) 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.

The Long Ashton Research Station Weather Generator (LARS-WG) is a stochastic weather generator used for the simulation of weather data at a single site under both current and future climate conditions using General Circulation Models (GCM). It was calibrated using the baseline and evaluated to determine its suitability in generating synthetic weather data for 2020 and 2055 according to the projections of HadCM3 and BCCR-BCM2 GCMs under SRB1 and SRA1B scenarios at Mount Makulu (Latitude: 15.550˚S, Longitude: 28.250˚E, Elevation: 1213 meter), Zambia. Three weather parameters-precipitation, minimum and maximum temperature were simulated using LARS-WG v5.5 for observed station and AgMERRA reanalysis data for Mount Makulu. Monthly means and variances of observed and generated daily precipitation, maximum temperature and minimum temperature were used to evaluate the suitability of LARS-WG. Other climatic conditions such as wet and dry spells, seasonal frost and heat spells distributions were also used to assess the performance of the model. The results showed that these variables were modeled with good accuracy and LARS-WG could be used with high confidence to reproduce the current and future climate scenarios. Mount Makulu did not experience any seasonal frost. The average temperatures for the baseline (Observed station

Review of literature related to the impact of climate change on maize (Zea mays L.) yield using Global Climate Models (GCMs), statistical downscaling, and crop simulation (APSIM-maize-and-CERES-maize models) models are discussed. GCMs can simulate the current and future climatic scenarios. Crop yield projections using crop models require climate inputs at higher spatial resolution than that provided by GCMs. The computationally inexpensive statistical downscaling technique is widely used for this translation. Studies on regional climate modeling have mostly focused on Southern Africa and West Africa, with very few studies in Zambia. Additionally, the integrated use of climate and crop models have received relatively less attention in Africa compared to other parts of the world. Conversely, the AgMIP protocols have been implemented in Sub-Saharan Africa (SSA) (Ethiopia, Kenya, Tanzania, Uganda and South Africa) and South Asia (SA) (Sri Lanka). In Zambia, however, the protocols have not been applied at either regional or local scale. Applying crop and statistical downscaling models requires calibration and validation, and these are crucial for correct climate and crop simulation. The review shows that although uncertainties exist in the design of models, and parameters, soil, climate and management options, the climate would adversely affect maize yield production in SSA. The potential effect of climate change on maize production can be studied using crop models such as agricultural production simulator (APSIM) and decision support system for agrotechnology (DSSAT) models. There is need to use integrated assessment modeling to study future climate impact on maize yield. The assessment is essential for long-term planning in food security and in developing adaptation and mitigation strategies in the face of climate variability and change.

It is grown for human consumption, livestock feed, and industrial raw materials (Lukeba et al., 2013). Maize yields have increased over the last decades due to an increase in nitrogen (N) fertilizer use, improvement in crop management, and enhanced stress tolerance in maize cultivars (Yakoub, Lloveras,

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Maize production in low-yielding regions is influenced by climate variability, poor soil fertility, suboptimal agronomic practices, and biotic influences, among other limitations. Therefore, the assessment of yields to various management practices is, among others, critical for advancing site-specific measures for production enhancement. In this study, we conducted a multiseason calibration and evaluation of the DSSAT–CERES-Maize model to assess the maize yield response of two common cultivars grown in Trans Nzoia County in Kenya under various agricultural strategies, such as sowing dates, nitrogen fertilization, and water management. We then applied the Mann–Kendall (MK), and Sen’s Slope Estimator (SSE) tests to establish the yield trends and magnitudes of the different strategies. The evaluated model simulated long-term yields (1984–2021) and characterized production under various weather regimes. The model performed well in simulating the growth and development of the two cultivars, as indicated by the model evaluation results. The RMSE for yield was 333 and 239 kg ha−1 for H614 and KH600-23A, respectively, representing a relative error (RRMSE) of 8.1 and 5.1%. The management strategies assessment demonstrated significant feedback on sowing dates, nitrogen fertilization, and cultivars on maize yield. The sowing date conducted in mid-February under fertilization of 100 kg of nitrogen per hectare proved to be the best strategy for enhancing grain yields in the region. Under the optimum sowing dates and fertilization rate, the average yield for cultivar KH600-23A was 7.1% higher than that for H614. The MK and SSE tests revealed a significant (p < 0.05) modest downwards trend in the yield of the H614 cultivar compared to the KH600-23A. The eastern part of Trans Nzoia County demonstrated a consistent downwards trend for the vital yield enhancement strategies. Medium to high nitrogen levels revealed positive yield trends for more extensive coverage of the study area. Based on the results, we recommend the adoption of the KH600-23A cultivar which showed stability in yields under optimum nitrogen levels. Furthermore, we recommend measures that improve soil quality and structure in the western and northern parts, given the negative model response on maize yield in these areas. Knowledge of yield enhancement strategies and their spatial responses is of utmost importance for precision agricultural initiatives and optimization of maize production in Trans Nzoia County.

Although Global Climate Models (GCMs) are regarded as the best tools available for future climate projections, there are biases in simulating precipitation and temperature due to their coarse spatial resolution and cannot be used directly to assess the impact of projected climate change. The study objective was to investigate how bias correction methods impact the modelled future climate change under Representative Concentration Pathway 8.5 (RCP8.5) for 2020-2050.Reanalysisdata (1980-2000) and bias correction approaches (change factor [CF], nudging and Quantile Mapping [QM]) were used to calibrate GCMs [GFDL-ESM2M, MIROC-MIROC5, MPI-ESM-MR, and NCAR-CCSM4] data under RCP8.5 scenarios (2020-2050) for Mount Makulu, Zambia (latitude: 15.550° S, longitude: 28.250° E, altitude: 1200 m). Bias correcti on methods enable the comparison of observed Original Research Article

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.