhouse gas (GHG) emissions by replacing fossil fuels. However, the energy yield from agronomic crops varies due to local climate, weather and soil variability. A variation in the yield of raw material used (feedstock) could also cause variability in GHG reductions if biofuels are used. The goal of the present study was to determine the net reduction of GHG emissions if ethanol from wheat produced in different regions of the south-eastern USA is used as an alternative to gasoline from fossil fuel sources. Two scenarios were investigated; the first included ethanol produced from grain only, and the second included ethanol produced from both grain and wheat straw. Winter wheat yield was simulated with the Cropping System Model (CSM)-CERES-Wheat model for climate, soil and crop management representing six counties in the following USA states: Alabama, Florida and Georgia. Ethanol production was determined from the simulated grain and straw yields together with fixed grain and straw yield ethanol ratios. Subsequently, net reductions in GHG emissions were determined by accounting for the emissions from the replaced gasoline, and by animal feed and electricity that were replaced by ethanol processing co-products. Greenhouse gases that were emitted in the ethanol production chain were also taken into account. Across all locations, the reduction in GHG emissions was 187 g CO 2 -equivalents/km in the grain-only scenario and 208 g CO 2 -equivalents/km in the grain and straw scenario. The reductions in GHG emissions varied significantly between locations and growing seasons within the two scenarios. Similar approaches could be applied to assess the environmental impact of GHG emissions from other biofuels.