Decision Support System For Agrotechnology Transfer Cropping System Model Research Articles

Assessing the Effects of a Winter Wheat Cover Crop on Soil Water Use, Cotton Yield, and Soil Organic Carbon in No-Till Cotton Production Systems

HighlightsSoil water depleted by the cover crop was quickly replenished by spring precipitation.Higher soil water was maintained during the cotton growing season under the cover crop treatment.Cover crops improved soil organic carbon under both irrigated and dryland conditions.Cover crops enhanced crop water productivity under both irrigated and dryland conditions.Abstract. Cover crops provide many soil health benefits to agricultural systems. An interest in growing cover crops in cotton (Gossypium hirsutum L.) production systems has been increasing in the Texas Rolling Plains (TRP) region. Due to limited rainfall and groundwater availability, producers in this semi-arid region are concerned that winter cover crops can reduce soil water availability for a subsequent cotton crop. In addition, the long-term effects of cover crops on soil organic carbon (SOC) are not well studied in this region. The overall goal of this study was to assess the long-term effects of growing a winter wheat (Triticum aestivum) cover crop on soil water, SOC, seed cotton yield, and crop water productivity (CWP) under irrigated and dryland cotton production systems using the Decision Support System for Agrotechnology Transfer (DSSAT) model. Measured data from cover crop experiments conducted at the Texas A&M AgriLife Research Station at Chillicothe from 2011 to 2020 were used to evaluate the CROPGRO-Cotton and CERES-Wheat modules in the DSSAT Cropping System Model. The average percent error (PE) between the simulated and measured seed cotton yield was 0.3% and -0.9%, and that between the simulated and measured aboveground wheat biomass was 1.4% and -3.4% during the calibration and evaluation periods, respectively. For the simulation of SOC, the PE was 8.5% and 7.6% during the calibration and evaluation periods, respectively. Long-term (2001 to 2020) simulations showed that soil water was reduced substantially by the winter wheat cover crop before its termination. However, depleted soil water was quickly replenished by spring precipitation and maintained at a higher level during the cotton growing season. Winter wheat cover crops can potentially improve SOC in irrigated and dryland cotton production systems. The CWP of cotton also improved with a winter wheat cover crop under both irrigated and dryland conditions. We concluded that cover crops could potentially improve soil health and provide a sustainable environment for TRP cotton production. Keywords: CERES-Wheat, CROPGRO-Cotton, DSSAT, Soil health, Texas Rolling Plains (TRP).

Assessing the Effects of a Winter Wheat Cover Crop on Soil Water Use, Cotton Yield, and Soil Organic Carbon in No-Till Cotton Production Systems

HighlightsSoil water depleted by the cover crop was quickly replenished by spring precipitation.Higher soil water was maintained during the cotton growing season under the cover crop treatment.Cover crops improved soil organic carbon under both irrigated and dryland conditions.Cover crops enhanced crop water productivity under both irrigated and dryland conditions.Abstract. Cover crops provide many soil health benefits to agricultural systems. An interest in growing cover crops in cotton (Gossypium hirsutum L.) production systems has been increasing in the Texas Rolling Plains (TRP) region. Due to limited rainfall and groundwater availability, producers in this semi-arid region are concerned that winter cover crops can reduce soil water availability for a subsequent cotton crop. In addition, the long-term effects of cover crops on soil organic carbon (SOC) are not well studied in this region. The overall goal of this study was to assess the long-term effects of growing a winter wheat (Triticum aestivum) cover crop on soil water, SOC, seed cotton yield, and crop water productivity (CWP) under irrigated and dryland cotton production systems using the Decision Support System for Agrotechnology Transfer (DSSAT) model. Measured data from cover crop experiments conducted at the Texas A&M AgriLife Research Station at Chillicothe from 2011 to 2020 were used to evaluate the CROPGRO-Cotton and CERES-Wheat modules in the DSSAT Cropping System Model. The average percent error (PE) between the simulated and measured seed cotton yield was 0.3% and -0.9%, and that between the simulated and measured aboveground wheat biomass was 1.4% and -3.4% during the calibration and evaluation periods, respectively. For the simulation of SOC, the PE was 8.5% and 7.6% during the calibration and evaluation periods, respectively. Long-term (2001 to 2020) simulations showed that soil water was reduced substantially by the winter wheat cover crop before its termination. However, depleted soil water was quickly replenished by spring precipitation and maintained at a higher level during the cotton growing season. Winter wheat cover crops can potentially improve SOC in irrigated and dryland cotton production systems. The CWP of cotton also improved with a winter wheat cover crop under both irrigated and dryland conditions. We concluded that cover crops could potentially improve soil health and provide a sustainable environment for TRP cotton production. Keywords: CERES-Wheat, CROPGRO-Cotton, DSSAT, Soil health, Texas Rolling Plains (TRP).

Review on Decision Support System for Agrotechnology Transfer (DSSAT) Model

Traditional agronomic experiments were conducted at a specific location in time and space, resulting in long, seasonal, time-consuming, and expensive experiments. An international team of scientists has developed a decision support system for the transfer of agrotechnology, which has been used by researchers from around and the world for 15 years. This package incorporates models for over 42 crops (since Version 4.7.5) as well as tools to facilitate effective use of the models. Tools include database management programs for soil, weather, crop management, and experimental data, utilities, and implementation programs. Crop simulation models simulate growth, development, and yield in accordance with soil-plant-atmosphere dynamics. Over the last few years, it has become increasingly difficult to maintain the DSSAT crop models, partly due to the fact that there were different sets of computer code for different crops with little attention to software design at the level of crop models themselves. Thus, the DSSAT crop models have been re-designed and programmed to facilitate more efficient incorporation of new scientific advances, applications, documentation, and maintenance. The basis for the new DSSAT cropping system model (CSM) design is a modular structure in which components separate along scientific discipline lines and are structured to allow easy replacement or addition of modules. In this review paper, I described the approaches used to model the primary scientific components (soil, crop, weather, and management). Besides, the review paper describes the limitations, the future of the DSSAT model, and its importance. The benefits of the new, re-designed DSSAT–CSM will provide considerable opportunities for its development and others in the scientific community for greater cooperation in interdisciplinary research and in the application of knowledge to solve problems in the field, farm, and higher levels.

Evaluating optimal irrigation strategies for maize in Western Kansas

A calibrated and validated Decision Support System for Agro-technology Transfer – Cropping System Model (DSSAT-CSM), CERES-Maize, was used to (1) evaluate maize (Zea mays L.) yield and water productivities (WP) under various irrigation frequencies (IRF) triggered by different plant available soil water (PASW) on three soil types, and (2) assess the optimum IRF and PASW triggers for maximum yield and water productivity on three different soils (with a depth of 1.2 m) in Finney County, Western Kansas. The treatments were four IRF (2, 4, 7, and 9 days), four PASW (5%, 25%, 50% and 75%) irrigation trigger and three soil types with a total of 48 combinations. The model was set to automatically apply 25 mm irrigation at selected day intervals when certain irrigation triggering PASW conditions were met. This study showed that maintaining higher PASW up to 75% or above might result in too many irrigations per season, which could be costly. IRF-9 showed substantially lowest yield under most PASW tested in this study. The highest yield was simulated at irrigation amount of 400–450 mm and ET of 650–800 mm depending on soil types. The highest irrigation water productivity (IWP, 30–33 kg/ha/mm) was simulated when triggered by 25% and 50% PASW for Ulysses silt loams. Overall, IRF-4 under 50% PASW threshold provided the maximum maize yield and WP for all soil types.

Management options for mid-century maize (Zea mays L.) in Ethiopia

This simulation study was carried out to assess the impact of climate change and adaptation strategies on maize production across 22 locations in Ethiopia using Decision Support System for Agrotechnology Transfer – Cropping System Model (DSSAT-CSM) CERES-Maize. Three maize varieties, i.e., [BH-660 (late maturing), BH-540 (medium maturing) and Melkasa-1 (short maturing)] along with three planting dates [early (25-Apr), normal (25-May) and late (25-Jun)], four N fertilizer rates (64, 96, 128 and 160 kg N/ha) and three water levels [rainfed (no irrigation), two irrigations (each 30 mm at time of flowering and 5 d after flowering with total = 60 mm) and five irrigations (each 30 mm at time of flowering to early grain-filing, which were applied every five days in total = 150 mm)] were evaluated as the adaptation strategy. The mid-century (2040–2069) temperatures and solar radiation were extracted from multiple model means across the Coordinated Regional Climate Downscaling Experiment (CORDEX) models under the highest Representative Concentration Pathway (RCP8.5). Maize productivity was evaluated assuming that maize was grown on shallow sandy loam soils. Yield of an early, medium and late maturing maize were changed by −13 to −8%, −10 to +4% and + 3 to +13%, respectively, relative to the baseline period (1980–2005). The days to maturity decreased by about 16%. Under rainfed condition, N application up to 64, 128 and 160 kg/ha significantly improved yield for early, medium and late maturing varieties, respectively. Relatively high yield and low inter-seasonal yield variability were simulated for BH-660 and Melkasa-1 when planted on 25-Apr and 25-May, respectively, for most locations. Application of two (60 mm) and five (150 mm) irrigation levels improved yield in drier locations. In conclusion, this study provides potential adaptation options under the future climate in maize producing regions of Ethiopia.

Comparison of evapotranspiration methods in the DSSAT Cropping System Model: II. Algorithm performance

Accurate calculations of evapotranspiration (ET) are highly important for agroecosystem model simulations, and improvement of ET algorithms is an on-going model development goal. The objective of this study was to evaluate and compare six ET methods in the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model (CSM) using agronomic and weighing lysimetry data from cotton field studies at Bushland, Texas. Three options were tested for estimating potential ET as required by the DSSAT-CSM: 1) a Priestley-Taylor method, 2) a Penman-Monteith combination equation estimate of grass reference ET with a DSSAT-specific single crop coefficient equation, and 3) the ASCE Standardized Reference ET Equation combined with a dual crop coefficient method for non-stressed conditions. The latter two reference ET methods were adapted to provide reasonable estimates for DSSAT-required potential ET. Additionally, two methods for calculation of soil water evaporation were tested, including both the original and updated formulations of Ritchie approaches for DSSAT-CSM. The combinations of the three potential ET and two soil water evaporation approaches led to six possible ET simulation options in the model. A computationally-intensive multiobjective optimization method was used to select among model parameterization options and ensure that modeler bias did not influence ET method comparisons. Among 23 agroecosystem metrics that included lysimeter-based ET, various cotton growth variables, and soil water content in multiple soil layers, the original Ritchie soil water evaporation approach performed statistically equivalent to or better than the more recent Ritchie method (p⩽0.05). The default ET method in the model, which involved Priestley-Taylor potential ET with the more recent Ritchie soil water evaporation method, was outperformed by other ET methods for 14 of 23 agroecosystem metrics (p⩽0.05). When the original Ritchie soil water evaporation method was combined with potential ET from the ASCE reference ET and dual crop coefficient method, the model performed statistically equivalent to or better than the other five ET options for all but 1 of 23 agroecosystem metrics (p⩽0.05). Based on three years of cotton data from the Bushland lysimetry fields, a DSSAT-CSM ET approach based on the standardized ET methodologies described by ASCE and FAO-56 combined with the original Ritchie soil water evaporation method provided holistic improvements to model simulations among multiple agroecosystem metrics.

Comparison of evapotranspiration methods in the DSSAT Cropping System Model: I. Global sensitivity analysis

Global sensitivity analysis (GSA) is useful for evaluating the responsiveness of agroecosystem models to input parameter adjustments. Evaluations of model sensitivity for diverse water status conditions and evapotranspiration (ET) algorithms will facilitate better use of models to provide water management recommendations. The objective of this study was to conduct a GSA to identify influential parameters in the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model (CSM), specifically using the CROPGRO-Cotton module with data from cotton field studies conducted in 2000, 2001, and 2008 at Bushland, Texas. The field studies tested fully-irrigated, deficit-irrigated, and dryland cotton production in a semi-arid environment. Using high performance computing resources, a GSA was conducted to evaluate the sensitivity of 24 model outputs with respect to 37 model input parameters. The GSA was conducted for six different ET methodologies available in the model. With first-order sensitivity indices <0.05, nearly half of the tested input parameters did not influence any model output for any of the tested simulation scenarios. Among the parameters with first-order sensitivity indices >0.05, eleven were cultivar parameters that controlled crop development and growth, and five were soil parameters that specified initial soil water conditions, soil water limits, drainage rate, and root growth characteristics. The influences of another soil parameter and one ET parameter were relevant only for the ET methods that required them. Large differences in sensitivity indices were found based on the choice between two soil water evaporation methods. In addition to providing insights for other applications of this model, the results specifically informed further efforts to evaluate the model using measured data from the Bushland cotton studies to compare performance among the six ET methods, as reported in a companion paper.

Simulating and Predicting Crop Yield and Soil Fertility under Climate Change with Fertilizer Management in Northeast China Based on the Decision Support System for Agrotechnology Transfer Model

The risks of climate change and soil degradation for the agricultural environment and crop production are increasingly prominent. Based on the limitations of land resources, it is important to explore a sustainable and effective fertilization strategy to reduce risks and ensure there is a high yield of grain and sustainable development of agriculture. Soil fertility underpins cultivated land, which is the most important resource of agricultural production, and is also the key for maintaining agricultural sustainability. The central elements of soil fertility are soil organic carbon (SOC) and soil nitrogen (SN). This study applied the Decision Support System for Agrotechnology Transfer-Cropping System Model (DSSAT-CSM) and the CENTURY-based soil module to simulate the trends of crop yields, SN storages and SOC storages until the end of this century under different climate change circumstances, based on a 36-year long-term experiment established at Shenyang site, China. Four fertilizer practices were applied: control (CK), combined chemical fertilizer of nitrogen, phosphorus, and potassium (NPK), NPK with manure (MNPK), and NPK fertilizers plus a high application rate of manure (hMNPK). The outcomes indicated that the DSSAT model can fully simulate the yields of maize and soybean as well as the dynamic stocks of the SN and SOC. Three Representative Concentration Pathways (RCP 2.6, RCP 4.5, RCP 8.5) for future development were chosen from the fifth assessment report of the United Nations Intergovernmental Panel on Climate Change (IPCC). Moreover, a baseline was installed. Crop yields, SN, and SOC storages from 2016 to 2100 were estimated under four climate scenarios (RCP 2.6, RCP 4.5, RCP 8.5, and Baseline). The RCP scenarios in some treatments reduced SN and SOC stocks and maize yield, and had no effect on soybean yield. However, the application of NPK with manure could improve crop yields, while it increased SN and SOC storages substantially. To some extent, the negative effects of climate scenarios could be mitigated by applying manure. In the RCP 4.5, maize yields of NPK, MNPK, and hMNPK treatments declined by 14.8%, 7.7%, and 6.2%, respectively, compared with that of NPK under Baseline. The NPK fertilizers plus manure treatments could cut the reduction of maize yield caused by climate change in half. Additionally, the SOC storage and SN of chemical fertilizers plus manure treatments under RCP scenarios increased by 20.2%–33.5% and 13.7%–21.7% compared with that of NPK under baseline, respectively. It was concluded that a rational combination of organic and inorganic fertilizer applications is a sustainable and effective agricultural measure to maintain food security and relieve environmental stresses.

Economic value and water productivity of major irrigated crops in the Ogallala aquifer region

Crop water productivity and return after variable cost for three major crops (winter wheat, grain sorghum and maize) in the southwest Kansas were evaluated based on simulated yield using the Decision Support System for Agrotechnology Transfer - Cropping System Model (DSSAT-CSM). Winter wheat and grain sorghum were treated with four irrigation capacities (ICs) (zero, 1.7, 2.5 and 5 mm/day) and an automatic irrigation (irrigation when required). Maize was treated with three ICs (0, 2.5 and 5 mm/day) and an automatic irrigation. Long-term simulations showed that average winter wheat, grain sorghum and maize yields can be stabilized under 1.7–2.5, 2.5 and 5 mm/day ICs with corresponding irrigation of 100–150, 100–250 and 450–500 mm, respectively. The crop water productivities of winter wheat, grain sorghum and maize ranged from 9.8 to 11.8, 12.8–15.0 and 4.2–19.1 kg/ha/mm, respectively. Maize was found to be profitable when grown under both deficit and full irrigation conditions although optimal irrigation application for maize substantially increased the net return after variable cost (RAVC). The median net return after variable cost for rainfed wheat ($252/ha) and grain sorghum ($180/ha) was greater than that for irrigated wheat ($169–$192/ha) and grain sorghum ($104–$110/ha). The RAVC for wheat and grain sorghum did not rise with increase in irrigation and IC, whereas the RAVC for maize increased with irrigation and IC. The lowest and highest RAVC for maize corresponded to IC of 0 ($16/ha) and 5 mm/day ($530/ha). However, this income from growing maize under higher IC may not last long due to rapidly depleting groundwater levels in the underlying Ogallala aquifer. Therefore, the short-term benefits of growing maize under higher ICs need to be compared with the long-term environmental impact, food security and employment opportunities including sustaining the growing municipal and industrial water needs.

Grain sorghum production functions under different irrigation capacities

Water is the major factor limiting crop production in semi-arid regions of the southern and central US High Plains. The Ogallala Aquifer is the major source of irrigation water in the region. The water levels in the aquifer have been declining due to water withdrawals for irrigation exceeding mean annual recharge. As a result, some of the wells in the region are not able to meet the crop water requirements due to lower irrigation capacity (IC). Grain sorghum, one of the major crops grown in western Kansas is considered drought tolerant. There is limited literature on the effect of IC on grain sorghum yield under erratic rainfall patterns of the semi-arid High Plains. Study objectives were to: (1) evaluate the Decision Support System for Agrotechnology Transfer – Cropping System Model (DSSAT-CSM) for assessing grain sorghum yield and water productivity; (2) develop grain sorghum water production functions under five different IC, two cultivars and three irrigation scheduling strategies; and (3) evaluate response of grain sorghum under various IC in western Kansas based on long term historical weather; 4) to determine the minimum irrigation capacity required for irrigating grain sorghum in western Kansas. A calibrated and validated late maturing cultivar in prior study was used, while measured yield and phenology data of an early maturing cultivar was used to evaluate an existing cultivar in DSSAT model. Results showed that the cultivar in DSSAT were representative of the cultivars in the field with Normalized Root Mean Square of Error (NRMSE) 18%, index of agreement (I) of 0.97 and percent of deviation (d) −19 to 29%. The corresponding statistical goodness of fit values for days to flowering was 12%, 0.99 and 0–17%, respectively. Overall, the simulated yield and phenology values agreed with the measured indicating the satisfactory simulation performance of the model. Highest yield, crop water productivity and irrigation water use efficiencies were obtained from IC of 2.5–3.6 mm/day under the irrigation scheduling strategies from panicle initiation to grain filling. However, long term simulation showed that the minimum IC for growing grain sorghum varied depending on the minimum yield goal to be met based on long term perspective. In some wet years, lower IC up to 1.7 mm/day or even in some of the wettest years no irrigation was suitable. Our findings indicate that it might not be necessary to irrigate grain sorghum from planting to maturity but if producers are forced to do so by climatic conditions, an IC of 1.7–2.5 mm/day was found to produce a yield of at least 5 t/ha similar to that of 5 mm/day IC in 75% of the long-term growing seasons. If producers have to irrigate grain sorghum from panicle initiation to maturity, the 2.5 mm/day IC was found to be adequate to meet a minimum yield goal of 5 t/ha in 3 out of 4 years based on soils and long-term average climate around western Kansas. IC of 3.6 mm/day was found to be adequate if producers wish to irrigate only from panicle initiation to grain filling to meet a minimum yield goal of 6 t/ha in 75–90% of the long-term seasons.

Impacts of climate and farming management on maize yield in southernTanzania

Climate is one of the major factors controlling agricultural productivity in Africa. Changes in meteorological variables such as rising temperatures, changes in precipitation and increase in atmospheric carbon dioxide levels affect crop production. The objective of this study was to evaluate the impacts of climate change and variability, and crop management on yield of maize (Zea mays L.) grown in the southern part of Tanzania. Using the Decision Support System for Agrotechnology Transfer Cropping System Model (DSSAT-CSM), a series of sensitivity experiments were conducted to evaluate the response of maize yields to a range of principal changes in rainfall and temperatures. The sensitivities were estimated under two management practices, one with traditional farming practices, and the other with application of external farm inputs. Dry-spells during the growing season caused yield losses of all cultivars of up to 43% for the prolonged dry-spells of 20 days. Increased rainfall intensity, during vegetative and reproductive stages, caused the decrease in yield of 5 and 2%, respectively. A 50-100% decrease in rainfall intensity during the growing season caused a loss of yields between 40-100%. Increased or decreased temperatures from the baseline values reduced or increased days to flowering and to physiological maturity, respectively. In addition, a decrease in temperature from the baseline values to 2 o C had an overall impact of yields loss for all cultivars. However, yields increased with an increase of temperature by up to 2.5 °C (UH6303 and H628) and 4.5 °C (PAN691). Growing seasons with lower total rainfall (&lt;50 mm) and temperature (&lt;1°C) from their climatological values, caused yield loss as much as 71 and 15%, respectively for PAN691 cultivar. Generally, the impacts depended on the management, cultivar, soil characteristics, magnitude, timing and duration of the stress.Keywords: DSSAT, rainfall, temperature, Zea mays

Modeling Regional Crop Yield and Irrigation Demand Using SMAP Type of Soil Moisture Data

Abstract Agricultural models, such as the Decision Support System for Agrotechnology Transfer cropping system model (DSSAT-CSM), have been developed for predicting crop yield at field and regional scales and to provide useful information for water resources management. A potentially valuable input to agricultural models is soil moisture. Presently, no observations of soil moisture exist covering the entire United States at adequate time (daily) and space (~10 km or less) resolutions desired for crop yield assessments. Data products from NASA’s upcoming Soil Moisture Active Passive (SMAP) mission will fill the gap. The objective of this study is to demonstrate the usefulness of the SMAP soil moisture data in modeling and forecasting crop yields and irrigation amount. A simple, efficient data assimilation algorithm is presented in which the agricultural crop model DSSAT-CSM is constrained to produce modeled crop yield and irrigation amounts that are consistent with SMAP-type data. Numerical experiments demonstrate that incorporating the SMAP data into the agricultural model provides an added benefit of reducing the uncertainty of modeled crop yields when the weather input data to the crop model are subject to large uncertainty.

Simulation of long-term spring wheat yields, soil organic C, N and water dynamics using DSSAT-CSM in a semi-arid region of the Canadian prairies

The overall performance of the Decision Support System for Agrotechnology Transfer-Cropping System Model (DSSAT-CSM) was evaluated for simulating wheat (Triticum aestivum L.) yield, grain N uptake, soil organic C (SOC) and N (SON), soil water and nitrate–N (NO3–N) dynamics. The data used was from a long-term (1967–2005) spring wheat experiment conducted at Swift Current, Saskatchewan in the semi-arid Canadian prairies. Four treatments were selected: (1) continuous wheat receiving N and P fertilizer, Cont-W(NP); (2) continuous wheat receiving P only, Cont-W(P); and each phase of a fallow wheat rotation receiving N and P fertilizer, (3) W–F(NP) and (4) F–W(NP). The simulated grain yields matched the measurements well, with high d (0.74–0.83) and EF (0.16–0.33). The grain N uptake was also simulated satisfactorily with RMSE of 14–17 kg N ha−1 and d of 0.66–0.81. DSSAT simulated topsoil (0–0.15 m) SOC and SON well in the drier period (1967–1991), whereas it underestimated SOC in the more humid period (1991–2003). The DSSAT successfully simulated soil water and NO3–N dynamics in 0–0.15 m depth, whereas it overestimated soil water and NO3–N in the deep layers and consequently underestimated NO3–N leaching, suggesting that further improvements in soil water module should be made for the semi-arid climatic conditions in Canadian prairies. Sensitivity results showed that soil water content was sensitive to both lower soil water and upper drainage limits in this study. The performances of DSSAT model to yield and soil dynamics were comparable with other models.

Evaluation of the DSSAT-CSM for simulating yield and soil organic C and N of a long-term maize and wheat rotation experiment in the Loess Plateau of Northwestern China

The aim of this study was to evaluate the potential of the Decision Support System for Agrotechnology Transfer – Cropping System Model (DSSAT-CSM) using the CENTURY-based soil module to simulate long-term trends of grain yield, soil organic C (SOC) and soil organic N (SON) based upon 14-year data from a spring maize (Zea mays L.) and winter wheat (Triticum aestivum L.) cropping system study conducted in the Loess Plateau of Northwestern China. There were four treatments including no fertilizer (N0), 90 kg N ha−1 from urea (N90), 30 kg N ha−1 from straw plus 90 kg N ha−1 from urea (SN90), and 40 kg N ha−1 from cattle manure plus 90 kg N ha−1 from urea (MN90) selected in this study. The DSSAT-CSM showed a good to excellent agreement for simulating maize yields with normalized root mean square error (nRMSE) ≤ 19%, index of agreement (d) > 0.91 and modeling efficiency (EF) ≥ 0.56, and a moderate to good agreement for wheat yields with nRMSE ≤ 22%, d > 0.89 and EF > 0.46 for N90, SN90 and MN90 treatments. The model simulated SOC in the 0–20 cm depth for both SN90 and MN90 very well with nRMSE < 13% and d > 0.63 and moderately for N90 and N0. The simulated topsoil SON matched the measured data for N90, SN90 and MN90 very well with nRMSE < 7%, d > 0.77 and EF > 0.15, whereas the simulation for N0 was poor. Both maize wheat yields were found to be more sensitive to the fertilizer N rates in humid than drought soil conditions. The sensitivity of grain yields for either maize or wheat to generated growing season precipitation was affected by fertilizer N rate. The simulated soil nitrate-N (NO3-N) in soil profile and the NO3-N leaching below 150 cm increased with the increased fertilizer N rates as expected. The periods occurring high NO3-N leaching were along with drainage events mainly in the next fallow periods. Therefore, this study found that the DSSAT-CSM has a large potential to assess the impacts of various agricultural practices on crop growth, soil C and N dynamics in the semi-arid to semi-humid region of the Loess Plateau, and could contribute to selecting the optimum management practices.

Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction

To improve the prediction of crop yields at an aggregate scale, we developed a data assimilation-crop modeling framework that incorporates remotely sensed soil moisture and leaf area index (LAI) into a crop model using sequential data assimilation. The core of the framework is an Ensemble Kalman Filter (EnKF) used to control crop model runs, assimilate remote sensing (RS) data and update model state variables. We modified the Decision Support System for Agro-technology Transfer – Cropping System Model (DSSAT-CSM)-Maize model (Jones et al., 2003) to be able to stop and start simulations at any given time in the growing season, such that the EnKF can update model state variables as RS data become available. The data assimilation-crop modeling framework was evaluated against 2003–2009 maize yields in Story County, Iowa, USA, assimilating AMSR-E soil moisture and MODIS-LAI data independently and simultaneously. Assimilating LAI or soil moisture independently slightly improved the correlation of observed and simulated yields (R=0.51 and 0.50) compared to no data assimilation (open-loop; R=0.47) but prediction errors improved with reductions in MBE and RMSE by 0.5 and 0.5Mgha−1 respectively for LAI assimilation while these were reduced by 1.8 and 1.1Mgha−1 for soil moisture assimilation. Yield correlation improved more when both soil moisture and LAI were assimilated (R=0.65) suggesting a cause–effect interaction between soil moisture and LAI, prediction errors (MBE and RMSE) were also reduced by 1.7 and 1.8Mgha−1 with respect to open-loop simulations. Results suggest that assimilation of LAI independently might be preferable when conditions are extremely wet while assimilation of soil moisture+LAI might be more suitable when conditions are more nominal. AMSR-E soil moisture tends to be more biased under the presence of high vegetation (i.e., when crops are fully developed) and that updating rootzone soil moisture by near-surface soil moisture assimilation under very wet conditions could increase the modeled percolation causing excessive nitrogen (N) leaching hence reducing crop yields even with water stress reduced at a minimum due to soil moisture assimilation. However, applying the data assimilation-crop modeling framework strategically by considering a-priori information on climate condition expected during the growing season may improve yield prediction performance substantially, in our case with higher correlation (R=0.80) and more reductions in MBE and RMSE (2.5 and 3.3Mgha−1) compared to when there is no data assimilation. Scaling AMSR-E soil moisture to the climatology of the model did not improve our data assimilation results because the model is also biased. Better soil moisture products e.g., from Soil Moisture Active Passive (SMAP) mission, may solve the soil moisture data issue in the near future.

Short Communication: Climate change and biofuel wheat: A case study of southern Saskatchewan

Wang, H., He, Y., Qian, B., McConkey, B., Cutforth, H., McCaig, T., McLeod, G., Zentner, R., DePauw, R., Lemke, R., Brandt, K., Liu, T., Qin, X., White, J., Hunt, T. and Hoogenboom, G. 2012. Short Communication: Climate change and biofuel wheat: A case study of southern Saskatchewan. Can. J. Plant Sci. 92: 421–425. This study assessed potential impacts of climate change on wheat production as a biofuel crop in southern Saskatchewan, Canada. The Decision Support System for Agrotechnology Transfer-Cropping System Model (DSSAT-CSM) was used to simulate biomass and grain yield under three climate change scenarios (CGCM3 with the forcing scenarios of IPCC SRES A1B, A2 and B1) in the 2050s. Synthetic 300-yr weather data were generated by the AAFC stochastic weather generator for the baseline period (1961–1990) and each scenario. Compared with the baseline, precipitation is projected to increase in every month under all three scenarios except in July and August and in June for A2, when it is projected to decrease. Annual mean air temperature is projected to increase by 3.2, 3.6 and 2.7°C for A1B, A2 and B1, respectively. The model predicted increases in biomass by 28, 12 and 16% without the direct effect of CO2 and 74, 55 and 41% with combined effects (climate and CO2) for A1B, A2 and B1, respectively. Similar increases were found for grain yield. However, the occurrence of heat shock (&gt;32°C) will increase during grain filling under the projected climate conditions and could cause severe yield reduction, which was not simulated by DSSAT-CSM. This implies that the future yield under climate scenarios might have been overestimated by DSSAT-CSM; therefore, model modification is required. Several measures, such as early seeding, must be taken to avoid heat damages and take the advantage of projected increases in temperature and precipitation in the early season.

SHORT COMMUNICATION: Climate change and biofuel wheat: A case study of southern Saskatchewan

Wang, H., He, Y., Qian, B., McConkey, B., Cutforth, H., McCaig, T., McLeod, G., Zentner, R., DePauw, R., Lemke, R., Brandt, K., Liu, T., Qin, X., White, J., Hunt, T. and Hoogenboom, G. 2012. Short Communication: Climate change and biofuel wheat: A case study of southern Saskatchewan. Can. J. Plant Sci. 92: 421–425. This study assessed potential impacts of climate change on wheat production as a biofuel crop in southern Saskatchewan, Canada. The Decision Support System for Agrotechnology Transfer-Cropping System Model (DSSAT-CSM) was used to simulate biomass and grain yield under three climate change scenarios (CGCM3 with the forcing scenarios of IPCC SRES A1B, A2 and B1) in the 2050s. Synthetic 300-yr weather data were generated by the AAFC stochastic weather generator for the baseline period (1961–1990) and each scenario. Compared with the baseline, precipitation is projected to increase in every month under all three scenarios except in July and August and in June for A2, when it is projected to decreas...

Using the DSSAT-CERES-Maize model to simulate crop yield and nitrogen cycling in fields under long-term continuous maize production

Simulation models, such as the DSSAT (Decision Support System for Agrotechnology Transfer) Crop System Models are often used to characterize, develop and assess field crop production practices. In this study, one of the DSSAT Cropping System Model, CERES-Maize, was employed to characterize maize (Zea mays) yield and nitrogen dynamics in a 50-year maize production study at Woodslee, Ontario, Canada (42°13′N, 82°44′W). The treatments selected for this study included continuous corn/maize with fertilization (CC-F) and continuous corn/maize without fertilization (CC-NF) treatments. Sequential model simulations of long-term maize yield (1959–2008), near-surface (0–30 cm) soil mineral nitrogen (N) content (2000), and soil nitrate loss (1998–2000) were compared to measured values. The model did not provide accurate predictions of annual maize yields, but the overall agreement was as good as other researchers have obtained. In the CC-F treatment, near-surface soil mineral N and cumulative soil nitrate loss were simulated by the model reasonably well, with n-RMSE = 62 and 29%, respectively. In the CC-NF treatment, however, the model consistently overestimated soil nitrate loss. These outcomes can be used to improve our understanding of the long-term effects of fertilizer management practices on maize yield and soil properties in improved and degraded soils.

Improving SHAW long-term soil moisture prediction for continuous wheat rotations, Alberta, Canada

The Decision Support System for Agrotechnology Transfer-Cropping System Model (DSSAT-CSM) is a widely used modeling package that often simulates wheat yield and biomass well. However, some previous studies reported that its simulation on soil moisture was not always satisfactory. On the other hand, the Simultaneous Heat and Water (SHAW) model, a more sophisticated, hourly time step soil microclimate model, needs inputs of plant canopy development over time, which are difficult to measure in the field especially for a long-term period (longer than a year). The SHAW model also needs information on surface residue, but treats them as constants. In reality, however, surface residue changes continuously under the effect of tillage, rotation and environment. We therefore proposed to use DSSAT-CSM to simulate dynamics of plant growth and soil surface residue for input into SHAW, so as to predict soil water dynamics. This approach was tested using three conventionally tilled wheat rotations (continuous wheat, wheat-fallow and wheat-wheat-fallow) of a long-term cropping systems study located on a Thin Black Chernozemic clay loam near Three Hills, Alberta, Canada. Results showed that DSSAT-CSM often overestimated the drying of the surface layers in wheat rotations, but consistently overestimated soil moisture in the deep soil. This is likely due to the underestimation of root water extraction despite model predictions that the root system reached 80 cm. Among the eight growth/residue parameters simulated by DSSAT-CSM, root depth, leaf area index and residue thickness are the most influential characteristics on the simulation of soil moisture by SHAW. The SHAW model using DSSAT-CSM-simulated information significantly improved prediction of soil moisture at different depths and total soil water at 0-120 cm in all rotations with different phases compared with that simulated by DSSAT-CSM. Key words: Soil moisture, modeling, Decision Support System for Agrotechnology Transfer-Cropping System Model, Simultaneous Heat and Water Model

The DSSAT cropping system model

The decision support system for agrotechnology transfer (DSSAT) has been in use for the last 15 years by researchers worldwide. This package incorporates models of 16 different crops with software that facilitates the evaluation and application of the crop models for different purposes. Over the last few years, it has become increasingly difficult to maintain the DSSAT crop models, partly due to fact that there were different sets of computer code for different crops with little attention to software design at the level of crop models themselves. Thus, the DSSAT crop models have been re-designed and programmed to facilitate more efficient incorporation of new scientific advances, applications, documentation and maintenance. The basis for the new DSSAT cropping system model (CSM) design is a modular structure in which components separate along scientific discipline lines and are structured to allow easy replacement or addition of modules. It has one Soil module, a Crop Template module which can simulate different crops by defining species input files, an interface to add individual crop models if they have the same design and interface, a Weather module, and a module for dealing with competition for light and water among the soil, plants, and atmosphere. It is also designed for incorporation into various application packages, ranging from those that help researchers adapt and test the CSM to those that operate the DSSAT–CSM to simulate production over time and space for different purposes. In this paper, we describe this new DSSAT–CSM design as well as approaches used to model the primary scientific components (soil, crop, weather, and management). In addition, the paper describes data requirements and methods used for model evaluation. We provide an overview of the hundreds of published studies in which the DSSAT crop models have been used for various applications. The benefits of the new, re-designed DSSAT–CSM will provide considerable opportunities to its developers and others in the scientific community for greater cooperation in interdisciplinary research and in the application of knowledge to solve problems at field, farm, and higher levels.