Simulate Crop Yield Research Articles

Stochastic Models to Generate Geospatial-, Temporal-, and Cross-Correlated Daily Maximum and Minimum Temperatures

Weather generators are tools used to downscale monthly to seasonal climate forecasts, from numerical climate models to daily values for use as inputs for crop and other environmental models. One main limitation of most of weather generators is that they do not incorporate neither the spatial/temporal correlations between/within sites nor the cross-correlations between variables, characteristics specially important when aggregating, for example, simulated crop yields, freeze events, or heat waves in a watershed or region. Three models were developed to generate realization of daily maximum and minimum temperatures for multiple sites. The first model incorporates only spatial correlation, whereas temporal correlation using a 1-day lag and cross-correlation between variables were added to model one, respectively, by the other two models. Vectors of correlated random numbers were rescaled to temperature values by multiplying each element with the standard deviation and adding the mean of the corresponding weather station. An extension of Crout's algorithm was developed to enable the factorization of nonpositive definite matrices. Monthly spatial correlations of generated daily maximum and minimum temperatures between all pairs of weather stations closely matched their observed counterparts. Performance was analyzed by comparing the root mean squared error, temporal semivariograms, correlation/cross-correlation matrices, multiannual monthly means, and standard deviations.

Using a crop simulation model to select the optimal climate grid cell resolution: A study case in Araucanía Region

Crop models are sensitivi by the climatic spatial scale for performing thesimulation. Several crop simulation studies use mesoescale climate database (20-50 km), where topography is neglected. We develop a method to select the optimal climate grid cell resolution (OCGR) based on winter wheat (Triticum aestivum L) yield simulations in complex topographical zones (CTZ) and flat topographical zones (FTZ) in the Araucania Region of Chile (37°35’ and 39°37’ S - 73o31´ and 71.31´ W). The OCGR was estimated from the simulated crop yield (CERES-DSSAT) using a semivariogram to compute the distance, which minimize yield differences with respect to its neighbors. Climate variables were obtained from DGF-PRECIS (25 km) downscaled to a fine resolution of 1 km through Precipitation characterization with Auto-Searched Orographic and Atmospheric (PCASOA). Climate variables were calibrated and validated from 56 in-situ meteorological stations between 1961 and 1991 and the yield was validated from field experiments. The crop simulation presented no significant differences (3.0±0.3–3.0±0.1 Mg ha -1 ) compared to field experiments. Increasing the resolution improves the crop simulation reducing the RSME from 0.8 to 0.32 Mg ha -1 The OCGR estimated averaged 25 km for FTZ. Our approach can be applied for similar crops and complex topographical zones.

Exploring the efficiency of bias corrections of regional climate model output for the assessment of future crop yields in Europe

Excessive summer drying and reduced growing season length are expected to reduce European crop yields in future. This may be partly compensated by adapted crop management, increased CO2 concentration and technological development. For food security, changes in regional to continental crop yield variability may be more important than changes in mean yields. The assessment of changes in regional and larger scale crop variability requires high resolution and spatially consistent future weather, matching a specific climate scenario. Such data could be derived from regional climate models (RCMs), which provide changes in weather patterns. In general, RCM output is heavily biased with respect to observations. Due to the strong nonlinear relation between meteorological input and crop yields, the application of this biased output may result in large biases in the simulated crop yield changes. The use of RCM output only makes sense after sufficient bias correction. This study explores how RCM output can be bias corrected for the assessment of changes in European and subregional scale crop yield variability due to climate change. For this, output of the RCM RACMO of the Royal Netherlands Meteorological Institute was bias corrected and applied within the crop simulation model WOrld FOod STudies to simulate potential and water limited yields of three divergent crops: winter wheat, maize and sugar beets. The bias correction appeared necessary to successfully reproduce the mean yields as simulated with observational data. It also substantially improved the year-to-year variability of seasonal precipitation and radiation within RACMO, but some bias in the interannual variability remained. This is caused by the fact that the applied correction focuses on mean and daily variability. The interannual variability of growing season length, and as a consequence the potential yields too, appeared even deteriorated. Projected decrease in mean crop yields is well in line with earlier studies. No significant change in crop yield variability was found. Yet, only one RCM is analysed in this study, and it is recommended to extend this study with more climate models and a slightly adjusted bias correction taking into account the variability of larger time scales as well.

A modeling approach to evaluating the impacts of policy-induced land management practices on non-point source pollution: A case study of the Liuxi River watershed, China

Conservation tillage and fertilization based on soil test have been promoted by the Chinese government in recent years. Their impacts on non-point source pollution were simulated using the Soil and Water Assessment Tool (SWAT) for the Liuxi River watershed, the only one local drinking-water-supply river basin of Guangzhou. The model was calibrated through comparing model outputs with observations to ensure reliable hydrologic, crop yield, and water quality simulations. The model results indicated that, 5% of total nitrogen (TN) load and 12% of total phosphorus (TP) load could be reduced at the watershed outlet by implementing the conservation tillage system of “rice parachute transplanting with no-tillage and straw mulching” for paddy rice fields which account for 9% of the watershed area. It is valuable to develop conservation tillage systems suitable for other types of agricultural land in southern China. When modeling the impacts of various fertilization levels derived from recommended fertilizer doses for medium fertility soil of the Pearl River Delta region where the watershed is located, the total crop yield was estimated to decrease by only 2% for up to 24% and 28% reduction in TN and TP loads at the watershed scale, respectively. The crop yields versus nutrient losses relationship simulated by SWAT indicated that great effects in TN and TP loads reduction could be achieved at the expense of minimal impacts on crop yield through optimal fertilization. The modeling approach presented in this study can be a useful tool for estimating the effects of policy instruments and pollution control measures.

Impact of derived global weather data on simulated crop yields

Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long-term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study's objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water-limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well-maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26–72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA-POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12–19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location-specific observed daily weather databases combined with an appropriate upscaling method.

Pan-European crop modelling with EPIC: Implementation, up-scaling and regional crop yield validation

Justifiable usage of large-scale crop model simulations requires transparent, comprehensive and spatially extensive evaluations of their performance and associated accuracy. Simulated crop yields of a Pan-European implementation of the Environmental Policy Integrated Climate (EPIC) crop model were satisfactorily evaluated with reported regional yield data from EUROSTAT for four major crops, including winter wheat, rainfed and irrigated maize, spring barley and winter rye. European-wide land use, elevation, soil and daily meteorological gridded data were integrated in GIS and coupled with EPIC. Default EPIC crop and biophysical process parameter values were used with some minor adjustments according to suggestions from scientific literature. The model performance was improved by spatial calculations of crop sowing densities, potential heat units, operation schedules, and nutrient application rates. EPIC performed reasonable in the simulation of regional crop yields, with long-term averages predicted better than inter-annual variability: linear regression R2 ranged from 0.58 (maize) to 0.91 (spring barley) and relative estimation errors were between ±30% for most of the European regions. The modelled and reported crop yields demonstrated similar responses to driving meteorological variables. However, EPIC performed better in dry compared to wet years. A yield sensitivity analysis of crop nutrient and irrigation management factors and cultivar specific characteristics for contrasting regions in Europe revealed a range in model response and attainable yields. We also show that modelled crop yield is strongly dependent on the chosen PET method. The simulated crop yield variability was lower compared to reported crop yields. This assessment should contribute to the availability of harmonised and transparently evaluated agricultural modelling tools in the EU as well as the establishment of modelling benchmarks as a requirement for sound and ongoing policy evaluations in the agricultural and environmental domains.

How satellite rainfall estimate errors may impact rainfed cereal yield simulation in West Africa

Rainfall monitoring via satellite sensors is particularly relevant for the agricultural sector of West Africa. Indeed, food shortages in this region are often caused by rainfall deficits and an early access to data available for the entire region can help to provide credible and timely information for better decision making. This study assesses the accuracy of state-of-the-art satellite rainfall retrievals for agriculture applications in two sites in Niger and Benin. Although these satellite data are widely used instead of rain gauge data for such applications, we found that, in a crop-modelling framework, their use can introduce large biases in crop yield simulations. Biases differ strongly among the four cultivars considered in both sites and are not simple extrapolation of each satellite product cumulative rainfall amount biases. In particular, we found that if an accurate estimation of the annual cumulative rainfall amount is important for yield simulations of pearl millet ‘Souna 3’ and ‘Somno’ cultivars in Niger, a realistic distribution of rainfall is also very important for predicting pearl millet ‘Somno’ and ‘HK’ yields in Niger as well as maize yields in Benin. Overall the satellite products tested, 3B42v6 appears to be the most suitable satellite product for our specific agricultural application since it minimizes both biases in rainfall distribution and in annual cumulative rainfall amount. For each crop and in both regions, biases in crop yield prediction are the highest when using non-calibrated satellite rainfall products (PERSIANN, 3B42RT, CMORPH and GSMAP).

Simulated impact of elevated CO2, temperature, and precipitation on the winter wheat yield in the North China Plain

We studied the separate and interacting effects of changes on CO2, temperature, and precipitation on the growth and yield of winter wheat in five representative sites on the North China Plain using a crop yield simulation model, known as the Environmental Policy Integrated Climate (EPIC) model. The daily-maximum/minimum temperature and precipitation data obtained using a comprehensive climate model, that is, the Model for Interdisciplinary Research On Climate (MIROC), based on the scenario A1B for 2085–2100 were calibrated using a novel statistical algorithm and used as the climate change scenario in the EPIC model. The results indicated that an increase in the CO2 concentration of up to 680 ppm would increase the winter wheat yield by 24.8 and 43.1 % in irrigated and rainfed fields, respectively. Increases in the average maximum temperature of up to 4.9 °C and the average minimum temperature of up to 4.8 °C would increase the crop yield by 5.2 % in irrigated condition, but decrease it by 7.2 % in rainfed condition. By contrast, the yield of irrigated field decreased by 5.5 % when the annual precipitation increased by 317 mm, whereas that of rainfed field increased by 30.1 %. The interacting effects of simultaneous increases in the parameters were also simulated. With a constant CO2 level (370 ppm), the EPIC model predicted that the effects of temperature and precipitation on yield would be −0.9 and −1.9 % for irrigated and rainfed fields, respectively. When the CO2 level increased to 680 ppm, the interacting effect of elevated CO2, temperature, and precipitation increased the average yield by ca 23.1 % with the irrigated treatment and by ca 27.7 % with the rainfed treatment. The results also indicated that with a climate change scenario, the temperature-stress days decreased during the period of winter wheat growth whereas the nitrogen-stress days increased significantly in the North China Plain. These simulated separate and interaction simulations may be useful for identifying appropriate management or genotype adaptations of winter wheat to cope with a climate change scenario in the North China Plain.

Assessing the effects of water table depth on water use, soil salinity and wheat yield: Searching for a target depth for irrigated areas in the upper Yellow River basin

The irrigation districts of the upper Yellow River basin face a progressive reduction of water allocation and the need to apply water-saving practices due to increasing water scarcity. The adoption of such practices will lead to lower water tables, hopefully in conjunction with controlled soil salinity levels and improved crop yields. However, excessive water saving associated with excessive increase of the water table depth may decrease capillary rise and affect crop production. In view of understanding the related processes in Qingtongxia Irrigation District, the physically based agro-hydrological model, SWAP, was adopted to explore the response of soil water and solute dynamics, and crop yield to water table changes. With this purpose, SWAP was modified through the inclusion of a simplified crop growth module, a method of variable active-nodes, and a nonlinear osmotic head-dependent function for a better description of the effects of salinity stress on root water uptake. The model was calibrated and validated using wheat's experimental data from 2007 and 2008. Simulations of soil water content, salinity concentration, biomass, and crop yield fitted well with field observations. The calibrated model was then used to predict changes in crop yield, soil water dynamics and soil salinity considering scenarios with different groundwater depths and irrigation strategies. The present irrigation strategy favors salt leaching even when considering the increase of water table depth that results in small crop yield reduction (<6%) due to salt stress. The 80% and 60% reduced irrigation strategies led to increased soil salinity and, eventually, crop yield reductions of 6–14% and 13–21%, respectively, when the depth of the water table increased. A target groundwater depth of 1.0–1.5m is suggested to be optimal for wheat's growth season with the aim of maintaining crop yields under the present conditions.

Agricultural Policy Environmental eXtender Simulation of Three Adjacent Row‐Crop Watersheds in the Claypan Region

The Agricultural Policy Environmental Extender (APEX) model is used to evaluate best management practices on pollutant loading in whole farms or small watersheds. The objectives of this study were to conduct a sensitivity analysis to determine the effect of model parameters on APEX output and use the parameterized, calibrated, and validated model to evaluate long-term benefits of grass waterways. The APEX model was used to model three (East, Center, and West) adjacent field-size watersheds with claypan soils under a no-till corn ( L.)/soybean [ (L.) Merr.] rotation. Twenty-seven parameters were sensitive for crop yield, runoff, sediment, nitrogen (dissolved and total), and phosphorous (dissolved and total) simulations. The model was calibrated using measured event-based data from the Center watershed from 1993 to 1997 and validated with data from the West and East watersheds. Simulated crop yields were within ±13% of the measured yield. The model performance for event-based runoff was excellent, with calibration and validation > 0.9 and Nash-Sutcliffe coefficients (NSC) > 0.8, respectively. Sediment and total nitrogen calibration results were satisfactory for larger rainfall events (>50 mm), with > 0.5 and NSC > 0.4, but validation results remained poor, with NSC between 0.18 and 0.3. Total phosphorous was well calibrated and validated, with > 0.8 and NSC > 0.7, respectively. The presence of grass waterways reduced annual total phosphorus loadings by 13 to 25%. The replicated study indicates that APEX provides a convenient and efficient tool to evaluate long-term benefits of conservation practices.

Drops for crops: modelling crop water productivity on a global scale

There is an emerging need to support water and food policy and decision making at the global and national levels. A systematic tool that is capable of analyzing water-food relationships with high spatial resolutions would be very useful. A GEPIC model has recently been developed by integrating a crop growth model with a Geographic Information System (GIS). The GEPIC model was applied to simulate crop yield and crop water productivity (CWP) for maize at a spatial resolution of 30 arc-minutes on a global scale. A comparison between simulated yields and FAO statistical yields in 124 countries shows a good agreement. The simulated CWP values are mainly in line with the measured values reported in literature. The crop yield and CWP were simulated with the assumption of sufficient water and fertilizer supply, holding other factors unchanged. The simulation results show that many countries have the potentials in achieving high maize yields and CWP. More than 80% of African countries have the potential to double their CWP. This reflects the current poor water and fertilizer management there. The results imply that efforts have to be strengthened to improve water and fertilizer management should the malnutrition be reduced or even eliminated.

Assessing crop yield and crop water productivity and optimizing irrigation scheduling of winter wheat and summer maize in the Haihe plain using SWAT model

AbstractHaihe plain is an important food production area in China, facing an increasing water shortage. The water used for agriculture accounts for about 70% of total water resources. Thus, it is critical to optimize the irrigation scheduling for saving water and increasing crop water productivity (CWP). This study first simulated crop yield and CWP for winter wheat and summer maize in historical scenario during 1961–2005 for Haihe plain using previously well‐established Soil and Water Assessment Tool model. Then, scenarios under historical irrigation (scenario 1) and sufficient irrigation (scenario 2) were, respectively, simulated both with sufficient fertilizer. The crop yield in scenario 2 was considered as the potential crop yield. The optimal irrigation scheduling with sufficient fertilizer (scenario 3) was explored by iteratively adjusting irrigation scheduling based on the scenario 1 and previous studies related to water stress on crop growth. Results showed that net irrigation amount was, respectively, reduced 23.1% and 18.8% in scenario 3 for winter wheat and summer maize when compared with scenario 1. The CWP was 12.1% and 8.2% higher with very slight change of crop yield. Using optimal irrigation scheduling could save 8.8 × 108 m3 irrigation water and reduce about 16.3% groundwater over‐exploitation in winter wheat growth period. The corresponding yield was 18.5% and 12.9% less than potential yield for winter wheat and summer maize but using less irrigation water. Therefore, it could be considered that the optimal irrigation was reasonable, which provided beneficial suggestions for increasing efficiency of agricultural water use with sustainable crop yield in Haihe plain. Copyright © 2013 John Wiley &amp; Sons, Ltd.

Sample Size Requirements for Assessing Statistical Moments of Simulated Crop Yield Distributions

Mechanistic crop growth models are becoming increasingly important in agricultural research and are extensively used in climate change impact assessments. In such studies, statistics of crop yields are usually evaluated without the explicit consideration of sample size requirements. The purpose of this paper was to identify minimum sample sizes for the estimation of average, standard deviation and skewness of maize and winterwheat yields based on simulations carried out under a range of climate and soil conditions. Our results indicate that 15 years of simulated crop yields are sufficient to estimate average crop yields with a relative error of less than 10% at 95% confidence. Regarding standard deviation and skewness, sample size requirements depend on the degree of symmetry of the underlying population’s distribution. For symmetric distributions, samples of 200 and 1500 yield observations are needed to estimate the crop yields’ standard deviation and skewness coefficient, respectively. Higher degrees of asymmetry increase the sample size requirements relative to the estimation of the standard deviation, while at the same time the sample size requirements relative to the skewness coefficient are decreased.

Maintaining environmental quality while expanding biomass production: Sub-regional U.S. policy simulations

This paper evaluates environmental policy effects on ligno-cellulosic biomass production and environmental outcomes using an integrated bioeconomic optimization model. The environmental policy integrated climate (EPIC) model is used to simulate crop yields and environmental indicators in current and future potential bioenergy cropping systems based on weather, topographic and soil data. The crop yield and environmental outcome parameters from EPIC are combined with biomass transport costs and economic parameters in a representative farmer profit-maximizing mathematical optimization model. The model is used to predict the impact of alternative policies on biomass production and environmental outcomes. We find that without environmental policy, rising biomass prices initially trigger production of annual crop residues, resulting in increased greenhouse gas emissions, soil erosion, and nutrient losses to surface and ground water. At higher biomass prices, perennial bioenergy crops replace annual crop residues as biomass sources, resulting in lower environmental impacts. Simulations of three environmental policies namely a carbon price, a no-till area subsidy, and a fertilizer tax reveal that only the carbon price policy systematically mitigates environmental impacts. The fertilizer tax is ineffectual and too costly to farmers. The no-till subsidy is effective only at low biomass prices and is too costly to government.

Evaluating the fidelity of downscaled climate data on simulated wheat and maize production in the southeastern US

Crop models are one of the most commonly used tools to assess the impact of climate variability and change on crop production. However, before the impact of projected climate changes on crop production can be addressed, a necessary first step is the assessment of the inherent uncertainty and limitations of the forcing data used in these crop models. In this paper, we evaluate the simulated crop production using separate crop models for maize (summer crop) and wheat (winter crop) over six different locations in the Southeastern United States forced with multiple sources of actual and simulated weather data. The paper compares the crop production simulated by a crop model for maize and wheat during a historical period, using daily weather data from three sources: station observations, dynamically downscaled global reanalysis, and dynamically downscaled historical climate model simulations from two global circulation models (GCMs). The same regional climate model is used to downscale the global reanalysis and both global circulation models’ historical simulation. The average simulated yield derived from bias-corrected downscaled reanalysis or bias-corrected downscaled GCMs were, in most cases, not statistically different from observations. Statistical differences of the average yields, generated from observed or downscaled GCM weather, were found in some locations under rainfed and irrigated scenarios, and more frequently in winter (wheat) than in summer (maize). The inter-annual variance of simulated crop yield using GCM downscaled data was frequently overestimated, especially in summer. An analysis of the bias-corrected climate data showed that despite the agreement between the modeled and the observed means of temperatures, solar radiation, and precipitation, their intra-seasonal variances were often significantly different from observations. Therefore, due to this high intra-seasonal variability, a cautious approach is required when using climate model data for historical yield analysis and future climate change impact assessments.

Validation of Noah-Simulated Soil Temperature in the North American Land Data Assimilation System Phase 2

AbstractSoil temperature can exhibit considerable memory from weather and climate signals and is among the most important initial conditions in numerical weather and climate models. Consequently, a more accurate long-term land surface soil temperature dataset is needed to improve weather and climate simulation and prediction, and is also important for the simulation of agricultural crop yield and ecological processes. The North American Land Data Assimilation phase 2 (NLDAS-2) has generated 31 years (1979–2009) of simulated hourly soil temperature data with a spatial resolution of ⅛°. This dataset has not been comprehensively evaluated to date. Thus, the purpose of this paper is to assess Noah-simulated soil temperature for different soil depths and time scales. The authors used long-term (1979–2001) observed monthly mean soil temperatures from 137 cooperative stations over the United States to evaluate simulated soil temperature for three soil layers (0–10, 10–40, and 40–100 cm) for annual and monthly time scales. Short-term (1997–99) observed soil temperatures from 72 Oklahoma Mesonet stations were used to validate simulated soil temperatures for three soil layers and for daily and hourly time scales. The results showed that the Noah land surface model generally matches observed soil temperature well for different soil layers and time scales. At greater depths, the simulation skill (anomaly correlation) decreased for all time scales. The monthly mean diurnal cycle difference between simulated and observed soil temperature revealed large midnight biases in the cold season that are due to small downward longwave radiation and issues related to model parameters.

Multiple Linear Correlation Analysis of Daily Reference Evapotranspiration

An accurate estimation of reference evapotranspiration (ET0) is of paramount importance for many studies such as hydrologic water balance, irrigation system design and management, crop yield simulation, and water resources planning and management. Simple regression techniques, may sometimes, provide adequate estimation of ET0. Implementation of regression methods considering all the predictor variables may, however, lead to overfit and consequent reduction in the predictive capability. The regression models for ET0 have been developed in the present study for Tirupati, Nellore, Rajahmundry, Anakapalli and Rajendranagar regions of Andhra Pradesh, India by following step-wise procedure, eliminating superfluous predictor variables based on statistical criteria. The sunshine hours, wind velocity, temperature and relative humidity influenced ET0 in the study area. The linear regression models developed in terms of predictor variables may conveniently be applied in the regions selected for the present study and, in the regions with similar climatic conditions for satisfactory ET0 estimation.

Use of agro-climatic zones to upscale simulated crop yield potential

Yield gap analysis, which evaluates magnitude and variability of difference between crop yield potential (Yp) or water limited yield potential (Yw) and actual farm yields, provides a measure of untapped food production capacity. Reliable location-specific estimates of yield gaps, either derived from research plots or simulation models, are available only for a limited number of locations and crops due to cost and time required for field studies or for obtaining data on long-term weather, crop rotations and management practices, and soil properties. Given these constraints, we compare global agro-climatic zonation schemes for suitability to up-scale location-specific estimates of Yp and Yw, which are the basis for estimating yield gaps at regional, national, and global scales. Six global climate zonation schemes were evaluated for climatic homogeneity within delineated climate zones (CZs) and coverage of crop area. An efficient CZ scheme should strike an effective balance between zone size and number of zones required to cover a large portion of harvested area of major food crops. Climate heterogeneity was very large in CZ schemes with less than 100 zones. Of the other four schemes, the Global Yield Gap Atlas Extrapolation Domain (GYGA-ED) approach, based on a matrix of three categorical variables (growing degree days, aridity index, temperature seasonality) to delineate CZs for harvested area of all major food crops, achieved reasonable balance between number of CZs to cover 80% of global crop area and climate homogeneity within zones. While CZ schemes derived from two climate-related categorical variables require a similar number of zones to cover 80% of crop area, within-zone heterogeneity is substantially greater than for the GYGA-ED for most weather variables that are sensitive drivers of crop production. Some CZ schemes are crop-specific, which limits utility for up-scaling location-specific evaluation of yield gaps in regions with crop rotations rather than single crop species.

Assessing the impacts of crop-rotation and tillage on crop yields and sediment yield using a modeling approach

This study was conducted in the Big Sunflower River Watershed (BSRW), north-west, Mississippi. The watershed has been identified as “impaired waters” under Section 303(d) of the Federal Clean Water Act due to high levels of sediment and total phosphorus. This excess is then transported to the Gulf of Mexico via the Yazoo River, further damaging the nation's water resources. The specific objectives of this study were to assess the impact of corn (Zea mays L.), soybean (Glycine max (L.) Merr., and rice (Oryza sativa, L.) crop-rotations (corn after soybean, soybean after rice, continuous soybean) and tillage practices (conventional, conservation, no-till) on crop yields and sediment yield using the Soil and Water Assessment Tool (SWAT) model.The SWAT model was calibrated from January 2001 to December 2005 and validated from January 2006 to September 2010 for monthly stream flow with good to very good performance [coefficient of determination (R2) values from 0.68 to 0.83 and Nash Sutcliffe Efficiency index (NSE) values from 0.51 to 0.63] using stream flow data from three spatially distributed USGS gage stations within the BSRW. The SWAT model was further calibrated for corn and soybean yields from research fields at Stoneville and validated using research fields at the Clarksdale experiment stations with fair to excellent statistics (R2 values from 0.43 to 0.59 and NSE values from 0.34 to 0.96). The SWAT model simulation results suggested that corn yields were greater in the corn after soybean rotation under conventional tillage (mean=9.88Mgha−1) than no-tillage (mean=8.81Mgha−1) practices. However, tillage practices had no effects on soybean yield for the corn after soybean rotation. Soybean yields under conventional tillage practice indicated greater yields (mean=3.01Mgha−1) for the soybean after rice rotation than for soybean after corn. Continuous soybean under conventional tillage had the lowest simulated crop yield (mean=2.07Mg ha−1) and the greatest sediment yield (5.2Mg ha−1) in this study. The cumulative (1981–2009) sediment yield at the end of the simulation period (2009) indicated a maximum difference of about 8Mgha−1 between no-till and conventional tillage practices, with no-till contributing the lowest sediment yield. The cumulative difference of the sediment yield between no-till and conservation till was about 2Mgha−1. The results of this study will help to better understand the impact of management practices on watershed crop management and water quality improvement within the BSRW. This information can be applied to other agricultural watersheds.

Waterlogging in Australian agricultural landscapes: a review of plant responses and crop models

This review summarises reported observations of the effects of waterlogging on agricultural production in Australia and briefly discusses potential remediation strategies. Inconsistencies are demonstrated in the current indicators used for assessment of waterlogging potential across agricultural landscapes as well as in parameters measured in waterlogging studies. It is suggested that predictions of waterlogging potential for landscapes should be based on a minimum dataset that includes pedological, topographical, and climate data for the defined area, as well as observations of plant morphological appearance and visible surface water. The review also summarises the effects of low oxygen concentration in soil on rhizosphere processes, and discusses evidence for direct effects on plant physiology of reductions in soil oxygen caused by waterlogging. Finally, the review describes current crop growth, water use, and yield simulation models used in Australia (SWAGMAN, DRAINMOD, and APSIM) that incorporate waterlogging stress. It is suggested that there is scope for modifications to these models based on recent improved understanding of plant physiological responses to waterlogging and on further research. The review concludes that improvements in modelling waterlogging outcomes to assist growth and yield predictions should ultimately enhance management capacity for growers.