Simulate Crop Yield Research Articles

Modeling the Effects of Crop Rotation and Tillage on Sugarbeet Yield and Soil Nitrate Using RZWQM2

HighlightsFour crop growth modules in RZWQM2 were calibrated for four sugarbeet rotation sequences.Sugarbeet following wheat had a slightly higher yield (3% to 6.5%).Moldboard plow increased sugarbeet yield by 1% to 2%.The difference in N losses under different crop rotations and tillage operations was negligible.Abstract. Sugarbeet (Beta vulgaris) is considered to be one of the most viable alternatives to corn for biofuel production as it may be qualified as the feedstock for advanced biofuels (reducing greenhouse gas emission by 50%) under the Energy Independence and Security Act (EISA) of 2007. Because sugarbeet production is affected by crop rotation and tillage through optimal use of soil water and nutrients, simulation of these effects will help in making proper management decisions. In this study, the CSM-CERES-Beet, CSM-CERES-Maize, CROPSIM-Wheat, and CROPGRO-Soybean models included in the RZWQM2 were calibrated against experimental field data of crop yield, soil water, and soil nitrate from the North Dakota State University Carrington Research Extension Center from 2014 to 2016. The models performed reasonably well in simulating crop yield, soil water, and nitrate (rRMSE = 0.055 to 2.773, d = 0.541 to 0.997). Simulation results identified a non-significant effect of crop rotation on sugarbeet yield, although sugarbeets following wheat resulted in 3% to 6.5% higher yields compared to other crops. Net mineralization and N uptake rates were slightly higher when sugarbeets followed wheat compared to the other crops. Seasonal N and water mass balances also showed lower N and water stresses when sugarbeets followed wheat. The effects of tillage operations on sugarbeet yield were also non-significant. The difference in the N losses to runoff and drainage from the sugarbeet fields under different crop rotations and tillage operations was negligible. As sugarbeet production may be expanded into nontraditional planting areas in the Red River Valley due to potential demand for biofuel production, our findings will help to assess the associated environmental impacts and identify suitable crop rotations and management scenarios in the region. Keywords: Biofuel, Crop rotation, RZWQM2, Sugarbeet, Tillage.

黑河中游主要农作物灌溉制度优化

科学的灌溉制度是干旱半干旱地区农业生产的重要保障。黑河位于西北干旱区，是我国第二大内陆河，且当地中游农业灌溉和下游生态需水矛盾十分突出。利用DSSAT （Decision Support for Agro-technology Transfer）模型模拟了黑河中游地区玉米、小麦、油菜、马铃薯的生长情况，对比分析了四种作物生育期内需水量变化与当地降水条件、现行灌溉制度之间的差异。通过设置灌溉组合探究了四种作物最适宜的灌溉制度，并计算了优化灌溉制度下的节水潜力。结果表明：DSSAT模型通过参数校正与验证后，对四种作物生长过程模拟性能较好，产量标准化均方根误差（nRMSE）均低于15.0%，决定系数（R²）均达到0.65以上。缺水量模拟结果表明，四种作物生长季平均水分亏缺介于122.5-367.0 mm。通过调整灌溉制度，可使玉米、小麦、油菜、马铃薯的水分利用效率分别提高54.8%、25.0%、18.3%和51.3%，且产量变幅均低于5.0%，实现了高产节水的目的。在研究区实施最优灌溉制度，中游农业灌区每年可以节省8.1×10⁸ m³的水资源量，用于支持下游生态保护。;Scientific irrigation scheduling is essential for agricultural production in arid and semi-arid regions. Located in the northwest arid region, the Heihe River is the second largest inland (terminal lake) river in China and faces increasing competition for water between the middle reach agricultural irrigation and lower reach ecosystem services. In this study, we use the Decision Support for Agro-technology Transfer (DSSAT) model to simulate the growth of four main crops:maize, wheat, rape seed and potato in the middle reaches of the Heihe River Basin. We first compared the temporal differences between the water demands of the four crops and the precipitation during the growing season as well as the differences between the crop water demands and the current irrigation scheduling. Subsequently, we explored multiple irrigation scheduling combinations during the growth period to optimize irrigation scheduling of the four crops. Finally, we calculated the water saving potential under the optimal irrigation scheduling. Results show after calibration and validation with in situ observations, the DSSAT model has better simulation performance for the four crops in the study region. The standardized root mean square error (nRMSE) of the crop yields is less than 15.0%, and the coefficient of determination (R²) is above 0.65. The annual average water deficit of the four crops ranged from 122.5 to 367.0 mm during the growth season. By adopting the optimal irrigation scheduling, the water use efficiency of maize, wheat, rape seed, and potato could be improved by 54.8%, 25.0%, 18.3% and 51.3%, respectively, and the variation of the simulated crop yield was all lower than 5.0%, achieving both high yield and water conservation. If the optimal irrigation scheduling is applied in the study area, potential water saving could reach to 8.1×10⁸ m³ annually in the middle reaches, which could be used to support downstream ecological protection.

Quantifying nitrate leaching to groundwater from a corn-peanut rotation under a variety of irrigation and nutrient management practices in the Suwannee River Basin, Florida

Nitrate leaching from agricultural fields is a significant contributor of groundwater pollution globally, threatening drinking water resources and downstream ecosystems. Quantifying nitrate leaching driven by variable climate, soils, and management practices is challenging, but it is critical for developing sustainable agricultural production systems. While irrigation and fertilizer “best management practices” (BMPs) have been widely implemented to reduce agricultural nitrate leaching, their ability to meet environmental protection goals remains uncertain. In this study, we used the Soil and Water Assessment Tool (SWAT) to simulate crop yields and nitrate leaching for corn-peanut rotations under a variety of nutrient and irrigation management practices in the Suwannee River Basin (Florida), where groundwater feeds springs that are protected by a federally mandated nutrient criteria of 0.35 mg/L Nitrate-Nitrogen (NO3-N). Data from a field experiment of nine irrigation and nitrogen (N) management treatments were used to calibrate SWAT, with good to excellent results (Nash Sutcliffe Efficiencies from 0.72 to 0.97 for soil moisture, 0.85–0.96 for crop yield, 0.48–0.96 for crop N uptake, and 0.15–0.82 for soil nitrate). The calibrated model was then used to quantify differences in crop yields, irrigation applied and nitrate leaching among practices over a range of historical weather. Soil moisture sensor-based irrigation with 246 kg N/ha for corn and 0 kg N/ha for peanut had no statistical difference in yields compared to common practices in the region (calendar-based irrigation, fertilization of 336 kg N/ha corn and 17 kg N/ha peanut), while reducing N leaching by 40% and irrigation applied by 45% (reductions of ~70 kg N/ha/yr and ~300 mm/year, respectively). Planting a rye cover crop reduced leaching by an additional ~50 N/ha/yr for all treatments. These results show the potential for widespread adoption of nutrient and water conservation practices to achieve the reductions in NO3-N load needed to meet environmental and regulatory goals without impacting crop yields.

Evaluation of Winter Wheat Yield Simulation Based on Assimilating LAI Retrieved From Networked Optical and SAR Remotely Sensed Images Into the WOFOST Model

To obtain sufficient observation data and simulate higher-precision crop yields, a crop yield simulation scheme was built based on the WOrld FOod STudies (WOFOST) crop growth model and a 4-D ensemble square root filter (4-DEnSRF) assimilation algorithm, and the time series of the leaf area index (LAI) retrieved by optical and synthetic aperture radar (SAR) networking data was introduced into the crop yield estimation scheme. Taking Shenzhou County, Hebei Province, as the study area, using the field-measured data as verification data, the regional application of winter wheat yield estimation was effectively carried out with a grid size of 500 m. Comparisons were made between the simulated yields based on different networked data of three key phenologies of winter wheat. The regional yield estimation results revealed an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> and normalized root mean squared error (NRMSE) between the simulated yield based on optical LAIs and the field-measured yield of 0.517 and 17.60%, respectively, while the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> and NRMSE between the simulated yield based on networked optical-SAR LAIs filtered by the Gaussian filtering algorithm (GFA) and the field-measured yield were 0.573 and 12.98%, respectively. From the comparisons between the simulated yields based on networked data of different combinations of key phenologies, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> and NRMSE between the simulated yield based on the introduced SAR LAI at the jointing stage and the field-measured yield were 0.437 and 21.49%, respectively, and were higher correlation among the three modes of networked data of different combinations of key phenologies. The winter wheat yield simulation results showed that the introduction of SAR LAIs at key crop growth stages (especially the jointing and booting stage) as outer observation data had a mild impact on the value of simulated winter wheat yield. Moreover, Gaussian filtering could reduce errors caused by multisource networked data to a certain extent. Thus, it can be concluded that using some radar images instead of optical images to retrieve LAI and assimilating multisource remotely sensed LAI into the crop model to simulate crop yield could enhance the reliability and robustness of the crop yield simulation system to some extent.

Assimilation of coupled microwave/thermal infrared soil moisture profiles into a crop model for robust maize yield estimates over Southeast United States

Global food security is one of the most pressing issues of the current century, particularly for developing nations. Agricultural simulation models can be a key component in testing new technologies, seeds and cultivars etc., however, inaccurate input information in addition to model related errors adds to model uncertainties. Satellite observations of soil moisture (SM), vegetation index etc. can be assimilated into crop models to reduce input and model related uncertainties. The goal of this study was to determine if assimilation of satellite-driven SM profiles can improve crop model yield estimations in a commonly used crop model while reducing the reliance on field management information at regional scales. Toward reaching this goal, this study utilized satellite derived microwave and thermal-infrared coupled SM profiles assimilated into a crop model via the Ensemble Kalman Filter over parts of the Southeastern United States from 2006 to 2010. The gridded Decision Support System for AgroTechnology Transfer (GriDSSAT) model was used to test and verify the SM profile assimilation methodology in two specific modes: (a) first using the best state-of-the art climate inputs available without data assimilation (“open-loop”); (b) then enhancing the open-loop model by assimilating into the model Remote sensing Driven SM Profile (RDSMP) data where available. The National Agricultural Statistical Services (NASS) reported yield data at county levels were used for comparison and validation purposes. For non-irrigated regions, the GriDSSAT model simulation (rain-fed) showed an overall absolute error of nearly 36 % in comparison with the reported NASS yields, whereas the error in crop yields after data assimilation was only 29 %. Over irrigated counties, the GriDSSAT model simulation (irrigated) showed an error of nearly 24 % while using the data assimilation model simulation without irrigation information also showed overall error of ∼23 %. Compared to the open-loop model simulation and improvement in simulated crop yields of nearly 2.5 times was found over irrigated regions (23 % vs 58 %).

The use of remote sensing to derive maize sowing dates for large-scale crop yield simulations

One of the major sources of uncertainty in large-scale crop modeling is the lack of information capturing the spatiotemporal variability of crop sowing dates. Remote sensing can contribute to reducing such uncertainties by providing essential spatial and temporal information to crop models and improving the accuracy of yield predictions. However, little is known about the impacts of the differences in crop sowing dates estimated by using remote sensing (RS) and other established methods, the uncertainties introduced by the thresholds used in these methods, and the sensitivity of simulated crop yields to these uncertainties in crop sowing dates. In the present study, we performed a systematic sensitivity analysis using various scenarios. The LINTUL-5 crop model implemented in the SIMPLACE modeling platform was applied during the period 2001–2016 to simulate maize yields across four provinces in South Africa using previously defined scenarios of sowing dates. As expected, the selected methodology and the selected threshold considerably influenced the estimated sowing dates (up to 51 days) and resulted in differences in the long-term mean maize yield reaching up to 1.7 t ha−1 (48% of the mean yield) at the province level. Using RS-derived sowing date estimations resulted in a better representation of the yield variability in space and time since the use of RS information not only relies on precipitation but also captures the impacts of socioeconomic factors on the sowing decision, particularly for smallholder farmers. The model was not able to reproduce the observed yield anomalies in Free State (Pearson correlation coefficient: 0.16 to 0.23) and Mpumalanga (Pearson correlation coefficient: 0.11 to 0.18) in South Africa when using fixed and precipitation rule-based sowing date estimations. Further research with high-resolution climate and soil data and ground-based observations is required to better understand the sources of the uncertainties in RS information and to test whether the results presented herein can be generalized among crop models with different levels of complexity and across distinct field crops.

System dynamics simulation of crop yield under different irrigation water quality and quantity

Abstract Agricultural products are one of the major sources of food for people in many countries. So, increasing crop yield is a major challenge for governments as increasing the population. On the other hand, agricultural improvement needs to take into consideration the type of land and water available. The decrease of water availability and soil salinity are two major limiting factors in sustainable agriculture in the arid and semiarid regions. In this study, the combined effect of salinity and water stress on crop yield was simulated by developing a computer model based on a system dynamics approach. Model calibration and validation were performed using data collected from the Abshar Irrigation Network located on the hydrological Zayandehrud River. For each individual run of the model, two statistics were calculated: Root Mean Square Error and Standard Error. The averages of these indices were estimated as 2,777 kg·ha−1 and 0.07 for sugar beet yield, 0.026 and 0.09 for soil moisture and finally 0.54 dS·m−1 and 0.08 for salinity of the root zone, respectively. The result showed a good agreement between the simulation model and the actual data. Therefore, the model can be calibrated and used to estimate the crop yield with reasonable accuracy.

Crop Coefficient for Potato Crop Evapotranspiration Estimation by Field Water Balance Method in Semi-Arid Region, Maharashtra, India

Crop evapotranspiration (ETc) estimation is essential for many studies such as irrigation system design and management, crop yield simulation, and water resource planning and management. Field studies were conducted at MPKV Rahuri, Maharashtra from 2015 - 2016 and 2016 - 2017 (2 years) in clay soils to determine crop evapotranspiration and crop coefficients (Kc) of the potato crop. The experimental area was cultivated with irrigation applied at 7-days interval. The irrigation scheduling was based on the field water balance approach. The crop evapotranspiration was determined by the field water balance; reference evapotranspiration (ETo) by the Penman-Monteith method and crop coefficients were computed using the standard FAO-56 methodology. The total reference evapotranspiration (ETo) and crop evapotranspiration (ETc) were 226 and 240 mm for the year 2015–2016 and 248 and 250 mm for the year 2016–2017, respectively. The 2-year average reference evapotranspiration was 237 mm and the crop evapotranspiration was 245 mm. The average estimated Kc values for the semi-arid region during the vegetative, tuber development, and maturity stages for potato are 0.55, 1.11, and 1.01, respectively. The calculated values are slightly lower than those suggested by FAO-56 for the vegetative and tuber development stages and higher for the maturity stage of potato. The estimated values of crop coefficients for potato are 11.25% higher than those suggested by FAO-56. However, observed variation between values from the FAO-Kc and Kc calculated by the field water balance is not significant. So, these values can be used for irrigation scheduling of potato in the semi-arid region.

Basic Soil Data Requirements for Process-Based Crop Models as a Basis for Crop Diversification

Data from global soil databases are increasingly used for crop modelling, but the impact of such data on simulated crop yield has not been not extensively studied. Accurate yield estimation is particularly useful for yield mapping and crop diversification planning. In this article, available soil profile data across Sri Lanka were harmonised and compared with the data from two global soil databases (Soilgrids and Openlandmap). Their impact on simulated crop (rice) yield was studied using a pre-calibrated Agricultural Production Systems Simulator (APSIM) as an exemplar model. To identify the most sensitive soil parameters, a global sensitivity analysis was performed for all parameters across three datasets. Different soil parameters in both global datasets showed significantly (p &lt; 0.05) lower and higher values than observed values. However, simulated rice yields using global data were significantly (p &lt; 0.05) higher than from observed soil. Due to the relatively lower sensitivity to the yield, all parameters except soil texture and bulk density can still be supplied from global databases when observed data are not available. To facilitate the wider application of digital soil data for yield simulations, particularly for neglected and underutilised crops, nation-wide soil maps for 9 parameters up to 100 cm depth were generated and made available online.

Drainage N Loads Under Climate Change with Winter Rye Cover Crop in a Northern Mississippi River Basin Corn-Soybean Rotation

To help reduce future N loads entering the Gulf of Mexico from the Mississippi River 45%, Iowa set the goal of reducing non-point source N loads 41%. Studies show that implementing winter rye cover crops into agricultural systems reduces N loads from subsurface drainage, but its effectiveness in the Mississippi River Basin under expected climate change is uncertain. We used the field-tested Root Zone Water Quality Model (RZWQM) to estimate drainage N loads, crop yield, and rye growth in central Iowa corn-soybean rotations. RZWQM scenarios included baseline (BL) observed weather (1991–2011) and ambient CO2 with cover crop and no cover crop treatments (BL_CC and BL_NCC). Scenarios also included projected future temperature and precipitation change (2065–2085) from six general circulation models (GCMs) and elevated CO2 with cover crop and no cover crop treatments (CC and NCC). Average annual drainage N loads under NCC, BL_NCC, CC and BL_CC were 63.6, 47.5, 17.0, and 18.9 kg N ha−1. Winter rye cover crop was more effective at reducing drainage N losses under climate change than under baseline conditions (73 and 60% for future and baseline climate), mostly because the projected temperatures and atmospheric CO2 resulted in greater rye growth and crop N uptake. Annual CC drainage N loads were reduced compared with BL_NCC more than the targeted 41% for 18 to 20 years of the 21-year simulation, depending on the GCM. Under projected climate change, average annual simulated crop yield differences between scenarios with and without winter rye were approximately 0.1 Mg ha−1. These results suggest that implementing winter rye cover crop in a corn-soybean rotation effectively addresses the goal of drainage N load reduction under climate change in a northern Mississippi River Basin agricultural system without affecting cash crop production.

A Bayesian framework to unravel food, groundwater, and climate linkages: A case study from Louisiana.

Advancing our understanding of the connections among groundwater, food, and climate is critical to meet global food demands while optimizing water resources usage. However, our understanding of the linkages among groundwater, food, and climate is still limited. Here, we offer a Bayesian framework to simulate crop yield at a regional scale and quantify its relationships and associated uncertainty with climate, groundwater, agricultural, and energy-related variables. We implemented the framework in the rice-producing regions of Louisiana from 1960-2015. To build a parsimonious model, we used a probability-based variable selection approach to detect the key drivers of rice yield. Rice yield increased, groundwater declined, and area planted declined or did not change over 56yrs. The number of irrigation wells, groundwater level, air temperature, and area planted were found to be the key drivers of rice yield. The regression coefficients showed that rice yield was positively related to groundwater level, and negatively related to area planted and the number of irrigation wells. The limited influence of N fertilizer was noted on rice yield for the period when fertilizer data were available. The inverse relationship between rice yield and area planted pointed to the adaption of efficient crop management practices that maintained or increased yield, despite the decline in area planted. The farmers' ability to install irrigation wells during droughts sustained the yields over long-term but not short-term. This decline in rice yield in response to drought over the short-term might explain the negative relation between yield and irrigation wells. Overall, this work highlighted the uncertainty in relationships between rice yield and key drivers and quantified the intimate connection between food and groundwater. This work may have implications for managing two highly competing commodities (i.e., groundwater and food) in agricultural regions.

ASSESSMENT OF CROP YIELD AND RAINFALL SIMULATION IN NASARAWA TOWN NASARAWA STATE NIGERIA

Recent technology use simulation to predict the amount and total crop production and yield in a particular piece of land. Crop yield is termed as the growth of crop per unit area. This study calculates the crop yield for 20 years and uses simulation to produce 18 years of crop yields at different locations in Nasarawa Local Government Area of Nasarawa State Nigeria. the study applies the use of time series analysis of both Linear, quadratic and growth curve models to ascertain the crop yield. The result indicates that there is a high amount of rainfall in the preceding year from 2020 -2038 with a rainfall trend of more than 2200mm- 2300mm per annum. The crop yield simulation shows a higher growth curve with a bumper harvest in the next years to come.

Crop yield simulations in Mexican agriculture for climate change adaptation

Climate change is considered a serious threat to food security worldwide. In this study, yields of maize, beans, wheat, soybean, sorghum, barley and potato were modeled with 28 future climate change scenarios. Our results reduce the information gap that is frequently reported for Mexico and will contribute to better knowledge on spatial impact of climate change. We applied FAO AquaCrop model for 22 case studies located in 14 states of Mexico. Climate change scenarios were: CNRM, GFDL, HADGEM, MPI and Ensemble REA, with two radiative forcing concentrations (4.5 and 8.5 W m –2 ) and three time horizons (2015-2039, 2045-2069, and 2075-2099). The results show decreases in yields of most of the case studies as a consequence of a decrease in the amount and distribution of precipitation. Maize yield in warm dry climates could decrease up to 84% in the most severe scenarios. Beans could decrease from 10 to 40% in the north of the country, while in the northwest a 15% decrease in wheat yield is predicted. Soybeans could benefit, with increases from 15 to 40%. Sorghum and potatoes are expected to decrease for all the case studies, while barley would have increases and decreases. The results suggest differentiated impacts according to crops and regions studied. We concluded that agriculture requires better focused strategies and policies (attention on crop and spatial distribution).

Role of deficit irrigation strategies on ET partition and crop water productivity of rice in semi-arid tropics of south India

Crop water productivity (CWP) is a measure of crop yield per unit of water consumed and plays a crucial role in evaluating the role of alternate management practices for sustainable production. This study investigates the crop water and yield dynamics of rice (Oryza sativa L.) subjected to three (low, moderate, and high water stress) deficit irrigation scenarios. Factorial experiments were conducted in four paddy fields in a command area of south India during the 2017–18 growing seasons with farmers’ current irrigation practice used as the control (T1). Seasonal total and irrigation water application of rice was ranged from 803 and 175 mm for high stress to 978 and 350 mm for T1 conditions. Cumulative potential evapotranspiration (ETo) for the growing seasons was observed to be 326.65 ± 2.51 mm. The FAO-56-based SIMDualKc model was parameterized to partition the evapotranspiration (ET) fluxes and to obtain site-specific crop coefficients for each scenario. Model-simulated root zone soil moistures were in agreement with the observations for all irrigation treatments (R2 > 0.80, RMSE < 0.04 cm3 cm−3, n = 20). Stage-specific single (Kc) and basal crop (Kcb) coefficients for rice with T1 are, respectively, 1.1, 1.09, 0.79, and 0.9, 0.97, 0.57, which are slightly lower than FAO tabulated values. The calibrated soil–water balance model was further applied to simulate crop yield using a simple crop growth algorithm. Our results conclude that crop yield is sensitive to water stress during vegetation (ky = 1.14 ± 0.10) followed by transplantation (ky = 1.07 ± 0.16) and reproduction stages (ky = 1.02 ± 0.17). We observed a marginal increase in CWP (12% in 2017 and 3% in 2018) and a reduction in deep percolation losses (29% in 2017 and 14% in 2018) through controlled water-saving strategies.

Climate change impact on yields and water use of wheat and maize in the North China Plain under future climate change scenarios

Climate change has already and will continue to exert a vital impact on crop yield and water use in the North China Plain (NCP). Currently, this plain is facing a dilemma between groundwater depletion and grain production demand. It is urgent to identify the impact of future climate change on crop yield and water consumption and then develop efficient adaptation strategies in the region. In this study, we used statistically downscaled daily climate data from 33 global climate models (GCMs) and two Representative Concentration Pathways (RCP4.5 and RCP8.5) for 61 stations distributed across the NCP and drove the well-validated APSIM model to simulate crop yield and water use for two future periods of 2031–2060 (2040s) and 2071–2100 (2080s). Data from all 33 GCMs show an increase in annual mean temperature and almost all the GCMs also show increases in annual mean solar radiation and annual total precipitation across the NCP. Future climate warming led to an advance in phenology for both winter wheat and summer maize, two typical crops in the NCP. However, the length of the reproductive growth period (RGP) of winter wheat was prolonged while that of summer maize was shortened under future climate scenarios across the NCP. Our simulated results show that future climate change had negative impacts on maize yield but positive impacts on wheat yield across the NCP. Mainly due to the shortening of the whole growth period, crop water consumption was largely decreased under future climate scenarios. The amount of irrigation required was also reduced mainly due to increased precipitation and decreased ET. Although future climate change would likely mitigate groundwater overdraft in most part of NCP, some areas in the northern NCP still had groundwater over-pumping in the future. Therefore, we suggest that it might be a good choice to change cropping system for reducing planting area of water-consuming crop (e.g. winter wheat) in those over-pumping areas to balance groundwater use and crop yield.

VALIDATION OF AQUACROP MODEL FOR SIMULATION OF RAINFED BULB ONION (ALLIUM CEPA L.) YIELDS IN WEST UGENYA SUB-COUNTY, KENYA

&lt;p&gt;&lt;strong&gt;Background. &lt;/strong&gt;The precision of crop growth simulation models is a paramount facet in their use for evaluating on-field management practices to improve crop yields.&lt;strong&gt; Objective.&lt;/strong&gt; To validate the accuracy of AquaCrop model in simulating onion yields and canopy cover in the sub humid environment of West Ugenya Sub County, Kenya. &lt;strong&gt;Methodology.&lt;/strong&gt; Aqua Crop model version 5.0 was evaluated for experimental yields of bulb onion (&lt;em&gt;Allium cepa &lt;/em&gt;L.) grown in West Ugenya Sub County, Kenya for two seasons (March to May long rains season, and October to December short rains season) on soil integrated with organic and inorganic fertilizers i.e. 5 Mega grams ha&lt;sup&gt;-1&lt;/sup&gt; cattle manure combined with inorganic fertilizers containing 28 kg P Ha&lt;sup&gt;-1&lt;/sup&gt; and 30 kg N ha&lt;sup&gt;-1&lt;/sup&gt;. The model was calibrated based on its conservative parameters for C3 crops under the Growing Degree Days (GDD) mode. &lt;strong&gt;Results.&lt;/strong&gt; Statistical comparison of the model’s simulated yields versus experimental yields gave RMSE (Root Mean Square Error) values of 0.22 and 0.61 in season I and season II, respectively, which are generally closer to zero, indicating average to high model precision. Modified Willmott index of agreement (d &lt;sub&gt;mod&lt;/sub&gt;) was 0.44 (season I) and 0.69 (season II), while for Nash and Sutcliffe coefficient (E), 0.85 (season I) and 0.14 (season II). The constant for d &lt;sub&gt;mod&lt;/sub&gt; and E indicates high model accuracy if value is close to one. The values from the simulations were detached, generally indicating in both cases average to high model performance. The canopy cover development from germination to the crop’s 150 days to physiological maturity gave a Pearson correlation coefficient (r) that averaged 0.9. The r-values were close to one, indicating a positive linear relationship between simulated and experimental canopy cover. &lt;strong&gt;Conclusion. &lt;/strong&gt;Overall, the model provided acceptable simulation of onion crop yield and canopy cover.&lt;/p&gt;

Direct assimilation of measured soil water content in Root Zone Water Quality Model calibration for deficit‐irrigated maize

AbstractCorrect soil water simulation is critical for water balance and plant growth in agricultural systems. Crop production simulation errors have often been attributed to a lack of accuracy in soil water content (SWC) estimates. However, only a few studies have quantified the effects of SWC estimate errors on crop production and evapotranspiration (ET), especially under different irrigation treatments. The objective of this study was to investigate the impacts of direct assimilation of measured SWC during model calibration for deficit irrigated maize (Zea mays L.) on simulated ET, leaf area index (LAI), biomass, and yield. The CERES‐Maize model within the Root Zone Water Quality Model (RZWQM) was calibrated using the automatic parameter estimation (PEST) software. Simulation results showed that, using PEST‐optimized crop parameters, RZWQM was able to adequately predict crop yield (relative root mean squared error, rRMSE, of 4.8%) and biomass (rRMSE of 7.1%) in response to irrigation levels, in spite of the bias in SWC and ET simulation. However, with the same crop parameters but replacing simulated SWC with measured data, simulations of crop yield and biomass became worse, with higher rRMSE values (14.5% for yield and 21.5% for biomass). This unexpected model performance with SWC assimilation was mainly associated with the water addition and removal from the soil, which was improved only by recalibration of both soil and crop parameters. This study suggested compensating effects between soil and crop parameters during model calibration. Caution should be applied when using measured SWC as model inputs, especially under water stress conditions.

OPTIMIZED IRRIGATION SCHEDULING USING SWAT FOR IMPROVED CROP WATER PRODUCTIVITY

AbstractThe agricultural sector is the largest consumer of the water resources potential of India. The ever‐increasing demand for this finite resource has led to water shortage in the Anandapur sub‐basin of the Baitarani River basin, which comes under the hot and subhumid region of India. The yield of maize, green gram and winter wheat in the basin was simulated for the Rabi season (October–February) using SWAT, considering both rainfed and irrigated scenarios. As observed, water stress progressively increased with a shift in the sowing period of crops from 15 October to 5 November at an interval of 10 days. In the present work, irrigation was scheduled considering the early sowing period, i.e. 15 October. Model performance was evaluated in terms of Nash–Sutcliffe efficiency (NSE), coefficient of determination (R2) and percentage bias (PBIAS). The performance of the model was found to be satisfactory for crop yield simulation during the calibration and validation periods. An optimal irrigation strategy was analysed considering three irrigation settings, i.e. S2 (sufficient irrigation), S3 (50% depletion of available water content, i.e. AWC) and S4 (70% depletion of AWC). The reduction in crop yield in S4 (deficit irrigation) was found to be relatively insignificant compared to full irrigation under scenario S2. © 2020 John Wiley &amp; Sons, Ltd.

A multi-model analysis of teleconnected crop yield variability in a range of cropping systems

Abstract. Climate oscillations are periodically fluctuating oceanic and atmospheric phenomena, which are related to variations in weather patterns and crop yields worldwide. In terms of crop production, the most widespread impacts have been observed for the El Niño–Southern Oscillation (ENSO), which has been found to impact crop yields on all continents that produce crops, while two other climate oscillations – the Indian Ocean Dipole (IOD) and the North Atlantic Oscillation (NAO) – have been shown to especially impact crop production in Australia and Europe, respectively. In this study, we analyse the impacts of ENSO, IOD, and NAO on the growing conditions of maize, rice, soybean, and wheat at the global scale by utilising crop yield data from an ensemble of global gridded crop models simulated for a range of crop management scenarios. Our results show that, while accounting for their potential co-variation, climate oscillations are correlated with simulated crop yield variability to a wide extent (half of all maize and wheat harvested areas for ENSO) and in several important crop-producing areas, e.g. in North America (ENSO, wheat), Australia (IOD and ENSO, wheat), and northern South America (ENSO, soybean). Further, our analyses show that higher sensitivity to these oscillations can be observed for rainfed and fully fertilised scenarios, while the sensitivity tends to be lower if crops were to be fully irrigated. Since the development of ENSO, IOD, and NAO can potentially be forecasted well in advance, a better understanding about the relationship between crop production and these climate oscillations can improve the resilience of the global food system to climate-related shocks.

The Implication of Different Sets of Climate Variables on Regional Maize Yield Simulations

High-resolution and consistent grid-based climate data are important for model-based agricultural planning and farm risk assessment. However, the application of models at the regional scale is constrained by the lack of required high-quality weather data, which may be retrieved from different sources. This can potentially introduce large uncertainties into the crop simulation results. Therefore, in this study, we examined the impacts of grid-based time series of weather variables assembled from the same data source (Approach 1, consistent dataset) and from different sources (Approach 2, combined dataset) on regional scale crop yield simulations in Ghana, Ethiopia and Nigeria. There was less variability in the simulated yield under Approach 1, ranging to 58.2%, 45.6% and 8.2% in Ethiopia, Nigeria and Ghana, respectively, compared to those simulated using datasets retrieved under Approach 2. The two sources of climate data evaluated here were capable of producing both good and poor estimates of average maize yields ranging from lowest RMSE = 0.31 Mg/ha in Nigeria to highest RMSE = 0.78 Mg/ha under Approach 1 in Ghana, whereas, under Approach 2, the RMSE ranged from the lowest value of 0.51 Mg/ha in Nigeria to the highest of 0.72 Mg/ha in Ethiopia under Approach 2. The obtained results suggest that Approach 1 introduces less uncertainty to the yield estimates in large-scale regional simulations, and physical consistency between meteorological input variables is a relevant factor to consider for crop yield simulations under rain-fed conditions.