Nitrogen (N) is the most significant nutrient affecting crop growth and development for all types of crops, except legumes. The goal of this study was to optimize the N level for cotton grown in a semi-arid environment to enhance growth and development, determine N status, and increase seed cotton yield and biomass. Two independent field experiments each three years in duration were conducted, from 2007 to 2009 (Exp.-I) and 2018 to 2020 (Exp.-II). Experiments were laid out in a randomized complete block design with three replicates. The N treatments in Exp.-I were comprised of 0, 40, 80, 120, 160, 200 and 240 kg N/ha, while treatments in Exp.-II were comprised of 0, 70, 140, 210 and 280 kg N/ha. A wide range of data sets for cotton traits were recorded, including canopy height, leaf area index, the N status of the leaf and stem, seed cotton yield and time-series biomass data. The higher N rates 240 and 280 kg N/ha performed better for all these traits. However, the highest leaf N contents were recorded for 210 kg N/ha. Based on these results, it is suggested that under semi-arid conditions, slightly higher rates than optimum or recommended N rates could be applied as a strategy by cotton growers for a higher seed cotton yield. The findings of this study may also increase profitability in other cotton-growing areas that have similar weather conditions.

New information on the growth analysis for different cassava genotypes grown under different irrigation managements during the early growth phase could support decision-making to improve crop productivity. This study aimed to determine the performance of 20 cassava genotypes grown under different irrigation management practices during the early growth phase. A strip-plot design with four replications was used during two growing seasons (2019/2020 and 2020/2021). Three levels of irrigation from 30 to 180 days after planting (DAP) were assigned as factor A (W1 = 100%, W2 = 60% and W3 = 20% of the crop water requirement), whereas 20 cassava genotypes were assigned as factor B. Crop data were recorded on SPAD chlorophyll meter reading (SCMR), specific leaf area (SLA) and leaf area index (LAI) at 180 and 330 DAP, relative growth rate (RGR) from 180 to 330 DAP and harvest index (HI) at 330 DAP. The genotypes CMR36-31-381 and Huay Bong 90 had high values of HI, indicating a good partitioning of photosynthate to the storage root. These two genotypes also showed superior performances in terms of SCMR, SLA and RGR inW2 and W3 treatments when compared to the other genotypes. They also had LAI values within the optimum range during the period of maximum canopy size. Therefore, the genotypes CMR36-31-381 and Huay Bong 90 could be used as genetic resources to improve cassava productivity under different irrigation levels.

Understanding the reasons for the yield gap between potential and actual yield can provide insights for enhancing canola production by adapting measures for ensuring food security. The canola yield gap under different management practices (e.g. water, nitrogen, N- and sowing dates) was quantified using research trials that were conducted at on-station and historical data (1980–2016) and the CROPGRO-Canola model for Punjab, Pakistan. The integrated approach revealed that low inputs of N, the amount of irrigation, sowing date and the use of seeds from home stocks were the principal causes for a low yield. The CROPGRO-Canola model was able to simulate the canola yield from research trials (R2 = >0.90) and farm survey data (R2 = 0.63). The average yield gap between potential (YP), N-limited (YNL), water-limited (YWL), N- and water-limited (YNWL), and overall farmer field yield (YOFF) was 50, 46, 62 and 72%, respectively. The yield-gap with achievable yield (YA) for YNL, YWL, YNWL and YOFF was 34, 28, 49 and 63%, respectively. Overall, the results showed that a high canola yield for farmers’ fields can be obtained by selecting appropriate varieties and sowing dates with N rate of 120 kg/ha and efficient irrigation management. However, further studies are necessary to fully comprehend the underlying causes for the low actual yield and the high yield variability of farmers’ fields.

During the past decade, the interest in using crop models for research, education, extension, outreach and in the private sector has rapidly increased. The iCROPM 2020 Symposium entitled ‘Crop Modeling for the Future’, held in February 2020, therefore, provided a great opportunity for over 400 scientists from 50 different countries to exchange information on crop model development, evaluation with experimental data and implementation. A key outcome was the understanding that crop models simulate the dynamics of the soil-plant-atmosphere continuum. Thus, the models can be used for a quantitative and multi-criteria assessment of cropping system functioning, agronomic performance and environmental impact. This should result in more practical applications that can provide actionable information for stakeholders.

The impact of climate change on agricultural productivity is difficult to assess. However, determining the possible effects of climate change is an absolute necessity for planning by decision-makers. The aim of the study was the evaluation of the CSM-CROPGRO-Sunflower model of DSSAT4.7 and the assessment of impact of climate change on sunflower yield under future climate projections. For this purpose, a 2-year sunflower field experiment was conducted under semi-arid conditions in the Konya province of Turkey. Rainfed and irrigated treatments were used for model analysis. For the assessment of impact of climate change, three global climate models and two representative concentration pathways, i.e. 4.5 and 8.5 were selected. The evaluation of the model showed that the model was able to simulate yield reasonably well, with normalized root mean square error of 1.3% for the irrigated treatment and 17.7% for the rainfed treatment, a d-index of 0.98 and a modelling efficiency of 0.93 for the overall model performance. For the climate change scenarios, the model predicted that yield will decrease in a range of 2.9–39.6% under rainfed conditions and will increase in a range of 7.4–38.5% under irrigated conditions. Results suggest that temperature increases due to climate change will cause a shortening of plant growth cycles. Projection results also confirmed that increasing temperatures due to climate change will cause an increase in sunflower water requirements in the future. Thus, the results reveal the necessity to apply adequate water management strategies for adaptation to climate change for sunflower production.

Fragrant rice is an important export commodity of Thailand and obtaining seasonal production estimates well in advance is important for marketing and stock management. Rice4cast is a software platform that has been developed to forecast rice yield several months prior to harvesting; it links a rice model with a Minimum Data Set (MDS) and Weather Research Forecast (WRF) data. The current study aimed to parameterize and evaluate the model and to demonstrate the use of the Rice4cast platform in forecasting seasonal KDML 105 rice yield and production with local data set. The study area encompassed 77 districts in Thailand, covering 0.94 of the total area of KDML 105 in the country. Minimum Data Sets for the 2013–2015 growing seasons were used for model parameterization and evaluation. The annual statistics from the Office of Agricultural Economics (OAE) were used as a reference basis and planted areas from the Geo-Informatics and Space Technology Development Agency (GISTDA) was used for production estimation. Model evaluation showed good to fairly good agreement between the predicted and reported OAE yield. Production forecasts, however, over-estimated the OAE values considerably, primarily because of the use of GISTDA planted areas that were larger than the harvested areas in the production estimates. Adjustment of the planted areas to account for damaged areas need to be explored further. Nevertheless, the results demonstrated the capability of yield predictions with the Rice4cast, making it a valuable tool for in-season estimates for fragrant rice yield and production.

Prior to applying the cropping system model-CERES-Rice model to deep water rice (DWR), it is important to estimate the rice genetic coefficients (GC). The goal of the current study was to compare two methods for estimating GC using a GC calculator (GENCALC) and generalized likelihood uncertainty estimation (GLUE) for three flooded rice (FDR) varieties. Data from a field experiment on the effect of planting date and variety on FDR production was conducted in 2009 on a DWR area in Bang Taen His Majesty's Private Development Project, Prachin Buri, Thailand. The experimental design was split-plot with four main plots (planting dates) and three sub-plots (FDR varieties) with four replications. The simulated values for anthesis date, maturity date and grain weight using GENCALC produced normalized root mean square errors (RMSEn) of 3.97, 3.69 and 3.68, while using GLUE produced RMSEn of 3.67, 2.50 and 3.68, respectively. The simulated grain number and grain yield under GENCALC GC were not significantly different from the observed values but were higher than simulated values for GLUE GC. Simulated values of above-ground biomass for both GENCALC (11 727 kg/ha) and GLUE GC (11 544 kg/ha) were overestimated compared to observed values (8512 kg/ha). In addition, good agreements of leaf N values were found with D-index values of 0.94 and 0.96 using GENACALC and GLUE GC simulations, respectively. Therefore, the GENCALC and GLUE GC estimators of DSSAT can both be used for estimating GC of FDR in the DWR area in Thailand and similar agro-ecosystems in Southeast Asia.

Three index-based crop insurance contracts are evaluated for representative south Georgia corn farms. The insurance contracts considered are based on indexes of historical county yields, yields predicted from a cooling degree-day production model, and yields predicted from a crop-simulation model. For some of the representative farms, the predicted yield index contracts provide yield risk protection comparable to the contract based on historical county yields, especially at lower levels of risk aversion. The impact of constraints on index insurance choice variables is considered and important interactions among constrained, conditionally optimized, choice variables are analyzed.

An agronomic crop growth model—the Decision Support System for Agro-Technology Transfer—and a constant relative risk aversion utility function are used to examine corn irrigation strategies in Mitchell County, Georgia. Precipitation contracts are designed to help farmers manage risk. Three conclusions originate from the findings. First, the optimal irrigation strategy can greatly increase producers' certainty-equivalent revenue. Second, changes in water pricing policy would have a limited impact on the amount of water used. And third, across levels of risk preference, the precipitation contracts are not effective in increasing certainty-equivalent revenue or reducing cumulative water use.