Agrotechnology Transfer Cropping System Model Research Articles

Effect of mineral N fertilizer and organic input on maize yield and soil water content for assessing optimal N and irrigation rates in Central Kenya

Maize ( Zea mays L.) is an important food crop in Kenya, while low and erratic rainfall, and low nutrient input mainly result in low maize yield. This study were to assess optimal nutrient and irrigation management practice for maize in central Kenya based on field experiment combined with modeling simulation. On-farm experiment with four treatments including no fertilizer (N0), N applied at dose of 100 kg ha –1 only in the form of a chemical fertilizer (N100) or combined with animal manure (N100M) or straw (N100S) has been conducting since 2013 in central Kenya. The Decision Support System for Agro-technology Transfer–Cropping System Model (DSSAT–CSM) was firstly calibrated under the relative optimal treatment N100M, and it was then evaluated for the rest three treatments for 6 maize growing seasons from 2014 to 2018. The responses of grain yield to different irrigation and fertilizer regimes were simulated using the calibrated DSSAT–CSM. The combination of fertilizer and manure (N100M) resulted in the highest yield and that of fertilizer and straw (N100S), in the highest level of soil water content in each soil layer. The model (DSSAT-CSM) successfully predicted both grain yield (normalized root mean square error, or nRMSE , of 21–37% and the index of agreement, or d , of 0.89–0.93) and changes in water content of each soil layer ( nRMSE < 20% and d > 0.70) in all treatments except N100S. The yield was most sensitive to any deficit in soil water content (dry spells) at the beginning of grain-filling stage, and the best regime for high yield, high water-use efficiency, and high agronomic efficiency comprised irrigation at 50–70 mm during that stage combined with fertilizer N at 100–120 kg ha –1 . The estimated magnitude yield gain with respect to optimal nutrition and irrigation ranged from 2 to 4 t ha –1 in different crop seasons. Optimal application of irrigation at the sensitive stage, fertilizer N, animal manure, and straw mulching holds great potential as an integrated farming practice for high grain yield and for efficient use of resources in maize cultivation in semi-arid parts of Kenya. • Optimal fertilizer practice and irrigation strategy was assessed for maize in Kenya. • The combination of mineral fertilizer, manure, and straw mulching is recommended. • DSSAT can be used to predict maize yield and soil water content in central Kenya. • Irrigation at 50 mm in early grain filling together with N at 100 kg ha –1 is optimal.

Using crop simulation model to evaluate influence of water management practices and multiple cropping systems on crop yields: A case study for Ethiopian highlands

Abstract Low soil fertility, soil water stress and deteriorating natural resources are among the causal factors to low crop productivity in Africa. In this study, impact of cropping systems and water management practices on yield of different crops were simulated using the Decision Support System for Agrotechnology Transfer-Cropping System Model. The treatments included continuous cropping (monoculture) of three different crops [wheat, WT; barley, BA; chickpea, CP]; and rotation systems (WT-BA, and WT-BA-CP). Crops were treated with three different water management practices (a) rainfed; (b) tied ridging; and (c) irrigation. Five years (2013−2017) average actual yields were obtained and average short-term (2013−2017) and long-term (1984−2019) yield gaps, relative yields and yield changes were calculated. The yield gap (Yg) [the difference between actual (Ya) and simulated rainfed yield (Yw)] for wheat, barley and chickpea were 0.0 to 0.6, 0.1 to 1.7 and 0.2 to 2.1 Mg/ha, respectively. The large Yg value for chickpea was mainly attributed to the water stress. Whereas barley and wheat yield were less affected by the water stress when compared to chickpea. The rotation WT-BA-CP was more responsive to water management than WT-BA. Tied ridging or irrigations can be considered as an option for improving yield in water scarce areas. Chickpea fixed substantial amount of nitrogen (90–246 kg N/ha) in the soil, which could result in yield benefits and reduced use of inorganic nitrogen. Overall, the rotation WT-BA-CP may be a better option than WT-BA or continuous cropping as it can provide multiple benefits via enhanced crop diversity, improved soil fertility and more resilience to pest and diseases. This simulation case study and discussion of results showed the importance of integration of crop rotations with soil and water conservation practices for improving overall yields and contributions to ecological intensification.

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.

Evaluation of hydroclimatic variables for maize yield estimation using crop model and remotely sensed data assimilation

We used the Decision Support System for Agro-technology Transfer-Cropping System Model (DSSAT) and data assimilation scheme (DSSAT-DA) to estimate maize (i.e., corn) yield and to evaluate the sensitivity of maize yield to hydroclimatic variables (i.e., precipitation, air temperatures, solar radiation, soil water). The remotely sensed soil moisture products, which includes Advanced Microwave Scanning Radiometer and the Soil Moisture and Ocean Salinity, were assimilated to DSSAT model by using the Ensemble Kalman Filtering approach. It was observed that both DSSAT and DSSAT-DA models can able to capture the annual trend of maize yield, although they overestimate the observed maize yield. The DSSAT-DA scheme assimilated with remotely sensed products slightly improves the model performance. The antecedent hydroclimatic information can influence the subsequent maize yield. The maize yield is sensitive to the soil water availability and precipitation amount, especially at the antecedent 1 month time to sowing and the subsequent second and third month’s growing period.

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.

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...

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.

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