The APSIM‐Maize and CERES‐Maize models are widely used in impact studies to analyze the effect of climate change on future maize yield. The study objectives were to develop climate scenarios, assess crop model's sensitivity, and predict the impact of climate change on rainfed maize yield based on five global climate models under two RCP (RCP4.5 and RCP8.5) scenarios. The scenarios were based on the mid‐century and tested for sowing dates (SDs), maize cultivars, and nitrogen fertilizer rates (N). For field calibration and validation, the split‐split‐plot experimental design with three replicates was set up at Mount Makulu, Zambia. The treatments were SD, cultivar, and N‐rate were the main plot, subplot, and subsub plot, respectively. The APSIM‐Maize and CERES‐Maize models were used to run simulations using seasonal analysis. The impacts of climate change on maize yield were simulated for the future 2040–2069/1980–2010 using the AgMIP Protocols. The ensemble means from the simulation result in precipitation decrease and temperature increase. Days after planting to anthesis and maturity would reduce in 2050 (2040–2069). The % change in grain yield would range from 2.78% to 9.94%, −3.81% to −8.88%, and −2.33% to 10.63% under N1 (55.2 N kg/ha), N2 (110.4 N kg/ha), and N3 (165.6 N kg/ha) as affected by SDs, respectively. The simulation showed evidence of climate change and hence affect maize growth and yield. Therefore, there is a need to put in place strategies for alleviating the impact of climate change in maize production in Zambia.

A field experiment was conducted during the 2013/2014 season to evaluate the performance of CERES-maize model in simulating the effect of date of planting, nitrogen fertilizer and root-zone soil water profile on growth and yield of maize ( Zea mays L.) at the Field Research Station of the School of Agricultural Sciences, University of Zambia, Zambia (15°23.6859'S, 28°20.226'E; 1,261 m.a.s.l). The experimental design was a split plot with three replicates, three planting dates (November 24, December 8, and December 22) assigned to main plots and two nitrogen fertilizer rates (112 and 168 kg N ha -1 ) assigned to sub-plot. Phenological stages and aboveground biomass were used for model evaluation and these were observed at vegetative and reproductive stages. Soil water profiles were monitored using the Diviner 2000 Probe. Planting date significantly affected grain and biomass yield at P < 0.05. The coefficients of variation for grain and biomass yield were below 12% and considered efficient. The Generalized Likelihood Uncertainty Estimation (GLUE) programme was used to estimate the genetic coefficients for the CERES-maize model. The model’s prediction of plant emergence (±1 days), time to anthesis (>= -3 <= ±1 days) and maturity (>= -4 <= 6 days) was good. Simulation of biomass (RMSE = 1135 kg/ha, d = 0.96, EF = 0.86) was reasonably accurate while leaf area index (d = 0.54, EF = -0.65) was simulated with less accuracy due to poor d-stat and forecasting efficiency. The model’s simulation of grain yield was fair (NRMSE = 21.4%) while soil root water availability demonstrated that substantial potential yield may have been lost due to water stress. The results showed that the model can be used to accurately determine optimum planting date, biomass yield and nitrogen fertilizer rates with reasonable accuracy.

Crop model calibration and validation is vital for establishing their credibility and ability in simulating crop growth and yield. A split–split plot design field experiment was carried out with sowing dates (SD1, SD2 and SD3); maize cultivars (ZMS606, PHB30G19 and PHB30B50) and nitrogen fertilizer rates (N1, N2 and N3) as the main plot, subplot and sub-subplot with three replicates, respectively. The experiment was carried out at Mount Makulu Central Research Station, Chilanga, Zambia in the 2016/2017 season. The study objective was to calibrate and validate APSIM-Maize and DSSAT-CERES-Maize models in simulating phenology, mLAI, soil water content, aboveground biomass and grain yield under rainfed and irrigated conditions. Days after planting to anthesis (APSIM-Maize, anthesis (DAP) RMSE = 1.91 days; DSSAT-CERES-Maize, anthesis (DAP) RMSE = 2.89 days) and maturity (APSIM-Maize, maturity (DAP) RMSE = 3.35 days; DSSAT-CERES-Maize, maturity (DAP) RMSE = 3.13 days) were adequately simulated, with RMSEn being <5%. The grain yield RMSE was 1.38 t ha⁻¹ (APSIM-Maize) and 0.84 t ha⁻¹ (DSSAT-CERES-Maize). The APSIM- and-DSSAT-CERES-Maize models accurately simulated the grain yield, grain number m⁻², soil water content (soil layers 1–8, RMSEn ≤ 20%), biomass and grain yield, with RMSEn ≤ 30% under rainfed condition. Model validation showed acceptable performances under the irrigated condition. The models can be used in identifying management options provided climate and soil physiochemical properties are available.

There is insufficient paucity of information on trends in long-term monthly and decadal rainfall in Zambia. This study assessed the monthly and decadal trends in rainfall over the agro-ecological regions (AERs) and Zambia from 1981 to 2022. The Mann-Kendall test statistic was used at 5 % significant level to compute trends in rainfall at monthly and decadal time step on CHIRPS v2 at 0.05° resolution. R/RStudio Sen's slope estimator was used to give the magnitude of the observed trends. The monthly rainfall time series trend over Zambia ranges from −0.04 to 0.03. The decadal trend analysis of rainfall at annual and monthly time step exhibits a decreasing/increasing trend with Sen's slope between −49.27 and 71.26 mm. Decadal trend at annual time step in AERIII, AERIIa, AERIIa and AERI exhibits a Sen's slope of −44.11 to 62.48 mm, −15.29 to 41.58 mm, −6.08 and 71.26 mm, and 2.20–64.86 mm, respectively. The decadal trend at monthly time step in AERIII, AERIIa, AERIIa and AERI exhibits a Sen's slope of −132.08 to −3.15 mm, −123.39 to −8.57 mm, −73.08 to −15.17 mm, and −80.02 to −5.21 mm, respectively. Decrease in rainfall is expected to affect agriculture, energy, water resources, sanitation and socio-economic aspects. Rainfall pattern shows spatio-temporal variability over Zambia. The results provide valuable input into the National Adaptation Plan and also useful for strategic planning purposes in water resources management under a changing climate. It is evident that spatio-temporal time steps utilized in this study provides new insights of rainfall trends at seasonal, monthly, and annual and decadal time steps.

Human LULCC is the many driver of environmental changes. Accurate and up-to-date current and predicted information on LULCC is important in land use planning and natural resource management; however, in Zambia, detailed information on LULCC is insufficient. Therefore, this study assessed the dynamics of LULC change (2000–2020) and future projections (2020–2030) for Zambia. The ESA CCI land cover maps, which have been developed from Sentinel-2 images were used in this study. This dataset has a grid spatial resolution of 300 m for the 2000, 2010 and 2020. The 31 ESA CCI Classification were reclassified into ten (10) local Classifications using the r.class module in QGIS 2.18.14. The 2000 and 2010 LULC maps were used to simulate the 2020 LULC scenario using Artificial Neural Network (Multi-layer Perception) algorithms in Modules for Land Use Change Evaluation (MOLUSCE) plugin in QGIS 2.18.14. The 2010 and 2020 maps were used to predict the 2030 LULC classes. The reference 2020 and predicted 2020 LULC maps were used to validate the model. Predicted against observed 2020 LULC map, Kappa (loc) statistic was 0.9869. The 2020 LULC patterns was successfully simulated using ANN-MLP with accuracy level of 95%. LULC classes were predicted for 2030 using the 2010–2020 calibration period. The predicted 2030 LULC types shows an increase in built-up (71.44%) and decrease in cropland (0.73%) with reference to 2020 LULC map. Dense forest (0.19%), grassland (0.85%) and bare land (1.37%) will reduce from 2020–2030. However, seasonally flooded, sparse forest, shrub land, wetland and water body will increase marginally. The largest LULC change is from forest into other LULC types. The insights from this study show that ANN-MLP can be used to predict LULCC, and that the generated information can be employed in land use planning and National Adaptation Plans at regional and national scale.

Climate change impacts are dependent on changes in air temperature, rainfall (frequency and amount) and climate indices, which are highly certain. Climate extreme indices are important metrics that are used to communicate the impacts of climate change. The CORDEX African-domain RCM (SMHI-RCA4) run by seven CMIP5 (CCCma-CanESM2, IPSL-IPSL-CM5A-MR, MIROC-MIROC5, MPI-M-MPI-ESM-LR, NCC-NorESM1-M, MOHC-HadGEM2-ES and NOAA-GFDL-GFDL-ESM2M) and two representative concentration pathways (RCP4.5 and RCP8.5) were used in this study. The future climate change is analysed relative to 2020–2050/1970–2000 using a multi-model ensemble projection. Selected climate indices were analysed using a multi-model ensemble of CMIP5 GCMs (GFDL-ESM2G, HadGEM2-ES and IPSL-CM5A-MR). The climate data operators (CDOs) were used in merging and manipulating the modelled (RCM) data and ETCCDI climate indices. The Mann–Kendall was used to compute the trends in time-series data at p < 0.05. Results indicate that temperature will increase in the Orange and Zambezi River Basins. Rainfall shows variability in both river basins. The temperature-based indices (tn90pETCCDI, tnnETCCDI, tnxETCCDI, tx90pETCCDI, txnETCCDI and txxETCCDI) were statistically significant with positive linear trends. The dtrETCCDI and wsdiETCCDI were statistically significant with positive linear trends within the Zambezi River Basin. csdiETCCDI and tn10pETCCDI were statistically significant with negative trends in both basins. The change in rainfall, temperature and climate indices will have implications on agricultural production, provisions of various ecosystem services, human health, water resources, hydrology, water security, water quality and quantity. The climate extreme indices can assist in analysing regional and global extremes in meteorological parameters and assist climate, and crop modellers and policymakers in assessing sectoral impacts.