Improving SHAW long-term soil moisture prediction for continuous wheat rotations, Alberta, Canada (original) (raw)
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Nutrient Cycling in Agroecosystems
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 mor...
Field Crops Research, 2019
Wheat production in drylands is determined greatly by the available water at the critical growth stages. In dry years, farmers usually face the dilemma of whether to harvest at an early stage for hay or silage, with reduced profit, or leave the crop for grain production with the risk of a major economic loss. Thus, an early prediction of potential wheat grain yield production is essential for agricultural decision making, particularly in water-limited areas. Here, we test whether using a proximal-based biophysical model of actual evapotranspiration (water use) and root-zone soil water content (SWC)-Crop RS-Met-may assist in providing early grain yield predictions in dryland wheat fields. Crop RS-Met was examined in eight experimental fields comprising a variety of spring wheat (Triticum aestivum L.) cultivars exposed to different treatments and amounts of water supply (185 mm-450 mm). Crop RS-Met was first validated against SWC measurements at the root-zone profile. Then, modeled SWC at heading (SWC Heading) was regressed against end-of-season grain yields (GY EOS), which ranged from 1.30 tons ha −1 to 7.12 tons ha −1 , for a total of 56 treatment blocks in 4 seasonal years (2014-2017). Results show that Crop RS-Met accurately reproduce seasonal changes in SWC with an average R 2 of 0.89 ± 0.05 and RMSE and bias of 0.014 ± 0.004 m 3 m −3 and-0.002 ± 0.004 m 3 m −3 , respectively. Modeled SWC Heading showed high and significant positive linear relationship with GY EOS (GY EOS [tons ha-1 ] = 0.080×SWC Heading [mm]-5.387; R 2 = 0.90; P < 0.001; N=56). Moreover, Crop RS-Met showed to be capable of accurately predicting GY EOS even in cases where water supply and grain yield had adverse relationships. Aggregating results to the field-scale level and classifying fields per water supply conditions resulted in an even stronger linear relationship (R 2 = 0.94; P < 0.001; N=9). We conclude that Crop RS-Met may be used to predict GY EOS at heading in dryland fields for possible use by farmers in decision making at critical wheat growth stages.
Predicting water balance of wheat and crop rotations with a simple model: AqYield
Agricultural and Forest Meteorology, 2018
Designing cropping systems that are well-adapted to water-limited conditions is one challenge of adapting agriculture to climate change. It requires estimating impacts of current and future cropping practices on crop water use and water resource availability in agricultural areas. Crop models such as AqYield are useful tools for evaluating effects of climate, soil and crop practices on evapotranspiration (ET) and drainage that directly impact soil available water (AW). AqYield is a simple model with few input data that has already been satisfactory evaluated for spring crops in southwestern France. Our main objective was to evaluate the ability of AqYield to predict components of soil water balance at the field level for crop rotations. First, we calibrated and evaluated AqYield predictions for winter wheat in France under a wide range of contrasting climatic and soil conditions. Fifty experimental situations (site × year × management) were chosen for calibration. AqYield was evaluated (i) for winter wheat in nine experimental situations, using daily drainage and ET data, and (ii) for two crop rotations on two fields with 7-years of continuous measurements of daily ET flux. During calibration, AqYield predicted soil AW in the contrasting situations with a model efficiency of 0.83, in the same range of accuracy as those of other widely published models. AqYield also predicted ET accurately from calibration and validation datasets, with a model efficiency of 0.84 and 0.69, respectively, for monthly ET. AqYield predicted daily and monthly drainage less accurately, although the range of drainage during the cropping period was predicted well. At the crop-rotation scale, AqYield yielded acceptable predictions of ET for contrasting climate conditions and crops. Whereas AqYield is simple and requires only a few input data, it accurately predicted ET of cropping systems. It therefore could be useful as a module in more complex modeling approaches.
AGU Fall Meeting Abstracts, 2018
Wheat production in drylands is determined greatly by the available water at the critical growth stages. In dry years, farmers usually face the dilemma of whether to harvest at an early stage for hay or silage, with reduced profit, or leave the crop for grain production with the risk of a major economic loss. Thus, an early prediction of potential wheat grain yield production is essential for agricultural decision making, particularly in water-limited areas. Here, we test whether using a proximal-based biophysical model of actual evapotranspiration (water use) and root-zone soil water content (SWC)-Crop RS-Met-may assist in providing early grain yield predictions in dryland wheat fields. Crop RS-Met was examined in eight experimental fields comprising a variety of spring wheat (Triticum aestivum L.) cultivars exposed to different treatments and amounts of water supply (185 mm-450 mm). Crop RS-Met was first validated against SWC measurements at the root-zone profile. Then, modeled SWC at heading (SWC Heading) was regressed against end-of-season grain yields (GY EOS), which ranged from 1.30 tons ha −1 to 7.12 tons ha −1 , for a total of 56 treatment blocks in 4 seasonal years (2014-2017). Results show that Crop RS-Met accurately reproduce seasonal changes in SWC with an average R 2 of 0.89 ± 0.05 and RMSE and bias of 0.014 ± 0.004 m 3 m −3 and-0.002 ± 0.004 m 3 m −3 , respectively. Modeled SWC Heading showed high and significant positive linear relationship with GY EOS (GY EOS [tons ha-1 ] = 0.080×SWC Heading [mm]-5.387; R 2 = 0.90; P < 0.001; N=56). Moreover, Crop RS-Met showed to be capable of accurately predicting GY EOS even in cases where water supply and grain yield had adverse relationships. Aggregating results to the field-scale level and classifying fields per water supply conditions resulted in an even stronger linear relationship (R 2 = 0.94; P < 0.001; N=9). We conclude that Crop RS-Met may be used to predict GY EOS at heading in dryland fields for possible use by farmers in decision making at critical wheat growth stages.
Ecological Modelling, 2004
A lysimeter experiment conducted on three soil types in a main agricultural production region of Austria in Marchfeld (latitude 48°12′N, longitude 16°34′E and altitude 150 m above sea level), was used to test the performance of the three widely used crop models, CERES, SWAP and WOFOST. The soils included chernozem, sandy chernozem and fluvisol with a 2.0 m profile depth. Daily measurements of the soil water content were taken using TDR probes (one per 0.3 m of depth) in six replicates for each soil type. The analysis was carried out for winter wheat and spring barley grown on the site during seasons 2000 and 2001 and included a detailed comparison of the simulated and measured soil water contents as well as an analysis of seasonal soil water balances, root front velocities and an evaluation of the modeled crop yields. CERES and SWAP, in contrast to WOFOST, simulated the grain yield of barley and wheat well. All three models simulated soil water content in the profile with similar results. The root mean square error (RMSE) range of soil water content was 0.71–4.67% for barley and 2.32–6.77% for wheat, depending on the model and soil type. None of the models simulated total soil water content in the profile significantly better, but there was a general tendency for the models to overestimate soil water depletion. Both CERES and SWAP mimicked the soil water content dynamics well in the top 0.3 m of the soil. The study shows that the multiple layer approach models (SWAP or CERES) including more sophisticated estimation methods for root growth and soil water extraction should be preferred in comparable environments. Further adjustments of evapotranspiration subroutines to the local conditions should be considered prior to the model use for drought impact assessment, yield forecasting or climate change impact studies.
Agricultural and Forest Meteorology, 2015
Climate-change scenarios predict increased scarcity of water available for agriculture in irrigated regions. To design and assess cropping system adaptations to more frequent droughts, soil-crop models are useful and efficient. The variety of models available makes it difficult to select one that is robust under a wide range of agro-pedoclimatic contexts and effective with limited input data and standard values. Here, we compared performances of two soil-crop models of contrasting complexity: STICS, a mechanistic model, and AqYield, which is much simpler and more empirical. For this purpose, predicted soil water contents and yields were compared to independent data acquired for three spring crops (maize, sunflower, and sorghum) at four sites in a summer-water-deficient region in southwestern France. Crops were grown under several irrigation strategies, from rainfed to full irrigation conditions. Both models tended to predict yields satisfactorily, but more accurately for maize, intermediate for sorghum and less accurately for sunflower. They accurately ranked situations according to crop, soil and irrigation strategy, but failed to rank inter-annual variability. Both AqYield and STICS predicted much of the variability observed in soil available water content (AWC) under maize and sorghum. Predictions were less accurate, although satisfactory, for sunflower. STICS underpredicted AWC under sorghum, but was generally more accurate than AqYield in situations with low water stress. AqYield was more accurate for high levels of water stress, but tended to overpredict AWC. Yearly dynamics of AWC were evaluated with a novel expert method. Both models accurately represented these dynamics in more than 60% of cases. Overall, we demonstrated that both models sufficiently predicted yield and water balance; however, STICS is more appropriate when other limiting processes need to be simulated.
Agricultural Water Management, 2021
Canada is one of the top wheat grain exporters, with a share of more than 10% in the world wheat market. The majority of Canadian wheat production takes place in the Prairies where 6.2 million ha of the area is seeded to spring wheat. The climate is semiarid with an estimated precipitation deficit of about 300 mm during the crop growing season, indicating that water is the primary limiting factor for crop production. In this study, three DSSAT-Wheat models (CSM-CERES,-CROPSIM,-NWHEAT) were used to quantify the impacts of water management practices on crop water stress and wheat yields. The models were evaluated individually and as an ensemble against observed wheat performance using three field experiments conducted to investigate irrigation, rainfed, and summer fallow impacts on wheat yields. The results showed that all three wheat models well simulated grain yield gains with irrigation and summer fallow that conserved additional soil water. Statistically, the multimodel ensemble improved the accuracy in simulating grain yields and biomass of spring wheat under both irrigated and rainfed conditions. The improvements could not be linked to eco-physiological processes in crop systems, and the increased simulation accuracy was likely due to the offsetting effects of simulation bias and errors from the individual models. Water input (precipitation + irrigation) of 400 mm was sufficient to reach the highest yield of spring wheat cultivars in the Canadian Prairies. Irrigation of 200 mm was able to alleviate most of the crop water stress in the study region. Further simulation scenarios showed that irrigating spring wheat when soil moisture was below 50% of available water capacity (AWC) for plants led to high yield, low irrigation rates, and reduced evaporation for different soil textures. Combined with improved drought tolerance varieties, this irrigation regime provides good prospects for increasing wheat yield and water use efficiency.
Agricultural Water Management, 2013
The dual crop coefficient (K c ) approach to estimate crop evapotranspiration (ET c ) separately considers soil evaporation (E) and plant transpiration (T) by computing a soil evaporation coefficient (K e ) and a basal crop coefficient (K cb ), respectively, with K c = K e + K cb . This approach may be more precise than the single K c approach particularly when the crops incompletely cover the ground. The SIMDualKc model, which is adopted in this study, is an irrigation scheduling simulation model that uses a daily time-step for performing two separate soil water balances, one for the soil evaporation layer from which K e is computed, and the other for the entire root zone, thus allowing to compute the actual K cb adjusted to the soil moisture conditions (K cb adj ). The standard K cb is corrected to the climate, crop density and height. Two years of field experimental data relative to winter wheat and summer maize were used for model calibration and validation using soil water content data observed with time-domain reflectometry (TDR) in a silt loam soil. Field data also include E measured with microlysimeters placed along the crop rows. The calibration procedure consisted in adjusting the basal crop coefficients, the soil evaporation parameters used to compute K e , and the soil water depletion fraction for no stress (p) to achieve the best fit of the observed soil water content data. The calibrated K cb values for winter wheat were 0.25 for the initial and the soil frozen period, 1.15 for the mid-season and 0.30 at harvesting. For the summer maize, the initial, mid season and end season K cb were respectively 0.2, 1.10 and 0.45. Model results have shown a good agreement between model predictions and field observations of the soil water content of both crops, with root mean square errors of estimates (RMSE) of about 0.01 m 3 m −3 for both the calibration and validation. The modelling efficiency EF and the index of agreement d IA were larger than 0.96 and 0.99, respectively, thus indicating good performance of modelling with SIMDualKc. Model estimates of E using Ritchie's approach were compared with microlysimeter data; for winter wheat a RMSE = 0.37 mm d −1 was obtained, while for maize RMSE of 0.45 and 0.49 mm d −1 were obtained for both years of observations. Results for soil evaporation allow confirming the appropriateness of using Ritchie's model to estimate soil evaporation of a cropped soil. E averaged 124 mm for wheat, representing 29% of ET c , and 146 mm for summer maize, i.e. 41% of ET c . In conclusion, results show that the model is appropriate to simulate the soil water balance adopting the dual K c approach and may be further used to develop improved irrigation schedules for the winter wheat-summer maize crop sequence in North China.
Journal of Hydrology and Hydromechanics, 2014
Mathematical models are effective tools for evaluating the impact of predicted climate change on agricultural production, but it is difficult to test their applicability to future weather conditions. We applied the SWAP model to assess its applicability to climate conditions, differing from those, for which the model was developed. We used a database obtained from a winter wheat drought stress experiment. Winter wheat was grown in six soil columns, three having optimal water supply (NS), while three were kept under drought-stressed conditions (S). The SWAP model was successfully calibrated against measured values of potential evapotranspiration (PET), potential evaporation (PE) and total amount of water (TSW) in the soil columns. The Nash-Sutcliffe model efficiency coefficient (N-S) for TWS for the stressed columns was 0.92. For the NS treatment, we applied temporally variable soil hydraulic properties because of soil consolidation caused by regular irrigation. This approach improve...
agronomy Article Integrating Wheat Canopy Temperatures in Crop System Models
2016
Abstract: Crop system models are generally parametrized with daily air temperatures recorded at 1.5 or 2 m height. These data are not able to represent temperatures at the canopy level, which control crop growth, and the impact of heat stress on crop yield, which are modified by canopy characteristics and plant physiological processes Since such data are often not available and current simulation approaches are complex and/or based on unrealistic assumptions, new methods for integrating canopy temperatures in the framework of crop system models are needed. Based on a forward stepwise-based model selection procedure and quantile regression analyses, we developed empirical regression models to predict winter wheat canopy temperatures obtained from thermal infrared observations performed during four growing seasons for three irrigation levels. We used daily meteorological variables and the daily output data of a crop system model as covariates. The standard cross validation revealed a ...