Simulated Maize Yield Research Articles

Spatial and temporal distribution of optimal maize sowing dates in Nigeria.

Climate change and inter-annual variability cause variation in rainfall commencement and cessation which has consequences for the maize growing season length and thus impact yields. This study therefore sought to determine the spatially explicit optimum maize sowing dates to enable site specific recommendations in Nigeria. Gridded weather and soil data, crop management and cultivar were used to simulate maize yield from 1981-2019 at a scale of 0.5°. A total of 37 potential sowing dates between 1 March and 7 November at an interval of 7 days for each year were evaluated. The optimum sowing date was the date which maximizes yield at harvest, keeping all other management factors constant. The results show that optimum sowing dates significantly vary across the country with northern Nigeria having notably delayed sowing dates compared to southern Nigeria which has earlier planting dates. The long-term optimal sowing dates significantly (p<0.05), shifted between the 1980s (1981-1990), and current (2011-2019), for most of the country. The most optimum planting dates of southern Nigeria shifted to later sowing dates while most optimum sowing dates of central and northern Nigeria shifted to earlier sowing dates. There was more variation in optimum sowing dates in the wetter than the drier agro-ecologies. Changes in climate explain changes in sowing dates in wetter agro-ecologies compared to drier agro-ecologies. The study concludes that the optimum sowing dates derived from this study and the corresponding methodology used to generate them can be used to improve cropping calendars in maize farming in Nigeria.

Exploring management strategies to improve yields and reduce reactive nitrogen emissions in a summer maize‐winter wheat cropping system under long‐term climate variability

AbstractAchieving high stable crop yields and minimal environmental damage is crucial to enhance the sustainability of agriculture in China. Process‐based models are indispensable tools to develop agronomy management practices to achieve sustainable agriculture by simulating crop production and emissions of reactive nitrogen (N), particularly in complex climate scenarios. In this study, a long‐term field experiment with an intensive summer maize‐winter wheat rotation system in north‐central China was simulated using the DeNitrification‐DeComposition (DNDC) model. The DNDC model validation and calibration was done by using two‐year monitoring data of crop yields and nitrous oxide emission fluxes and ammonia volatilization. Moreover, the optimal management practices to promote crop production and reduce the reactive N loss under 22 years of climate variability were explored using the calibrated DNDC model in this region. The results showed that the DNDC model effectively simulated wheat and maize yields, N uptake, ammonia volatilization, and nitrous oxide emissions. Sensitivity analyses demonstrated that the agronomic management practices (N rates and ratio of base to topdressing, planting time, and tillage depth) substantially affected crop yields and reactive N losses under long‐term climate variability. Compared with current farming practices, optimal Nutrient Expert (NE) management achieved an increase in high yields and environmental pollution radiation by altering the rate of N application and ratio of base to topdressing. Moreover, the optimal management strategies developed by the DNDC model, such as adjusting the planting date and tillage depth, further increased the average grain yield by 2.9% and reduced the average reactive N losses by 10.5% compared with the NE management implemented in the annual rotation cropping with a 22‐year simulation. This study suggests that the modeling method facilitates the development of most effective agronomic management practices to promote crop production and alleviate the negative impact on environment.

Enhancing Maize Yield Simulations in Regional China Using Machine Learning and Multi-Data Resources

Improved agricultural production systems, together with increased grain yield, are essential to feed the growing global population in the 21st century. Global gridded crop models (GGCMs) have been extensively used to assess crop production and yield simulation on a large geographical scale. However, GGCMs are less effective when they are used on a finer scale, significantly limiting the precision in capturing the yearly maize yield. To address this issue, we propose a relatively more advanced approach that downsizes GGCMs by combining machine learning and crop modeling to enhance the accuracy of maize yield simulations on a regional scale. In this study, we combined the random forest algorithm with multiple data sources, trained the algorithm on low-resolution maize yield simulations from GGCMs, and applied it to a finer spatial resolution on a regional scale in China. We evaluated the performance of the eight GGCMs by utilizing a total of 1046 county-level maize yield data available over a 30-year period (1980–2010). Our findings reveal that the downscaled models created for maize yield simulations exhibited a remarkable level of accuracy (R2 ≥ 0.9, MAE &lt; 0.5 t/ha, RMSE &lt; 0.75 t/ha). The original GGCMs performed poorly in simulating county-level maize yields in China, and the improved GGCMs in our study captured an additional 17% variability in the county-level maize yields in China. Additionally, by optimizing nitrogen management strategies, we identified an average maize yield gap at the county level in China ranging from 0.47 to 1.82 t/ha, with the south maize region exhibiting the highest yield gap. Our study demonstrates the high effectiveness of machine learning methods for the spatial downscaling of crop models, significantly improving GGCMs’ performance in county-level maize yield simulations.

Assessing the Impact of Organic-Inorganic Nitrogen Application on Maize Yield and Nitrogen Leaching Using the DNDC Model

In order to optimize maize yield and minimize nitrogen leaching, field experiments and DNDC model simulations assessed various organic-inorganic nitrogen application treatments. Trials were conducted in the Hetao irrigation area of Inner Mongolia, comparing six treatments: no nitrogen (CK), solely inorganic nitrogen (U1), and combinations where 25%, 50%, 75%, and 100% of inorganic nitrogen was replaced with organic nitrogen (U3O1, U1O1, U1O3, O1). The DNDC model was calibrated using the U1 treatment data from 2018-2020 and validated with other treatments. Results indicated the model accurately simulated maize yield (NRMSE &lt; 5°%) and nitrate nitrogen leaching (NRMSE &lt; 15%). Simulation outcomes demonstrated that increasing inorganic nitrogen application reduced crop yield and significantly increased nitrate leaching. Conversely, higher proportions of organic nitrogen, along with adjusted irrigation and frequency of inorganic nitrogen fertilization, increased maize yield while reducing nitrate leaching. At a fixed nitrogen application rate of 240 kg hm−2, maize yield initially rose and then declined with increasing organic nitrogen ratios, while nitrate nitrogen leaching decreased consistently. Optimal results were achieved with a 3:2 ratio of organic to inorganic nitrogen, yielding 12578 kg hm−2 with nitrate nitrogen leaching at 15.7 kg NO-3-N hm−2, indicating this ratio as the preferred method for combined nitrogen application in the region.

Modelling adaptation measures to improve maize production and reduce soil N2O emissions under climate change in Northeast China

Optimizing management practices is essential to guarantee crop yield and reduce greenhouse gas emissions for the sustainable agricultural production under climate change. The objectives of this study were to i) calibrate and evaluate the Denitrification-Decomposition (DNDC) model using the measurements of maize yield, nitrogen (N) uptake, soil temperature, soil moisture, soil inorganic N and nitrous oxide (N2O) emissions from a 5-year maize field experiment in Northeast China (NEC) and ii) simulate the impacts of climate change on maize yield and N2O emissions and explore adaptation strategies based on DNDC modelling. For N-fertilized treatments (farmers’ practice, FP-N; current optimized management, OPT-N), the DNDC model overall showed “good” performance in simulating maize yield, aboveground biomass, N uptake and soil inorganic N based on the average statistical values. The model also performed well in simulating soil moisture, soil temperature and N2O emissions. For N-unfertilized treatments, however, the DNDC model overestimated crop yield and N uptake mainly due to the overestimation of crop growth under low-temperature stress conditions, and the underestimation of soil inorganic N resulted from the underestimated N mineralization and N availability from the dissolved inorganic N pools under continuous N deficiency. The DNDC model showed that compared to the baseline scenario, future climate change would improve the maize yield (1.4–8.8%) while increasing the N2O emissions (37.6–83.4%) without adaptation measures in NEC. Compared to OPT-N management, solely adjusting N fertilization rate (200–220 kg N ha−1) would concurrently increase maize yield (5.0–8.7%) and N2O emissions (0.9–18.6%), while the adoption of optimized planting dates (April 27th to May 18th), new cultivars (with thermal degree days ranging from 2550 to 2700 °C), and the implementation of no-tillage and maize-soybean rotation would increase the maize yield by 1.8–8.9% and reduce the N2O emissions by 0.7–26.5% under future climate change scenarios. This study provided insights into the adaptive management strategies for the sustainable maize production under climate change in NEC.

Assessment of Climate Change Impact and Adaptation Strategies for Rainfed Maize (Zea mays L.)

The study was performed to assess the impact of climate change on spatiotemporal changes in rainfed maize yield. Climate projections data of MIROC-ESM-CHEM model from CMIP6 was used for future climatic scenarios in the maize growing areas of Dindigul and Perambalur districts of Tamil Nadu. The DSSAT model was used to simulate maize yield and evaluate adaptation strategies for base period (1991-2020), the mid (2040-2069) and end centuries (2070-2099) under SSP245 and SSP585 scenarios. The simulation finding shows that, in all scenarios maize yield declined in both Dindigul (7 to 9% and 11 to 12%) and Perambalur (6 to 9% and 11 to 13%) during mid and end centuries respectively from the base period (1991-2020). Following the adaptation strategies such as delayed sowing, the yield was increased in both Dindigul (5 to 6% and 4 to 5%) and Perambalur (4 to 5% and 5 to 6%) with respect to normal sowing date. The results of this study would help in developing adaptation strategies for minimizing the adverse effects of the projected climate in maize-growing districts of Tamil Nadu.

Impact of rainfall onset date on crops yield in Ghana

Rainfall onset date (ROD) influences farmer planting decisions, yet there is a dearth of information on the extent to which ROD influences crop yield. This study assesses the effect of ROD on the yield of four crops (Maize, millet, rice, and sorghum) in Ghana. It uses crop yields from the Ministry of Food and Agriculture (MoFA) and the Food and Agriculture Organization (FAO), and employs the Decision Support System for Agro-technology Transfer (DSSAT) crop model to simulate maize yields from 1985 to 2004. The crop model simulations were forced with weather data from the gridded Global Meteorological Forcing Dataset (GMFD). The relationship between crop yields and RODs from three datasets (observed, satellite, and GMFD) are studied. The results of the study show a good correlation between MoFA and FAO crop yield data (with correlation coefficient (r) of 0.97, 0.92, 0.77, and 0.99 for maize, millet, rice, and sorghum, respectively). RODs from satellite observation feature a high correlation with RODs from station observation (r = 0.72), but RODs from GMFD feature weak correlations (r &amp;lt; 0.3) with both observation datasets. The study finds a negative correlation between observed RODs and crop yields (i.e. an early onset corresponds to high yields) but a positive correlation between GMFD RODs and crop yields (i.e. an early onset correspondence to low yields). The DSSAT model reproduces the observed yield pattern, but with substantial biases. The findings of this study can be used to advise small-holder farmers on planting dates and crop variety selection.

Evaluating the use of nitrogen and phosphorous fertilization as crop management options for maize adaptation to climate change in the Nigeria savannas

Poor soil fertility and climate variability are major constraints to maize production in the Nigeria savannas. The application of nitrogen (N) and phosphorus (P) as adaptation strategy may enhance maize yield under climate change. In this study, the already calibrated and validated CERES-maize model in DSSAT was used to simulate the response of maize varieties to N and P in three agroecological zones. Similarly, the model, coupled with data for representative concentration pathways (RCP4.5 and RCP8.5) scenarios, was applied to simulate maize yields for mid-century and end-of-century periods and to estimate the effect of use of N and P as a strategy for maize adaptation to climate change. Results of a 30-year sensitivity analysis showed that the optimum grain yields were obtained with application of 150 kg N + 30 kg P ha–1 to the two varieties in Kano and Zaria. In Abuja, the optimum grain yields were obtained with the application of 150 kg N ha–1 + 30 kg P ha–1 to SAMMAZ–15 and 120 kg N ha–1 + 30 kg P ha–1 to SAMMAZ–16. When P is not applied, the simulation results show that across all N rates, maize yield would decrease by 25%–52% and 32%–52% for the mid- and end-of-century, respectively, under RCP4.5 for both varieties. There would be a greater reduction under RCP8.5, with a decrease of 32%–59% and 52%–69% under mid- and end-of-century scenarios, respectively. When P is applied at 30 kg ha−1, the reduction in yield due to climate change is lower. Under RCP4.5, the yield would decrease by 9%–15% and 11%–21% for the mid- and end-of-century, respectively. There would be a reduction of 12%–21% and 32%–41% for mid-century and end-of-century, respectively, under RCP8.5 scenario. This suggests that the application of optimum P could reduce the impact of yield loss due to climate change.

Modelling maize yield impacts of improved water and fertilizer management in southern Africa using cropping system model coupled to an agro-hydrological model at field and catchment scale

AbstractThis study quantifies the effect of fertilizer and irrigation management on water use efficiency (WUE), crop growth and crop yield in sub-humid to semi-arid conditions of Limpopo Province, South Africa. An approach of coupling a cropping system model (DSSAT) with an agro-hydrological model (SWAT) was developed and applied to simulate crop yield at the field and catchment scale. Simulation results indicated that the application of fertilizer has a greater positive effect on maize yield than irrigation. WUE ranged from 0.10–0.57 kg/m3 (rainfed) to 0.84–1.39 kg/m3 (irrigated) and was positively correlated with fertilizer application rate. The combined application of the variants with deficit irrigation and fertilizer rate (120:60 kg N:P/ha) for maize turned out to be the best option, giving the highest WUE and increasing average yield by up to 5.7 t/ha compared to no fertilization and rainfed cultivation (1.3 t/ha). The simulated results at the catchment scale showed the considerable spatial variability of maize yield across agricultural fields with different soils, slopes and climate conditions. The average annual simulated maize yield across the catchment corresponding to the highest WUE ranged from 4.0 to 7.0 t/ha. The yield gaps ranged from 3.0 to 6.0 t/ha under deficit irrigation combined with 120N:60P kg/ha and ranged from 0.2 to 1.5 t/ha when only applying deficit irrigation but no fertilizer. This information can support regional decision makers to find appropriate interventions that aim at improving crop yield and WUE for catchments/regions.

ESTIMATION OF MAIZE GENETIC COEFFICIENTS FOR THE CERES-MAIZE MODEL USING NON-DESTRUCTIVE METHODS

The Crop Environment Resource Synthesis-Maize (CERES-Maize) mechanistic model, included in the Decision Support System for Agrotechnology Transfer (DSSAT), is a useful and powerful tool that simulates the growth and yield of maize in different environments. The qualitative and quantitative information provided to the CERES-Maize model guarantees reliability in the simulations obtained. However, it requires a lot of information such as soil characteristics, daily climate, crop characteristics and management, as well as six genetic coefficients. This research assessed a non-destructive methodology for estimating the six required parameters (genetic coefficients): P1, P2, P5, G2, G3 and PHINT, based on the maize physiology, measured from the Growing Degree Days (GDD) base 10. An experiment was established at the experimental station of the International Maize and Wheat Improvement Center (CIMMYT) in Tlaltizapan, Morelos, Mexico, where 27 white maize hybrids and 14 yellow maize hybrids were manually sown in an irrigation conservation tillage system. Once the simulations of maize growth and yield were obtained with CERES-Maize model, the genetic coefficients were calibrated using the Generalized Likelihood Uncertainty Estimation (GLUE). After calibration of the six genetic coefficients for all hybrids, an average values of P1, G2 and G3 were within the typical range, while P2 and P5 were greater than the typical range, and PHINT was below typical range. However, the simulation model showed good performance after calibration, according to the average R2 of 0.9809 and 0.9730 between measured and simulated yields for white and yellow hybrids respectively. The coefficients estimated in this study can be used in the CERES-Maize model to simulate maize yields in different regions of the country for the hybrids used here.

A critical analysis of the effect of projected temperature and rainfall for differential sowing of maize cultivars under RCP 4.5 and RCP 6.0 scenarios for Punjab

A simulation study was conducted for two maize cultivars (PMH1 and PMH2) under two Representative Concentration Pathways (RCPs) in four agroclimatic zones of Punjab state of India where climate change depicts a consistent rise in temperature and variability in rainfall. The temperature and rainfall varied from location to location so agroclimatic zone II (Ballowal Saunkhri), zone III (Ludhiana, Amritsar, and Patiala), zone IV (Bathinda), and zone V (Abohar and Faridkot) were selected for the study. The bias-corrected ensemble model data from seventeen global circulation models (GCMs) for RCP 4.5 and RCP 6.0 was used to simulate maize yield for a period of 70 years (2025–2095) using calibrated and validated CERES-Maize model. The simulated yield trend in Punjab under current dates of sowing indicated a strong negative correlation between the yield and the weather parameters under the two scenarios. Agroclimatic zones II, III, and V (Faridkot) observed an increase in temperature by 1 °C over the 70 years’ time period which led to lowering of the maize yield from high yield category (> 5000 kg/ha) to low (< 3000 kg/ha). The cv PMH1 was able to compensate these effects and performed better in agroclimatic zones II, III, and V (Faridkot). In agroclimatic zones IV (Bathinda) and V (Abohar), an increase in temperature by 2 °C is observed, which led to decline of yield categories from medium yield years to low yield years. Though the rainfall in the region was higher for low yield years, but the rainfall amount was insufficient to mitigate the impact of temperature. Under the future stabilization scenarios, amongst the current sowing dates, mid-June was found suitable in agroclimatic zones II and III and in agroclimatic zone V (Faridkot) both early and mid-June sowing dates performed well. Considering the suitable sowing dates under current farming practices, it would be easy to determine the future sowing window for maize cultivars in Punjab.

Usability of monthly ERFS (Extended Range Forecast System) to predict maize yield using DSSAT (Decision Support System for Agro-technology Transfer) model over Erode District of Tamil Nadu

Extended Range of Forecast Service (ERFS) is highly useful for planning of cropping season and midterm correction at the farm level. The medium-range and long-range forecast validation have many studies, whereas ERF has less that needs to be studied. Maize is an important field crop in India after rice and wheat. Therefore, the prediction of maize yield has significant importance. In the present study, ERFS data were validated by correlation analysis using monthly observed rainfall frequency and intensity. This data was imported to DSSAT (Decision Support System for Agro-technology Transfer) to simulate maize yield of Erode district of Tamil Nadu. The model output and actual yield data from Erode were compared. Forecasted monthly total rainfall was correlated at a rate of 0.97r value with that observed. Yield simulation of maize was done using DSSAT by integrating ERFS data and the observed monthly data. Mean per cent deviation among the yields of observed weather and the disaggregated one tended to be -15.7 %. The average deviation between the yields of ERF forecasted weather data and actual yield was very high ( -29.7 % ) for Erode. Mean % deviation between the yields of observed weather and the actual yield was -14.7 %. Downscaled and accurate weather forecasts could be facilitated for yield prediction of crops by DSSAT model. Yield prediction by the model under observed weather was convenient and usable. Model under-predicted the yields when using ERF data. Both model and ERF forecast need to be improved further for higher resolution.

Simulating the Impacts of Climate Change on Maize Yields Using EPIC: A Case Study in the Eastern Cape Province of South Africa

Climate change has been projected to impact negatively on African agricultural systems. However, there is still an insufficient understanding of the possible effects of climate change on crop yields in Africa. In this study, a previously calibrated Environmental Policy Integrated Climate (EPIC) model was used to assess the effects of future climate change on maize (Zea mays L.) yield in the Eastern Cape Province of South Africa. The study aimed to compare maize yields obtained from EPIC simulations using baseline (1980–2010) weather data with maize yields obtained from EPIC using statistically downscaled future climate data sets for two future periods (mid-century (2040–2069) and late century (2070–2099)). We used three general circulation models (GCMs): BCC-CSM1.1, GFDL-ESM2M and MIROC-ES under two Representative Concentration Pathways (RCPs), RCP 4.5 and RCP 8.5, to drive the future maize yield simulations. Simulation results showed that for all three GCMs and for both future periods, a decrease in maize production was projected. Maize yield was projected to decrease by as much as 23.8% for MIROC, RCP 8.5, (2070–2099). The temperature was projected to rise by over 50% in winter under RCP 8.5 for both future periods. For both future scenarios, rainfall was projected to decrease in the summer months while increasing in the winter months. Overall, this study provides preliminary evidence that local farmers and the Eastern Cape government can utilise to develop local climate change adaptation strategies.

Evaluating area-specific adaptation strategies for rainfed maize under future climates of India

This study investigates the spatio-temporal changes in maize yield under projected climate and identified the potential adaptation measures to reduce the negative impact. Future climate data derived from 30 general circulation models were used to assess the impact of future climate on yield in 16 major maize growing districts of India. DSSAT model was used to simulate maize yield and evaluate adaptation strategies during mid (2040-69) and end-centuries (2070-99) under RCP 4.5 and 8.5. Genetic coefficients were calibrated and validated for each of the study locations. The projected climate indicated a substantial increase in mean seasonal maximum (0.9–6.0 °C) and minimum temperatures (1.1–6.1 °C) in the future (the range denotes the lowest and highest change during all the four future scenarios). Without adaptation strategies, climate change could reduce maize yield in the range of 16% (Tumkur) to 46% (Jalandhar) under RCP 4.5 and 21% (Tumkur) to 80% (Jalandhar) under RCP 8.5. Only at Dharwad, the yield could remain slightly higher or the same compared to the baseline period (1980–2009). Six adaptation strategies were evaluated (delayed sowing, increase in fertilizer dose, supplemental irrigation, and their combinations) in which a combination of those was found to be effective in majority of the districts. District-specific adaptation strategies were identified for each of the future scenarios. The findings of this study will enable in planning adaptation strategies to minimize the negative impact of projected climate in major maize growing districts of India.

Uncertainty in climate change impact studies for irrigated maize cropping systems in southern Spain

This study investigates the main drivers of uncertainties in simulated irrigated maize yield under historical conditions as well as scenarios of increased temperatures and altered irrigation water availability. Using APSIM, MONICA, and SIMPLACE crop models, we quantified the relative contributions of three irrigation water allocation strategies, three sowing dates, and three maize cultivars to the uncertainty in simulated yields. The water allocation strategies were derived from historical records of farmer’s allocation patterns in drip-irrigation scheme of the Genil-Cabra region, Spain (2014–2017). By considering combinations of allocation strategies, the adjusted R2 values (showing the degree of agreement between simulated and observed yields) increased by 29% compared to unrealistic assumptions of considering only near optimal or deficit irrigation scheduling. The factor decomposition analysis based on historic climate showed that irrigation strategies was the main driver of uncertainty in simulated yields (66%). However, under temperature increase scenarios, the contribution of crop model and cultivar choice to uncertainty in simulated yields were as important as irrigation strategy. This was partially due to different model structure in processes related to the temperature responses. Our study calls for including information on irrigation strategies conducted by farmers to reduce the uncertainty in simulated yields at field scale.

Improving crop modeling to better simulate maize yield variability under different irrigation managements

Crop models have been used for investigating crop responses to environmental stresses for decades. The study objectives were to (i) calibrate and validate a simple crop model (Hybrid-Maize) using in-situ measured data from sixteen uniquely managed treatments as part of the University of Nebraska-Lincoln Testing Ag Performance Solutions (TAPS) program in North Platte, NE, and (ii) carry out sensitivity analysis and parameter estimation using a multi-parameter optimization approach. Sixteen Arable Mark 1 sensors and thirty two Mark 2 sensors (Arable Labs Inc., San Francisco, CA) collecting hourly and daily weather and crop information in 2019 and 2020 respectively, were installed in the TAPS subsurface drip irrigation experimental plots which were planted with maize and subjected to different irrigation and nitrogen practices. Hybrid-Maize was used for simulating maize yield on the sixteen treatments in 2019 and 2020. Sensitivity analysis showed that initial light use efficiency (LUE), potential kernel filling rate (G5), potential number of kernels per ear (G2), growth respiration coefficient of grain (GRG), empirical parameter determining the relative contribution of a soil layer to water uptake (SLW), and maximum photosynthetic rate (MPR) were the most sensitive parameters to yield. A novel multi-parameter optimization (MPO) approach based on kriging was used for calibrating these six parameters, and the best parameter sets which were later used for model validation. Calibration results showed that there seemed to be strong linear relationships between total water received (WR) and some of the parameters. By using each year’s MPO averages of the six parameters instead of default values, ME, MAE, RMSE, uRMSE, and nRMSE were all reduced by about 69%, 66%, 60%, 27%, and 61% respectively for validation treatments. The advantage of using in-situ sensors, coupled with the suitability of the calibrated model for simulating maize yield under different irrigation management, will make the model more useful in future field-scale research with focus on developing decision support tools for in-season crop management and yield forecasts.

Modelling the impact of climate change on maize yield in Victoria Nile Sub-basin, Uganda

Agriculture is the backbone of Uganda’s economy, with about 24.9% contribution to the gross domestic product (GDP) as per the Uganda National Household Survey 2016/17. Agricultural productivity (yield per hectare) is still low due to the high dependence on rain-fed subsistence farming. Climate change is expected to further reduce the yield per hectare. Therefore, this study aims to evaluate the potential impact of climate change on maize yield in the Victoria Nile Sub-basin using the AquaCrop model. It further assesses the possible adaptation measures to climate change. The Hadley Centre Global Environmental Model version 2–Earth System (HadGEM2-ES) data downloaded from the Coordinated Regional Downscaling Experiment (CORDEX) was used to simulate maize yield in the near future (2021–2040), mid future (2041–2070) and late future (2071–2099). Results show that maize yield is likely to reduce by as high as 1–10%, 2–42% and 1–39% in the near, mid and late futures, respectively, depending on the agro-ecological zone. This decline in maize yield can have a significant impact on regional food security as well as socio-economic well-being since maize is a staple crop. The study also shows that improving soil fertility has no significant impact on maize yield under climate change. However, a combined application of supplementary irrigation and shifting the planting dates is a promising strategy to maintain food security and socio-economic development. This study presents important findings and adaptation strategies that policymakers and other stakeholders such as farmers can implement to abate the effects of climate change on crop production.

Basin-wide productivity and livelihood analysis of flood-based agricultural systems in African drylands: A case study in the Fogera floodplain

Flood-based Agricultural Systems (FAS), which rely on temporary floods, provide livelihoods for nearly 50 million smallholder farmers across water-stressed African basins. This paper analyses the impact of externally driven agricultural production and productivity improvement interventions in FAS, taking the Ethiopian Fogera floodplain as a case study. Ostroms’ Governing the Commons Principles were used as analytical framework. Field data was gathered through focus group discussions and interviews with 266 farmers and pastoralists, and 10 local administration staff, while the AquaCrop model was used to simulate maize yield under varied floodwater management and farming practices. The interventions in the Fogera floodplain replaced vital maize and teff food crops with high-return upstream rice cultivation. It has characteristics similar to other interventions across Africa: 1) inadequate integration of local agricultural water management practices; 2) narrow focus on short-term economic gains and insufficient attention to long-term sustainability of livelihoods and environmental issues; and 3) lack of detailed ex-ante analysis of basin-wide consequences – it failed to prioritize the needs of downstream vegetable producers and pastoralists using shallow wells. The intervention missed several low-cost opportunities, including the establishment of rules to protect downstream water rights; the construction of gabion-strengthened on-farm structures to efficiently distribute floods or raised brick-walls to reinforce shallow wells; and the implementation of measures for improved soil fertility and weed management. The study establishes that these missed opportunities could have enhanced livelihoods by doubling rice yield to 6 tons/ha; increased teff and maize harvests by one-third, to 3 and 5 tons/ha respectively; and mitigated 25% vegetable yield loss and 40% reduction in grazing land. These opportunities could have also produced environmental benefits, including reduced soil moisture and fertility depletion. Lessons from the Fogera floodplain on making interventions cost-effective and considering basin-wide livelihood impacts are relevant to FAS globally.

Simulating maize yield at enhanced level of temperature using CERES maize model

Simulating maize yield at enhanced level of temperature using CERES maize model

Vulnerability Assessment of Maize Yield Affected by Precipitation Fluctuations: A Northeastern United States Case Study

Crop yields are threatened by global climate change. Maize has high water requirements, and precipitation fluctuations can impact its yield. In this study, we used the Environmental Policy Integrated Climate (EPIC) model to simulate maize yields in eight northeastern U.S. states. We used precipitation fluctuations and the coefficient of variation (CV) of yield as indicators to construct a vulnerability curve for the CV of yield and precipitation fluctuations. We then evaluated the vulnerability of maize yields under precipitation fluctuations in the region. We obtained the following results: (1) the fitted vulnerability curves were classified into three categories (positive slope, negative slope, and insignificant fit), of which the first category accounted for about 92.7%, indicating that the CV of maize yield was positively correlated with precipitation fluctuations in most parts of the study area; and (2) the CV of maize yield under 11 precipitation fluctuation scenarios was mapped to express the CV at the spatial level, and the maize yield in Connecticut and Maryland proved to be the most sensitive to precipitation fluctuations. This study provided a theoretical and experimental basis for the prevention of maize yield risk under fluctuating precipitation conditions.