Cultivar Coefficients Research Articles

Yield gap analysis and the relative importance of factors explaining yield variability in paddy fields

Closing the yield gap of strategic crops can facilitate achieving sustainable solutions to alleviate hunger, ensure food security, and enhance nutrition. This study aimed to quantify factors affecting rice yield gap in northern Iran using experimental and survey data on yields and management practices. The CERES-Rice model, calibrated with data (2011, 2016, 2019) from two regions, was used to estimate potential yield (Yp) of different rice cultivars. Yield gaps were calculated as the difference between economic yield potential and the average yield attained by farmers. The calibration process showed that cultivar coefficients varied across rice varieties. The simulated yields (2012–2021) showed that the mean potential yields were 13362 kg ha−1 and 8818 kg ha−1 for the late and early-maturing cultivars, respectively. High temperature was the most important yield defining factor across the rice growing areas. The yield gap was 3751 kg ha−1 and 2830 kg ha−1 in the late- and early-maturing cultivars, respectively. Nitrogen fertilizer rate, number of fertilizer applications, phosphorus fertilizer rate, transplanting date, potassium fertilizer rate, and seedling age were the top factors influencing the yield gap. Among the management factors, rice fertilizer management explained about 65% of the yield variability. Together, transplanting date and seedling age explained 15.3% of the variability in rice production, while pest, disease, and weed damage accounted for 9.6%. Breaking down the yield gap into affecting factors allows growers to improve the production system by providing the best crop management according to their facilities and capabilities. This approach is essential for optimizing agricultural productivity and achieving sustainable food production goals.

Modeling performances of maize cultivars under current and future climate scenarios in southern central Ethiopian rift valley

BackgroundIn southern central rift valley of Ethiopia, maize is an important crop because of its adaptation to wider agro-ecologies and higher yield potential. However, most cultivars were not parameterized to include in the database of Decision Support System for Agro-technology Transfer (DSSAT). As a result simulation of growth and yield of those cultivars was not possible under changing climate.MethodsTwo set of independent crop, management and soil data were used for calibration and validation of genetic coefficients of maize cultivars (BH-540, BH-546, BH-547, Shala and Shone) under condition of historic weather (1990–2020). Later, we simulated the growth and yield of maize using twenty multimodel climate ensembles across RCP 4.5 and 8.5 during early, medium and late century across Shamana, Bilate, Hawassa and Dilla clusters using DSSATv4.8 model.ResultsCultivars BH-540, BH-546, BH-547, Shala and Shone produced yields of 5.7, 5.4, 5.2, 6.9 and 7.4 t ha−1 with the corresponding error percentage of − 0.1, − 0.8, − 1.0, − 6.1 and 2.6%. The results of normalized root mean square were 1.14–4.2 and 3.0–3.9%, for grain yield during calibration and validation, respectively showing an excellent rating. The simulation experiment produced 5.4–9.2 t ha−1 for grain yield of maize cultivars across the study areas, which is likely to fall close to 63.3% by 2070 if right adaptation options are not introduced necessitating switch in cultivars and production areas.ConclusionsThere is critical need for reduction of GHGs emissions, generation of innovative adaptation strategies, and development of drought and heat stress tolerant maize cultivars. Hence, researchers and policy makers shall act with utmost urgency to embark with breeding programs that target climate change adaptation traits in maize crop.

Improving crop model accuracy in the development of regional irrigation and nitrogen schedules by using data assimilation and spatial clustering algorithms

Crop growth models have been used to develop irrigation and nitrogen schedules (INSs). However, differences in crop cultivar coefficients are often ignored in the development of regional INSs. This study aimed to formulate suitable INSs under spatial heterogeneities in crop cultivar coefficients. Therefore, we propose two strategies for retrieving maize cultivar coefficients by using a data assimilation algorithm and spatial clustering algorithm. The first strategy involves determining the cultivar coefficients for each simulation unit in the examined region and then assigning different cultivar coefficients to the different clusters obtained using a spatial clustering algorithm, with the cultivar coefficients employed as clustering characteristics (CAs). The second strategy involves assigning different cultivar coefficients to the different clusters obtained using a spatial clustering algorithm on the basis of cultivar coefficients and geographical characteristics (CAGCs). By using observational data, the accuracy of cultivar coefficients CAs and CAGCs was compared with that of commonly used regional representative coefficients (RRs) and sub-region representative coefficients (SRRs). Furthermore, we formulated INSs with these four coefficients by using yield maximization as the objective and water use efficiency (WUE) and nitrogen use efficiency (NUE) as constraints. We examined the differences between the INSs, yields, WUE values, and NUE values obtained using each of the aforementioned four coefficients and those obtained using a point optimization approach. The results revealed that the highest accuracy in the simulation of the regional leaf area index, yield, and phenological stage was exhibited by the CAGCs-based strategy, followed by the CAs-based strategy. The RRs-based and SRRs-based strategies produced considerable errors. Crucially, the INSs obtained using the CAGCs-based strategy were more similar to those obtained through point optimization and more reasonable than were the INSs obtained using the other three strategies. In addition, more accurate yield, WUE, and NUE values were obtained with the CAGCs-based strategy than with the other three coefficients-based strategies. The results of this study indicate that the combination of a data assimilation algorithm and spatial clustering algorithm can improve the application potential of crop models in agricultural systems.

Optimizing Nitrogen Fertilization to Enhance Productivity and Profitability of Upland Rice Using CSM-CERES-Rice.

Nitrogen (N) deficiency can limit rice productivity, whereas the over- and underapplication of N results in agronomic and economic losses. Process-based crop models are useful tools and could assist in optimizing N management, enhancing the production efficiency and profitability of upland rice production systems. The study evaluated the ability of CSM-CERES-Rice to determine optimal N fertilization rate for different sowing dates of upland rice. Field experimental data from two growing seasons (2018-2019 and 2019-2020) were used to simulate rice responses to four N fertilization rates (N30, N60, N90 and a control-N0) applied under three different sowing windows (SD1, SD2 and SD3). Cultivar coefficients were calibrated with data from N90 under all sowing windows in both seasons and the remaining treatments were used for model validation. Following model validation, simulations were extended up to N240 to identify the sowing date's specific economic optimum N fertilization rate (EONFR). Results indicated that CSM-CERES-Rice performed well both in calibration and validation, in simulating rice performance under different N fertilization rates. The d-index and nRMSE values for grain yield (0.90 and 16%), aboveground dry matter (0.93 and 13%), harvest index (0.86 and 7%), grain N contents (0.95 and 18%), total crop N uptake (0.97 and 15%) and N use efficiencies (0.94-0.97 and 11-15%) during model validation indicated good agreement between simulated and observed data. Extended simulations indicated that upland rice yield was responsive to N fertilization up to 180 kg N ha-1 (N180), where the yield plateau was observed. Fertilization rates of 140, 170 and 130 kg N ha-1 were identified as the EONFR for SD1, SD2 and SD3, respectively, based on the computed profitability, marginal net returns and N utilization. The model results suggested that N fertilization rate should be adjusted for different sowing windows rather than recommending a uniform N rate across sowing windows. In summary, CSM-CERES-Rice can be used as a decision support tool for determining EONFR for seasonal sowing windows to maximize the productivity and profitability of upland rice production.

Evaluation of DSSAT CROPGRO model on growth and yield of pigeonpea cultivars under different fertigation levels

Crop modelling can make it easier for researchers to comprehend and describe experimental results and pinpoint yield disparities. In this competition, the impact of pigeonpea growth and yield under various fertigation levels was examined using the Decision Support Systems for Agrotechnology Transfer 4.6 (DSSAT) and CROPGRO pigeonpea models. Under drip fertigated levels, the cultivars received various nutrient doses. The pigeonpea model developed by DSSAT-CROPGRO successfully simulated measured pigeonpea grain yield. The field trials took place in Coimbatore at the millet breeding facility of the Tamil Nadu Agricultural University. The study ran the GLUE coefficient estimator to estimate the cultivar coefficients until it had a good match between the predicted and observed seed yield. The accuracy of the model was measured by calculating its R-squared, RMSE, NRMSE, and Agreement percentage. According to model simulation and field measurements, drip fertigation at 125% RDF via WSF + Azophosmet and foliar spray of 1% PPFM resulted in the highest seed output of 1875 kg ha-1(V1F5) over both years. The increase in seed yield with drip fertigation at 125% RDF via WSF + Azophosmet and foliar spray of 1% PPFM (V1F5) was 8.0 - 11.0% when compared to drip fertigation at 100% RDF via WSF + Azophosmet and foliar spray of 1% PPFM (V1F4)12.9 - 16.1 % compared to drip fertigation at 100% RDF through WSF; and 68.0 - 74.3 % compared to conventional fertilizer. It was indicated that the DSSAT v.4.6 can be a helpful tool for determining and forecasting pigeonpea growth yield if it is appropriately calibrated. Simulation models substantially facilitated maximizing crop growth and generating management advice.

Modeling climate change impact on dryland wheat production for increased crop yield in the Free State, South Africa, using GCM projections and the DSSAT model

Introduction: The impact of climate change on food production in South Africa is likely to increase due to low rainfall and frequent droughts, resulting in food insecurity in the future. The use of well-calibrated and validated crop models with climate change data is important for assessing climate change impacts and developing adaptation strategies. In this study, the decision support system for agrotechnology transfer (DSSAT) crop model was used to predict yield using observed and projected climate data.Materials and Methods: Climate, soil, and crop management data were collected from wheat-growing study sites in Bethlehem, South Africa. The DSSAT wheat model (CROPSIM-CERES) used was already calibrated, and validated by Serage et al. (Evaluating Climate Change Adaptation Strategies for Disaster Risk Management: Case Study for Bethlehem Wheat Farmers, South Africa, 2017) using three wheat cultivar coefficients obtained from the cultivar adaptation experiment by the ARC-Small Grain Institute. The model was run with historical climate data for the eastern Free State (Bethlehem) from 1999 to 2018 as the baseline period. To determine the effects of climate change, the crop model simulation for wheat was run with future projections from four Global Climate Models (GCM): BCC-CSM1_1, GFDL-ESM2G, ENSEMBLE, and MIROC from 2020 to 2077.Results: The average wheat yield for the historic climate data was 1145.2 kg/ha and was slightly lower than the highest average yield of 1215.9 kg/ha from GCM ENSEMBLE during Representative concentration pathways (RCP) 2.6, while the lowest yield of 29.8 kg/ha was produced during RCP 8.5 (GCM GFDL-ESM2G). Model GFDL-ESM2G produced low yields (29.8–47.74 kg/ha) during RCP 8.5 and RCP 6.0, respectively. The yield range for GCM BCC-CSM1_1 was 770.2 kg/ha during RCP 2.6 to 921.68 kg/ha during RCP 4.5 and 547.84 kg/ha during RCP 8.5 to 700.22 kg/ha during RCP 2.6 for GCM MIROC.Conclusion: This study showed a declining trend in yield for future climate projections from RCP2.6 to RCP8.5, indicating that the possible impacts of higher temperatures and reduced rainfall in the projected future climate will slightly decrease wheat production in the eastern Free State. Adaptation measures to mitigate the potential impact of climate change could include possible changes in planting dates and cultivars. Using a crop model to simulate the response of crops to variations in weather conditions can be useful to generate advisories for farmers to prevent low yield.

Water use and crop coefficients of soybean cultivars of diverse maturity groups and assessment of related water management strategies

Water use and crop coefficients of soybean cultivars of diverse maturity groups and assessment of related water management strategies

Simulating potential yield of rainfed soybean in northeast Nigeria

We used the CROPGRO-Soybean model to simulate the production potential of rainfed soybean in northeast Nigeria. Data from ten soybean experiments conducted under optimal conditions in 2016–2018 at Kano and Dambatta in the Sudan savanna (SS) agroecological zone were used to determine the cultivar coefficients and calibrate the model for the varieties TGX 1448–2E and TGX1951–3 F. The model was evaluated with data from four phosphorous response trials conducted at Zaria and Doguwa in the northern Guinea savanna (GS) of Nigeria between 2016 and 2018. Results show that the CROPGRO-Soybean model was able to accurately simulate soybean growth and grain yield with low RMSE and high d-index values. Consequently, the model was used to investigate the rainfed yield potential of the two varieties in 24 sites in northeast Nigeria under different sowing windows using 30-year (1985–2014) weather data. The result shows that soybean can be grown in northeast Nigeria, but yield performance is dependent on location, variety and sowing window. The simulated yield was higher in the SS than in the GS agro-ecozone despite the longer growing period in the later. Low yield was simulated for TGX 1448–2E for most of the sites. The yield of TGX1951–3 F was above a threshold of 1500 kg ha−1 in 5 out of 12 sites in the GS and 7 out of 12 sites in the SS, suggesting that this variety is the most suitable for cultivation in northeast Nigeria. Sowing TGX 1951–3 F can be delayed to July 16 at Gwaskara, Nasarawo Demsa and Tawa in the GS and at Briyel, Lakundum, Jara Dali, Kurbo Gayi, and Mathau in the SS with a low-risk of crop failure. The desired yield will be achieved at Chikala and Puba Vidau with a significantly low risk of crop failure for all sowing windows. The results from this study suggest that the CSM-CROPGRO-Soybean model can be a valuable tool in determining the right variety and sowing window for soybean production in targeted agroecological zones in northeast Nigeria.

Performance of a model in simulating growth and stability for cassava grown after rice

AbstractSelecting the appropriate genotype for growing cassava (Manihot esculenta Crantz) after rice (Oryza sativa L.) can help increase the supply of cassava and improve land use efficiency. However, conducting the selection requires data from many years of multi‐environment yield trials. A systems analysis approach using crop models can support this process. This study aimed to evaluate a potential application of the Cropping System Model (CSM)‐MANIHOT‐Cassava in determining genotype stability across different upper paddy field conditions when planted following rice. Four cassava genotypes, that is, Kasetsart 50, Rayong 9, Rayong 11, and CMR38‐125‐77, were evaluated for the six different environments in Thailand from the 2015 through 2018 growing seasons. The data required for the model were recorded, including biomass and yield. The cultivar coefficients of the CSM‐MANIHOT‐Cassava model for the four genotypes were calibrated and evaluated with the experimental data. The model was then run for historical weather data from 1988 to 2018 for the six environments for production under rain‐fed conditions in upper paddy fields following rice. The overall results showed that the model was able to simulate biomass and yield of the four cassava genotypes quite well when compared to the experimental data. The model identified the same stable genotypes as presented in the actual trials. The genotype CMR38‐125‐77 was found to be a stable genotype and had the highest mean performance for both the actual yield trials and the simulation study. Therefore, the CSM‐MANIHOT‐Cassava model could be used to help identify the favorable genotype for planting in various paddy field conditions after rice harvest.

Data Analysis Using Representation Theory and Clustering Algorithms

This work aims to expand the knowledge of the area of data analysis through both persistence homology, as well as representations of directed graphs. To be specific, we looked for how we can analyze homology cluster groups using agglomerative Hierarchical Clustering algorithms and methods. Additionally, the Wine data, which is offered in R studio, was analyzed using various cluster algorithms such as Hierarchical Clustering, K-Means Clustering, and PAM Clustering. The goal of the analysis was to find out which cluster's method is proper for a given numerical data set. By testing the data, we tried to find the agglomerative hierarchical clustering method that will be the optimal clustering algorithm among these three; K-Means, PAM, and Random Forest methods. By comparing each model's accuracy value with cultivar coefficients, we came with a conclusion that K-Means methods are the most helpful when working with numerical variables. On the other hand, PAM clustering and Gower with random forest are the most beneficial approaches when working with categorical variables. All these tests can determine the optimal number of clustering groups, given the data set, and by doing the proper analysis. Using those the project, we can apply our method to several industrial areas such that clinical, business, and others. For example, people can make different groups based on each patient who has a common disease, required therapy, and other things in the clinical society. Additionally, for the business area, people can expect to get several clustered groups based on the marginal profit, marginal cost, or other economic indicators.

Cultivar Coefficient Estimator for the Cropping System Model Based on Time-Series Data: A Case Study for Soybean

HighlightsSoftware was developed for estimation of DSSAT CSM-CROPGRO-Soybean cultivar coefficients.Phenology-related coefficients were estimated based on observed phenological events.Growth-related cultivar coefficients were estimated based on time-series observations.Cultivar coefficients were optimized based on single- and multiple-experiment data sets.Abstract. The Decision Support System for Agrotechnology Transfer (DSSAT) is one of the most popular software solutions for predicting crop growth and yield while capturing the effects of management practices and interactions between the crop and the environment. Accurate estimation of the crop cultivar coefficients that govern in-season growth and development is critical for correct yield estimates. The manual cultivar coefficient estimation process is time-consuming and results in user-dependent, subjective optimums that are difficult to reproduce. Typically, end-of-season observations (point-based) are used for estimating dynamic in-season biomass accumulation rates. The objective of this study was to develop a time-series estimator (TSE) capable of using multiple in-season observations for estimating the coefficients that define in-season growth and biomass partitioning. Using the TSE, cultivar coefficients were estimated based on multiple in-season observations of leaf area index (LAI) and shoot, leaf, and grain dry matter weights. The cultivar coefficients were estimated from single- and multiple-treatment (seasons and locations) in-season observations. This was done for two cultivars for six management × environment combinations. Estimated multiple-treatment based cultivar coefficients were evaluated with an independent data set and compared to DSSAT standard (manual) coefficients and the cultivar coefficients estimated with the GLUE method. The average normalized root mean squared error (nRSME) for LAI and shoot, leaf, and grain weights was 26% lower for one cultivar and about the same for the other cultivar when compared to the DSSAT standard. Because GLUE uses end-of-season point-based cultivar coefficient estimation, the grain weight over time was underestimated in earlier phases and more accurate toward harvest. The TSE-estimated cultivar coefficients based on 346 in-season observations across multiple target variables and six experiments more accurately reflected in-season growth and grain weight without compromising final grain weight predictions. Keywords: . CROPGRO-Soybean, DSSAT, Genetic coefficients, Normalized root mean square error minimization, Time-seris observations.

Data analysis using representation theory and clustering algorithms

This work aims to expand the knowledge of the area of data analysis through persistence homology and representations of directed graphs. To be specific, we looked for how we can analyze homology cluster groups using agglomerative Hierarchical Clustering algorithms and methods. Additionally, the Wine data, which is offered in R studio, was analyzed using various cluster algorithms such as Hierarchical Clustering, K-Means Clustering, and PAM Clustering. The goal of the analysis was to find out which cluster's method is proper for a given numerical dataset. We tried to find the agglomerative hierarchical clustering method by testing the data that will be the optimal clustering algorithm among these three; K-Means, PAM, and Random Forest methods. By comparing each model's accuracy value with cultivar coefficients, we concluded that K-Means methods are the most helpful when working with numerical variables. On the other hand, PAM clustering and Gower with Random Forest are the most beneficial approaches when using categorical variables. These tests can determine the optimal number of clustering groups, given the data set, and by doing the proper analysis. Using those the project, we can apply our method to several industrial areas such that clinical, business, and others. For example, people can make different groups based on each patient who has a common disease, required therapy, and other things in the clinical society. Additionally, people can expect to get several clustered groups based on the marginal profit, marginal cost, or other economic indicators for the business area.

Implementation of an automatic time‐series calibration method for the DSSAT wheat models to enhance multi‐model approaches

AbstractMulti‐modeling (MM) approaches allow increasing modeling accuracy through a combination of different modeling structures for the simulation of plant growth and yield. The Decision Support System for Agrotechnology Transfer (DSSAT) 4.7 modeling platform currently includes three different wheat (Triticum aestivum L.) models (CERES, N‐Wheat, and Cropsim). However, the main obstacle for using an MM approach is the calibration procedure. Calibration is time consuming and complex, especially if the user is not familiar with all three models. It results in a subjective calibration optimum and might discriminate models if the user is less trained. To avoid these conflicts, an automated calibration program which optimizes cultivar coefficients based on the root means square error (RMSE) of time‐series data was developed to ensure objective calibration results across three different wheat models and to highlight the potential of MM approaches for decision support in the future. Model calibration was performed on a 4‐yr nitrogen wheat fertilizer trial (0–240 kg ha−1 N) in southwest Germany. The evaluation mean showed satisfying results for the calibration (d‐index = .93) and evaluation dataset (d‐index = .81). By comparing different years, the MM approach improved modeling accuracy in most cases. Especially in the drought season of 2018, the MM approach revealed higher modeling accuracy for yield (d‐index = .61) in contrast to a single simulation of CERES (d‐index = .34) and Cropsim (d‐index = .39). This demonstrated the advantage of an MM approach as different modeling structures could compensate for errors that occur in single modeling approaches.

Modeling Planting-Date Effects on Intermediate-Maturing Maize in Contrasting Environments in the Nigerian Savanna: An Application of DSSAT Model

The Crop Environment Resource Synthesis (CERES)-Maize model in Decision Support System for Agricultural Technology Transfer (DSSAT) was calibrated and evaluated with experimental data for simulation of response of two intermediate-maturing maize varieties to different sowing dates in the Nigerian savannas. The calibration experiments involved 14 consecutive field trials conducted in the rainy and dry seasons in Bayero University Kano (BUK), Dambatta, and Zaria between 2014–2019. Two sets of field experiments were conducted simultaneously for model evaluation in Iburu in the southern Guinea savanna zone and Zaria in the northern Guinea savanna zone during 2015 and 2016 cropping seasons. The experiments for calibration had two maize (SAMMAZ-15 and SAMMAZ-16) varieties planted under optimum conditions with no water and nutrients stresses. The trials for model evaluation were conducted using the same varieties under four different nitrogen (N) rates (0, 60, 120 and 180 kg N ha−1). A 30-year (1985–2014) term simulation was performed to determine effect of varying sowing dates on yields of two maize varieties (SAMMAZ-15 and SAMMAZ-16) in the Sudan savanna (SS), northern Guinea savanna (NGS), and southern Guinea savanna (SGS) zones. The calibration results showed that the cultivar coefficients of the two maize varieties resulted in simulated growth and development parameters that were in good agreement with observed parameters. Model evaluation showed a good agreement between simulated and observed data for phenology and growth of maize. This demonstrated the potential of the CERES-Maize model to simulate growth and yield of maize in the Nigeria savannas. Results of 30-year sensitivity analysis with 9 different sowing windows showed that in SS, sowing the intermediate maize varieties from early to mid-June produced the highest grain yields. In NGS, the optimum sowing windows were found between late June and late July for the both varieties. In SGS, the optimum sowing window is from early June to late July for SAMMAZ-15 and mid-June to late July for SAMMAZ-16. These planting windows gave the highest long-term average yields for each variety. The variety SAMMAZ-15 was found to be best performing across the three agro-ecologies. Maize performance was generally higher in NGS than in SGS. SS in the Sudan savanna recorded the lowest yield compared with other locations.

Evapotranspiration and crop coefficients in two irrigated wheat cultivars

ABSTRACT In the state of Mato Grosso, Brazil, wheat is an alternative to the current production system, mainly in irrigated crops, with new cultivars improved for this environment and system. The objective was to determine the evapotranspiration of the irrigated wheat crop and the dual coefficients of cultivars for the Brazilian Midwest. The experiment was conducted in the field using 12 weighing lysimeters and mini-lysimeters, filled by soil monoliths. The wheat cultivars BRS-254 and BRS-394 were used in the first and second years, respectively. Reference evapotranspiration was obtained by the Penman-Monteith equation and crop evapotranspiration by the mass difference in the lysimeters. Basal crop coefficient and soil water evaporation coefficient were determined according to the crop development stages in both years of cultivation. The mean ETc and ETo values were 3.87, 3.49 mm d-1 and 4.10 and 3.23 mm d-1, respectively, in the first and second cultivation cycles, with higher values of ETc recorded in the reproductive stages of the crop, flowering and grain filling. The basal crop coefficients and soil water evaporation coefficients estimated by weighing lysimeters composed of soil monoliths are: Kcb - 0.42, 0.78, 0.94, 0.74 and 0.47 and Ke - 0.72, 0.52, 0.39, 0.58 and 0.13 for the cultivar BRS-254, and Kcb - 0.28, 0.27, 0.65, 0.98 and 0.66 and Ke - 1.04, 0.96, 0.65, 0.34 and 0.51 for the cultivar BRS-394, at the stages of emergence, tillering, flowering, maturation and harvest, respectively.

Evaluating short-season soybean management adaptations for cover crop rotations with a crop simulation model

Cover crop fall biomass production and thus successful provisioning of ecosystem services depend on the previous cash crop harvest date. We used a process-based eco-physiological model to investigate the potential of short-season soybean maturity groups (MG) to lengthen the cover crop growing window while achieving yields similar to full-season MG cultivars. Cultivar coefficients for MG 0–4 cultivars for the DSSAT – CROPGRO model were calibrated with data from 13 site-years (in 2017 and 2018) across Kentucky, Nebraska, and Ohio. The model was efficient in predicting differences in soybean harvest maturity date (R8; Model efficiency [ME] = 0.61; Root Mean Square Error [RMSE] = 7.4 days) and yield (ME = 0.38; RMSE = 0.452 Mg ha−1) for an independent set of soybean cultivars in the same site-years. Thereafter, a multi-factor sensitivity analysis across 30-yr of historical weather data was conducted. Simulated results showed that MG 3 cultivars would not reduce yield and would advance cover crop establishment compared to MG 4 cultivars. For planting dates in May and conditions of no water stress, adaptating cultivar choices to MG lower than 3 would reduce yields by 55 to 567 kg ha−1 per unit decrease in MG. Under water stress or when planting date was delayed, adapting cultivar choices to MG lower than 3 had a less detrimental effect on yield. Overall, switching to earlier cutlivar maturities would advance soybean harvest by 7–11 days MG−1 (May 15 planting date) or 1–7 days MG-1 (Jul 1 planting date), and lengthen the cover crop growing season in the fall by 95–198 °C day MG−1 (May 15 planting date) or 19−104 °C day MG−1 (Jul 1 planting date). The greater potential to increase the cover crop growing season with short-season MG cultivars was also associated with a greater soybean yield penalty in the warmest locations in our study. Using crop coefficients calibrated by MG rather than by specific cultivar provided a way to increase model application within a study region to study cultivar maturity adaptations for crop rotations while reducing the need for calibration. Further studies that analyze the tradeoffs from soybean cultivar adaptation on C, N, and water balance, and other indirect ecosystem services from cover crops are necessary.

Simulating the effects of human urine on phenology and some cultivar coefficients of Cowpea (Vigna unguicalata. L. Walp) using the DSSAT-CROPGRO model

Simulating the effects of human urine on phenology and some cultivar coefficients of Cowpea (Vigna unguicalata. L. Walp) using the DSSAT-CROPGRO model

Quantifying sugarcane cultivar differences in tiller and stalk phenology: identifying traits suited to crop model-assisted breeding

ABSTRACT The “Canegro” sugarcane (Saccharum spp.) model uses cultivar trait coefficients to simulate crop growth. The primary objective of this study was to quantify the cultivar trait values for some tillering and stalk traits for a diverse range of cultivars. An additional objective was to determine the stability and heritability of these traits across environments and crop stages to assess their potential contribution to model-assisted breeding. A total of 12 diverse cultivars were evaluated in a rainfed and an irrigated environment across two crops (plant cane and first ratoon). The traits peak tiller population (PTP), thermal time to peak tiller population (TTPP), final population (FPOP), tiller survival percentage (TSP) and stalk elongation rate (SER) were derived from in-season measurements. The highly significant (p < 0.001) effect of cultivar was larger than the cultivar × ratoon and cultivar × site effects for all traits studied. The traits PTP, FPOP and SER showed strong cultivar rank correlations between sites, crops, and crops within sites, suggesting that they can be quantified per cultivar by single-site and single-crop experiments. These traits had heritability estimates of 0.70 (PTP), 0.97 (FPOP), and 0.72 (SER). Hence, these traits were identified as good candidates for model-assisted breeding. Some cultivars showed consistent rankings for certain traits and were identified as ideal indicator cultivars for future characterization studies. The cultivar trait values determined here will be used to calibrate the crop model. The range of values for these traits will also contribute to model-wise exploration of genotype × environment interactions and model-assisted breeding efforts.

A comprehensively quantitative method of evaluating the impact of drought on crop yield using daily multi-scale SPEI and crop growth process model.

The quantitative evaluation of the impact of drought on crop yield is one of the most important aspects in agricultural water resource management. To assess the impact of drought on wheat yield, the Environmental Policy Integrated Climate (EPIC) crop growth model and daily Standardized Precipitation Evapotranspiration Index (SPEI), which is based on daily meteorological data, are adopted in the Huang Huai Hai Plain. The winter wheat crop yields are estimated at 28 stations, after calibrating the cultivar coefficients based on the experimental site data, and SPEI data was taken 11 times across the growth season from 1981 to 2010. The relationship between estimated yield and multi-scale SPEI were analyzed. The optimum time scale SPEI to monitor drought during the crop growth period was determined. The reference yield was determined by averaging the yields from numerous non-drought years. From this data, we propose a comprehensive quantitative method which can be used to predict the impact of drought on wheat yields by combining the daily multi-scale SPEI and crop growth process model. This method was tested in the Huang Huai Hai Plain. The results suggested that estimation of calibrated EPIC was a good predictor of crop yield in the Huang Huai Hai Plain, with lower RMSE (15.4%) between estimated yield and observed yield at six agrometeorological stations. The soil moisture at planting time was affected by the precipitation and evapotranspiration during the previous 90days (about 3months) in the Huang Huai Hai Plain. SPEIG90 was adopted as the optimum time scale SPEI to identify the drought and non-drought years, and identified a drought year in 2000. The water deficit in the year 2000 was significant, and the rate of crop yield reduction did not completely correspond with the volume of water deficit. Our proposed comprehensive method which quantitatively evaluates the impact of drought on crop yield is reliable. The results of this study further our understanding why the adoption of counter measures against drought is important and direct farmers to choose drought-resistant crops.

Simulation of genotype-by-environment interactions on irrigated soybean yields in the U.S. Midsouth

Dynamic crop models that incorporate the effect of environmental variables can potentially explain yield differences associated with location, year, planting date, and cultivars with different growing cycles. Soybean (Glycine max (L.) Mer.) cultivar coefficients for the DSSAT-CROPGRO model were calibrated from two growing seasons (2012−2013) comprising 58 irrigated environments (site×year×planting date combinations) for cultivars within maturity groups (MGs) 3 to 6 using end of season data (yield, seed weight, and seed oil and protein concentration) and previously calibrated phenology coefficients. Model accuracy after calibration of cultivar coefficients by MG (cultivars averaged within a MG) was similar compared to cultivar-specific coefficients. During the subsequent growing season in 2014 (33 environments), the model efficiency (ME) for predicting yield was 0.40, with a root mean square error (RMSE) of 571kgha−1. The model was less efficient predicting seed number and seed weight (ME=0.06 and −0.06, respectively) than yield. The model was able to simulate differences in seed oil concentration across environments and MGs (ME=0.52), but not protein concentration (ME=−0.25). The analysis of yield stability had similar slopes for the observed and predicted yield regressions against an observed environmental index (EI) that were only dependent on the MG. Simulated yields were significantly different from the observed when EI>0, but yield differences in the highest yielding environments were still relatively small (245 to 608kgha−1). The results indicate an overall robust model performance in capturing G×E responses with coefficients calibrated by MG.