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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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.

Abstract. The suitability of the University of Zambia farm for selected crops was assessed using the principles and concepts of the FAO Framework for Land Evaluation. Moisture and nutrient availability were found to be the most limiting land qualities, but moisture availability is a more important consideration because the farm is managed with high inputs. Heavy dependence on rainfall makes the farm very vulnerable to drought which has had a devastating impact on yields in recent years. Soyabean, potato and wheat are the best‐suited crops. Sunflower is not recommended because of the low market value. Maize is not a suitable crop because of its sensitivity to water stress and nutrient availability. Rhodes grass is recommended in mapping units where other crops have little economic value. The FAO Framework for Land Evaluation can be used in Zambia. It can identify land utilization types that are physically and economically suitable. Based on these findings an appropriate land use plan of the University Farm has been developed and implemented.

The impact of climate change on crop growth and yield can be predicted using crop simulation models. A study was conducted to assess the reliability and uncertainty of simulated maize yield for the near future in 2050s at Mount Makulu (latitude = 15.550o S, longitude = 28.250o E, altitude = 1213 m), Zambia. The Long Ashton Research Station Weather Generator (LARS-WG) was used to generate baseline (1980-2010) and future (2040-2069) climate scenarios for two Representative Concentration Pathways (RCP 4.5 and RCP 8.5). Results showed that mean temperature would increase by 2.09oC (RCP 4.5) and 2.56oC (RCP 8.5) relative to the baseline (1980-2010). However, rainfall would reduce by 9.84% (RCP 4.5) and 11.82% (RCP 8.5). The CERES-Maize model simulated results for rainfed maize growth showed that the simulated parameters; days after planting (DAP), biomass and grain yield would reduce from 2040-2069/1980-2010 under both RCP4.5 and RCP8.5 scenarios. The LARS-WG was successfully for our location can be used in generating climate scenarios for impact studies to inform policy, stakeholders and decision makers. Adaptation strategies to mitigate for the potential impact of climate change includes several sowing dates, cultivar selection that are efficient at using nitrogen fertilizer and planting new cultivars breeds that will thrive under low root soil water content and higher temperatures.