Comparison of models of simazine transport and fate in the subsurface environment in a citrus farm

Contamination of groundwater by agrochemicals is now widely recognized as an extremely important environmental problem. Modern agricultural practices involve the combined use of irrigation with the application of large amounts of agrochemicals to maximize crop yield. Due to flood irrigation and natural runoff, agricultural activities might generate soil, surface water and groundwater contamination problems and leaching of pesticides. Modeling of the transport and fate of pesticides, such as simazine, may help understand the long-term potential risk to the subsurface environment. This paper illustrates a comparative study via the use of three different pesticide transport simulation models and the applicability of those models in determining the groundwater vulnerability to pesticides contamination in a citrus orchard located at the Lower Rio Grande Valley (LRGV). The three models used in the study are the pesticide root zone model-3 (PRZM-3), the pesticide analytical model (PESTAN) and integrated pesticide transport modeling (IPTM). The concentration values obtained from all three models are in agreement, and they show a decreasing trend from the surface through the vadose zone. The problem is how to use this information and, specifically, how to combine the testimony of a number of experts into a single useful judgment. With the aid of the fuzzy multiattribute decision making method, PRZM-3 is deemed as the most promising one for such precision farming applications.     

Conservation of Socioculturally Important Local Crop Biodiversity in the Oromia Region of Ethiopia: A Case Study

In this study, we surveyed diversity in a range of local crops in the Lume and Gimbichu districts of Ethiopia, together with the knowledge of local people regarding crop uses, socio-economic importance, conservation, management and existing threats. Data were collected using semistructured interviews and participant observation. The study identified 28 farmers' varieties of 12 crop species. Among these, wheat (Triticum turgidum) and tef (Eragrostis tef) have high intra-specific diversity, with 9 and 6 varieties respectively. Self-seed supply or seed saving was the main (80?%) source of seeds for replanting. Agronomic performance (yield and pest resistance), market demand, nutritional and use diversity attributes of the crop varieties were highlighted as important criteria for making decisions regarding planting and maintenance. Over 74?% of the informants grow a combination of "improved" and farmers' varieties. Of the farmers' varieties, the most obvious decline and/or loss was reported for wheat varieties. Introduction of improved wheat varieties, pest infestation, shortage of land, low yield performance and climate variability were identified as the principal factors contributing to this loss or decline. Appropriate interventions for future conservation and sustainable use of farmers' varieties were suggested.     

Prediction of Crop Production using Drought indices at Different Time Scales and Climatic Factors to Manage Drought Risk1

Sadat Noori, S.M., A.M. Liaghat, and K. Ebrahimi, 2011. Prediction of Crop Production Using Drought Indices at Different Time Scales and Climatic Factors to Manage Drought Risk. Journal of the American Water Resources Association (JAWRA) 48(1): 1‐9. DOI: 10.1111/j.1752‐1688.2011.00586.x Abstract: Drought causes great damage to rainfed and irrigated farming. Therefore, prediction of crop production during the drought period is essential in order to manage drought risk. Thus, proceeding to agricultural drought risk management can be very useful. This study shows the results of early crop prediction using the combination of climate factors and drought indices at different time scales. The study region was Hamadan, a semiarid region in Iran. The methodology demonstrated here has allowed the prediction of production several months before harvest. Moreover, the predictive models constructed have explained 89% of the temporal variability of wheat production. This method could be very efficient for managing crop production. Moreover, having clear prediction, decision makers can plan better for overcoming drought impacts to reduce crop uncertainty for farmers in insurance companies.     

INVOLVING WATERSHED STAKEHOLDERS: AN ISSUE ATTRIBUTE APPROACH TO DETERMINE WILLINGNESS AND NEED1

ABSTRACT: The development of effective solutions for addressing nonpoint source pollution on a watershed basis often involves watershed stakeholders. However, success in engaging stakeholders in collaborative decision making processes varies, as watershed managers are faced with the challenges inherent to finding the right process for the decisions needed and in successfully engaging stakeholders in that process. Two characteristics that may provide guidance for determining the appropriateness of applying a collaborative process to a watershed problem are the need to collaborate and the willingness of stakeholders to engage in a collaborative decision making process. By examining seven attributes of the issues confronted by stakeholders in a collaborative process, the consequences of these attributes on the need for collaboration and stakeholders' willingness to engage can be estimated. The issue attributes include: level of uncertainty, balance of information, risk, time horizon of effects, urgency of decision, distribution of effects, and clarity of problem. The issue attribute model was applied to two collaborative decision making processes conducted by the same watershed stakeholder group in a North Carolina coastal watershed. Need and willingness to engage did not coincide for either issue; that is, stakeholders were more willing to engage on the issue that required less need for their involvement.     

An imprecise fuzzy risk approach for water quality management of a river system

Uncertainty plays an important role in water quality management problems. The major sources of uncertainty in a water quality management problem are the random nature of hydrologic variables and imprecision (fuzziness) associated with goals of the dischargers and pollution control agencies (PCA). Many Waste Load Allocation (WLA) problems are solved by considering these two sources of uncertainty. Apart from randomness and fuzziness, missing data in the time series of a hydrologic variable may result in additional uncertainty due to partial ignorance. These uncertainties render the input parameters as imprecise parameters in water quality decision making. In this paper an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system subject to uncertainty arising from partial ignorance. In a WLA problem, both randomness and imprecision can be addressed simultaneously by fuzzy risk of low water quality. A methodology is developed for the computation of imprecise fuzzy risk of low water quality, when the parameters are characterized by uncertainty due to partial ignorance. A Monte-Carlo simulation is performed to evaluate the imprecise fuzzy risk of low water quality by considering the input variables as imprecise. Fuzzy multiobjective optimization is used to formulate the multiobjective model. The model developed is based on a fuzzy multiobjective optimization problem with max–min as the operator. This usually does not result in a unique solution but gives multiple solutions. Two optimization models are developed to capture all the decision alternatives or multiple solutions. The objective of the two optimization models is to obtain a range of fractional removal levels for the dischargers, such that the resultant fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision making. The methodology is demonstrated with a case study of the Tunga–Bhadra river system in India.     

Simulating the Impacts of Irrigation Levels on Soybean Production in Texas High Plains to Manage Diminishing Groundwater Levels

There is an increasing need to strategize and plan irrigation systems under varied climatic conditions to support efficient irrigation practices while maintaining and improving the sustainability of groundwater systems. This study was undertaken to simulate the growth and production of soybean [Glycine max (L.)] under different irrigation scenarios. The objectives of this study were to calibrate and validate the CROPGRO‐Soybean model under Texas High Plains’ (THP) climatic conditions and to apply the calibrated model to simulate the impacts of different irrigation levels and triggers on soybean production. The methodology involved combining short‐term experimental data with long‐term historical weather data (1951–2012), and use of mechanistic crop growth simulation algorithms to determine optimum irrigation management strategies. Irrigation was scheduled based on five different plant extractable water levels (irrigation threshold [ITHR]) set at 20%, 35%, 50%, 65%, and 80%. The calibrated model was able to satisfactorily reproduce measured leaf area index, biomass, and evapotranspiration for soybean, indicating it can be used for investigating different strategies for irrigating soybean in the THP. Calculations of crop water productivity for biomass and yield along with irrigation water use efficiency indicated soybean can be irrigated at ITHR set at 50% or 65% with minimal yield loss as compared to 80% ITHR, thus conserving water and contributing toward lower groundwater withdrawals. Editor's note: This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

VARIABILITY OF SOIL WATER PROPERTIES AND CROP YIELD IN A SLOPED WATERSHED1

ABSTRACT: Spatial distribution of soil and water properties and the correlations between them and crop yield were determined for a natural rainfall environment. Hydraulic conductivity, soil texture, water retention, and soil-water flux were variables used to investigate their relationship to crop yield using multiple regression techniques. Variations in crop yields on a watershed with a 3 to 4 percent slope and moderately erosive soils were related to soil-water characteristics and soil properties along slope and with depth. Climatic conditions to sustain crop growth and yield ranged from inadequate soil water in 1983 to adequate soil water in 1984. Crop yield was predicted with models using both available and measured soil-water content. Available water content provided a better model for the prediction of water yield and does not require field measurements of actual soil-water content. Soil water holding capacity was more significant for predicting crop yield in soils with moderate to high silt content than infiltrability of water into the soil.     

IMPACTS OF AGRICULTURAL DRAINAGE WELL CLOSURE ON CROP PRODUCTION: A WATERSHED CASE STUDY1

ABSTRACT: Much of north-central Iowa is characterized by flat topography, shallow depressions, and poor natural surface drainage. Land drainage systems comprising of tile drains and agricultural drainage wells (ADWs) are used as outlets for subsurface drainage of cropland under corn and soybean production. Studies have shown that these drainage systems, mainly the ADWs, are potential routes for agricultural chemicals to underground aquifers. To protect the region's vital groundwater resource, researchers are evaluating alternative outlets ranging from complete closure of existing ADWs (and creation of wetlands) to continued use of ADWs and chemical management in a comprehensive policy framework. This paper presents the results of a study designed to provide government jurisdictions, farmers, and land managers information for assessing the impact of closing ADWs on crop production. The study couples a geographic information systems database for a 471-hectare watershed in Humboldt County, Iowa, with a groundwater flow model (MODFLOW) and an empirical crop yield loss model to predict long-term effects of complete closure of ADWs on crop production. The cropland areas inundated and the relative crop yield loss due to ADW closure are determined as a function of long-term climatic data. The results indicate that elimination of drainage outlets in the watershed could result in ponding of low-lying areas and poorly drained soils, making them unsuitable for crop production. Such wetness also decreases the efficiency of production in the no-ponding areas by isolating fields, and the crop yield loss can be reduced by an annual average of about 18 percent.     

Multicriteria Group Decision-making Model for Environmental Conflict Analysis in the Cape Region,Mexico

This paper is concerned with developing a model for group decision making under multiple criteria. The multiple criteria group decision making (MCGDM)problem involves a set of feasible land use patterns that are evaluated on the basis of multiple, conflicting and noncommensurate criteria by a group of individuals. The model integrates the Analytic Hierarchy Process (AHP) and an integer mathematical programming method. The former provides a tool for structuring the decision problem and determining land suitability for different socio-economicactivities (the uses of land), the latter is used to identify the land use pattern that maximizes consensus among interest groups. The model is used to analyze environmental conflict over land resource allocation in the Cape Region of Mexico.     

Evaluation of the ADAPT model for simulating nitrogen dynamics in a tile-drained agricultural watershed in central Illinois

Assessing the accuracy of agronomic and water quality simulation models in different soils, land-use systems, and environments provides a basis for using and improving these models. We evaluated the performance of the ADAPT model for simulating riverine nitrate-nitrogen (NO3-N) export from a 1500-km2 watershed in central Illinois, where approximately 85% of the land is used for maize-soybean production and tile drainage is common. Soil chemical properties, crop nitrogen (N) uptake coefficient, dry matter ratio, and a denitrification reduction coefficient were used as calibration parameters to optimize the fit between measured and simulated NO3-N load from the watershed for the 1989 to 1993 period. The applicability of the calibrated parameter values was tested by using these values for simulating the 1994 to 1997 period on the same watershed. Willmott's index of agreement ranged from 0.91 to 0.97 for daily, weekly, monthly, and annual comparisons of riverine nitrate N loads. Simulation accuracy generally decreased as the time interval decreased. Willmott's index for simulated crop yields ranged from 0.91 to 0.99; however, observed crop yields were used as input to the model. The partial N budget results suggested that 52 to 72 kg N ha(-1) yr(-1) accumulated in the soil, but simulated biological N fixation associated with soybeans was considerably greater than literature values for the region. Improvement of the N fixation algorithms and incorporation of mechanisms that describe soybean yield in response to environmental conditions appear to be needed to improve the performance of the model.     

POLLUTANT RUNOFF MODELS: SELECTION AND USE IN DECISION MAKING1

Models for pollutant runoff can be useful in water quality management planning if appropriately structured for the problem at hand. Accordingly, a “top-down” approach is proposed for the examination of extant pollutant runoff models. The approach consists of the identification of objectives and attributes that reflect the needs of planners and decision makers when these models are used for water quality management planning. Ideally, the attributes should concern the effect of model information on improved decision making and the cost of model application. Practical difficulties with the first attribute necessitates substitution of surrogate attributes reflecting model appropriateness, resolution, and uncertainty. Common pollutant runoff models, in particular export coefficients and hydrology-driven simulation models, are found to have serious weaknesses on some of the attribute scales. The “top-down” approach leads to a set of desirable pollutant runoff model attributes; alternate modeling techniques are thus examined in order to identify promising future directions for model development. The focus of this examination is phosphorus, due to its importance in the eutrophication of surface waters. Models for both sediment-attached and dissolved phosphorus are considered. Among the conclusions is the belief that the partial contributing area concept can yield an effective yet simple simulation despite the variable and complex nature of runoff.     

基于双系统理论的消费者价格判断决策模型

参考价格理论是构建价格判断模型的传统理论,其价格判断模型是一种典型的推理性模型.根据双系统理论,人们的决策应是推理性和直觉性两类过程的交互作用,心理账户理论正是描述直觉性决策的基本理论之一.构建了基于参考价格与心理账户的双系统价格判断决策模型,对不同心理账户数据进行分析发现,被试者的目标价格决策过程具有显著差异.     

Farmers’ risk preference and the adoption of risk management strategies in Northern Ghana

The study used the hypothetical lottery-choice questions to measure risk aversion and a detailed survey collected data on input use, farm production and non-farm activities to specifically assess whether risk aversion, risk perceptions, and socioeconomic factors affect the risk management strategies of farm households in Northern Ghana. Risk aversion significantly increases crop diversification strategies of households but marginally reduces herbicide use by households. Market risk significantly increases the use of improved seed varieties and the application of inorganic fertiliser but reduces diversification into livestock production. Production risk largely increases diversification into livestock production. Farmers’ risk management strategies are affected by socioeconomic variables such as access to extension services, area cultivated, age and gender. Policy effort focused on building pliable on-farm crop related risk management strategies should aim at considering the risk aversion and the perception of market risk whilst those focused on livestock should focus on production risk.     

Multi-criteria risk management with the use of <Emphasis Type="Italic">DecernsMCDA</Emphasis>: methods and case studies

Emerging challenges of risk management, environmental protection, and land-use planning requires integration of stakeholder values and expert judgment. The process of decision making in situation of high uncertainty can be assisted through the use of decision support systems (DSSs). Such DSSs are often based on tools for spatial data representation (GIS) and environmental models that are integrated using multi-criteria decision analysis (MCDA). This paper presents DecernsMCDA implementing all major types of multi-criteria methods and tools (AHP, MAUT, Outranking) under the same user interface. In addition to providing ability for testing model uncertainty associated with selection of specific MCDA algorithms, DecernsMCDA implements new algorithms for parameter uncertainty analysis based on probabilistic approaches and fuzzy sets. The paper illustrates application of DecernsMCDA for selecting remedial alternative at radiologically contaminated sites.     

PREDICTIVE ASSESSMENT OF SEVERITY OF AGRICULTURAL DROUGHTS BASED ON AGRO-CLIMATIC FACTORS1

ABSTRACT: Past historical evidence indicates that droughts have had great impacts on human life. Drought (or scarcity of water) is assessed based on two key factors, namely, the estimated water demand, and the expected water supply. The formulation of these key factors for a region largely depends on the agro-climatic and economic conditions. Consideration of one such key factor is the relationship between the crop yield and water deficit in the assessment and prediction of agricultural droughts. The varying nature of this relationship from crop to crop adds to the complexity of agricultural drought analysis. To overcome this difficulty in analyzing agricultural droughts of a region, it is adequate to consider and place emphasis on a single crop (i.e., an index crop) grown homogeneously over the major area of the region. From one year to another year, the pattern of water requirement during the growing season of an index crop is rather stationary, and the water supply in arid and semi-arid area is mainly from seasonal random precipitation. In a region, grain yield of the index crop and, in turn, assessment of the severity of drought can reasonably be predicted as a function of the time of crop sowing and the distribution of rainfall, provided that temporal and spatial effects of other contributing factors (crop variety, soil fertility status, crop disease, pest control, cultivation practices etc.) on grain yield are considered to be uniformly distributed (i.e., stable). A predictive method of assessing agricultural droughts in an arid area of western India is presented. The major crop (Pearl Millet) of this region is grown from. July through September. The formulation of the proposed predictive method inherently implies that the grain yield of the main crop is a reliable indicator of agricultural drought. In the development of this predictive relationship (i.e., a regression type model) a number of potential yet simple variables affecting the grain yield in the region were investigated. The soil moisture index, although generally considered significant compared to the simple variables, has been found to account for insignificant variation in the grain yield. Results of our investigations suggest that it would be advisable to exclude the soil moisture index variable from the model. The proposed regression model can be used in the prediction of grain yield of the main crop several months ahead of crop harvesting operations and, in turn, the assessment of agricultural drought severity as mild, moderate, or severe. Such an assessment is expected to be helpful to planners for arranging appropriate measures to effectively combat agricultural drought situations.     

A Streamflow Forecasting Framework using Multiple Climate and Hydrological Models1

Abstract: Water resources planning and management efficacy is subject to capturing inherent uncertainties stemming from climatic and hydrological inputs and models. Streamflow forecasts, critical in reservoir operation and water allocation decision making, fundamentally contain uncertainties arising from assumed initial conditions, model structure, and modeled processes. Accounting for these propagating uncertainties remains a formidable challenge. Recent enhancements in climate forecasting skill and hydrological modeling serve as an impetus for further pursuing models and model combinations capable of delivering improved streamflow forecasts. However, little consideration has been given to methodologies that include coupling both multiple climate and multiple hydrological models, increasing the pool of streamflow forecast ensemble members and accounting for cumulative sources of uncertainty. The framework presented here proposes integration and offline coupling of global climate models (GCMs), multiple regional climate models, and numerous water balance models to improve streamflow forecasting through generation of ensemble forecasts. For demonstration purposes, the framework is imposed on the Jaguaribe basin in northeastern Brazil for a hindcast of 1974‐1996 monthly streamflow. The ECHAM 4.5 and the NCEP/MRF9 GCMs and regional models, including dynamical and statistical models, are integrated with the ABCD and Soil Moisture Accounting Procedure water balance models. Precipitation hindcasts from the GCMs are downscaled via the regional models and fed into the water balance models, producing streamflow hindcasts. Multi‐model ensemble combination techniques include pooling, linear regression weighting, and a kernel density estimator to evaluate streamflow hindcasts; the latter technique exhibits superior skill compared with any single coupled model ensemble hindcast.     

A MULTIOBJECTIVE,DISCRETE SYSTEM REPRESENTATION OF RANGELAND WATERSHEDS1

ABSTRACT: Resource utilization as practiced by humans is identified as the main cause of the degradation of rangeland watersheds, a process referred to as desertification. This paper introduces a multi-objective decision making methodology for the selection of a plan which if implemented should limit the desertification process. The evaluation objectives are measured by quantitative and qualitative criteria. The quantitative criteria provide the performance level for production and sediment yield, while the qualitative criteria give such information as public acceptance of a particular alternative or worth of an appropriate wildlife habitat. A system model is applied to describe the dynamics of a range site in response to climatic and human inputs. As such it provides the information required by the quantitative criteria as well as a range condition index that identifies the productivity of a given range site. A multiobjective selection procedure is presented that will lead to the appropriate technique from an available set, in this case ESAP, Environmental Sensitivity Analysis Package. Four individuals with diverse backgrounds in natural resource management participated as decision makers and decided on their preferred alternatives. Finally, ranked alternatives in agreement with all of the decision makers were obtained.     

Multicriteria evaluation of simulated logging scenarios in a tropical rain forest

Forest growth models are useful tools for investigating the long-term impacts of logging. In this paper, the results of the rain forest growth model FORMIND were assessed by a multicriteria decision analysis. The main processes covered by FORMIND include tree growth, mortality, regeneration and competition. Tree growth is calculated based on a carbon balance approach. Trees compete for light and space; dying large trees fall down and create gaps in the forest. Sixty-four different logging scenarios for an initially undisturbed forest stand at Deramakot (Malaysia) were simulated. The scenarios differ regarding the logging cycle, logging method, cutting limit and logging intensity. We characterise the impacts with four criteria describing the yield, canopy opening and changes in species composition. Multicriteria decision analysis was used for the first time to evaluate the scenarios and identify the efficient ones. Our results plainly show that reduced-impact logging scenarios are more 'efficient' than the others, since in these scenarios forest damage is minimised without significantly reducing yield. Nevertheless, there is a trade-off between yield and achieving a desired ecological state of logged forest; the ecological state of the logged forests can only be improved by reducing yields and enlarging the logging cycles. Our study also demonstrates that high cutting limits or low logging intensities cannot compensate for the high level of damage caused by conventional logging techniques.     

Importance of Crop Yield in Calibrating Watershed Water Quality Simulation Tools1

Surendran Nair, Sujithkumar, Kevin W. King, Jonathan D. Witter, Brent L. Sohngen, and Norman R. Fausey, 2011. Importance of Crop Yield in Calibrating Watershed Water Quality Simulation Tools. Journal of the American Water Resources Association (JAWRA) 47(6):1285–1297. DOI: 10.1111/j.1752‐1688.2011.00570.x Abstract: Watershed‐scale water‐quality simulation tools provide a convenient and economical means to evaluate the environmental impacts of conservation practices. However, confidence in the simulation tool’s ability to accurately represent and capture the inherent variability of a watershed is dependent upon high quality input data and subsequent calibration. A four‐stage iterative and rigorous calibration procedure is outlined and demonstrated for Soil Water Analysis Tool (SWAT) using data from Upper Big Walnut Creek (UBWC) watershed in central Ohio, USA. The four stages and the sequence of their application were: (1) parameter selection, (2) hydrology calibration, (3) crop yield calibration, and (4) nutrient loading calibration. Following the calibration, validation was completed on a 10 year period. Nash‐Sutcliffe efficiencies for streamflow over the validation period were 0.5 for daily, 0.86 for monthly, and 0.87 for annual. Prediction efficiencies for crop yields during the validation period were 0.69 for corn, 0.54 for soybeans, and 0.61 for wheat. Nitrogen loading prediction efficiency was 0.66. Compared to traditional calibration approaches (no crop yield calibration), the four‐stage approach (with crop yield calibration) produced improved prediction efficiencies, especially for nutrient balances.     

SELECTING A MULTICRITERION DECISION MAKING TECHNIQUE FOR WATERSHED RESOURCES MANAGEMENT1

ABSTRACT: A method for selecting a most appropriate multicritenon decision making (MCDM) technique for watershed resources management is modeled as a multicriterion problem. The procedure consists of identifying a set of feasible MCDM techniques, and evaluating them with respect to four sets of choice criteria, namely (1) problem related, (2) decision-maker (DM)/analyst related, (3) technique-related, and (4) solution-related criteria. Altogether 15 techniques are evaluated in terms of 24 criteria, forming an evaluation matrix of criteria versus alternative MCDM techniques. The evaluation matrix is then analyzed by means by a composite programming algorithm resulting in a preference ranking of the alternatives. Application of the techniques to a watershed resources management problem illustrates the method throughout the paper. (KEY TERMS: alternative selection; watershed resources management; multicriterion decision making; composite programming.)