No abstract

No abstract

No abstract

This paper presents a procedure in which the water-driven water productivity model AquaCrop was fine-tuned and validated for maize for the local conditions in Zimbabwe and then applied to develop sowing management options for decision support. Data from experiments of 2 seasons in Harare and from 5 other sites around Zimbabwe were used for the local calibration and validation of AquaCrop. Model parameters such as the reference harvest index (HI o ); the canopy growth coefficient (CGC); early canopy decline and normalised biomass water productivity (WP b * ) were adjusted during model calibration. Model performance was satisfactory after calibration with a Nash-Sutcliffe model efficiency parameter (EF = 0.81), RMSE = 15% and R 2 = 0.86 upon validation. To develop sowing guidelines, historical climate series from 13 meteorological stations around Zimbabwe were used to simulate maize yield for 6 consecutive sowing dates determined according to criteria applicable in Zimbabwe. Three varieties and typical shallow and deep soil types were considered in the simulation scenarios. The simulated yield was analysed by an optimisation procedure to select the optimum sowing time that maximised long-term mean yield. Results showed that highest yields depended on the climate of the site (rainfall availability), variety (length of growing cycle) and soil depth (soil water storage capacity). The late variety gave higher mean yields for all sowing dates in the maize belt. Staggered sowing is recommended as a way of combating the effects of rainfall variability and as an answer to labour constraints.

Water scarcity and unreliable weather conditions frequently cause smallholder farmers in Zimbabwe to plant maize ( Zea mays L.) varieties outside the optimum planting timeframe. This challenge exacts the necessity to develop sowing management options for decision support. The study's objective was to use a hybrid approach to determine the best planting windows and maize varieties. The combination will guide farmers on planting dates, dry spell probability during critical stages of the crop growth cycle and rainfall cessation. To capture farmer's perception on agroclimatic information, a systematic random sampling of 438 smallholders was carried out. An analysis of climatic data during 1949–2012 was conducted using INSTAT to identify the best planting criterion. The best combination of planting criterion and maize varieties analysis was then achieved by optimizing planting dates and maize varieties in the DSSAT environment. It was found that 56.2% of farmers grew short-season varieties, 40.2% medium-season varieties and 3.6% long-season varieties. It was also established that the number of rain days and maize yield had a strong positive relationship (p = 0.0049). No significant association was found amongst maize yield (p > 0.05), and planting date criteria, Depth (40mm in 4 days), the AREX criterion- Agricultural Research Extension (25 mm rainfall in 7 days) and the MET Criterion-Department of Meteorological Services (40 mm in 15 days). Highest yields were simulated under the combination of medium-season maize variety and the AREX and MET criteria. The range of simulated yields from 0.0 t/ha to 2.8 t/ha formed the basis for the development of an operational decision support tool (cropping calendar) with (RMSE) (0.20). The methodology can be used to select the best suitable maize varieties and a range of planting time.

The study parameterised and validated performance of AquaCrop model, to simulate attainable yields for chickpea crop in response to the effects of planting date in the Northestern Region of South Africa. Model calibration data were obtained from two field experiments of contrasting water regime, planted in 2014 winter season at University of Venda, South Africa, whilst data for model validation was obtained from 2015 planting season from the same station. The model performance was satisfactory, with a good combination between the simulated and observed canopy cover (CC), Soil water content (SWC), biomass (B) and grain yield (Y). All the statistical indicators (R 2 , RMSE, and MAPE) used to compare field observed and model-predicted parameters, showed good performance. For example, the regression analysis of simulated and observed yield showed a good relationship with R 2 values of 0.949, 0.928 and 0.990 for early, normal and late planting dates, respectively, whilst RMSE was 0.135, 0.143 and 0.031 for early, normal and late planting dates. Similarly, SWC had strong regression relationship (R 2 = 0.982, 0.949, 0.954, respectively, and a RMSE of 0.226, 0.696 and 0.310, respectively, in early, normal and late planting date). The results indicate that the model could be used for evaluating the effects of different planting dates on chickpea yield.

Climate change encompassing mostly hydro-meteorological hazards is a reality affecting the world in diverse ways. It is manifesting in various ways such as increases in frequency and intensity of floods, droughts, and extreme temperatures. In recent years, climate change has induced droughts, other extreme weather events and meteorological disasters in many countries including Zimbabwe. Effective management of climate change induced challenges require localized strategies which may vary from one part of the world to another and even within a country. In view of the need to understand localized impacts and responses to climate change, the main objectives of the study were to (i) assess the impact of climate change on livelihoods and food security, (ii) identify and evaluate adaptation and mitigation strategies that small holder farmers in Ward 17, Nyanga, Zimbabwe have developed. The research used both qualitative and quantitative approaches with data collection methods comprising of questionnaires (56), observations and interviews (8). The tools were used to gather information which included encounters with extreme weather events, climatic trends as well as adaptive responses. The findings showed that climate change had a significant negative impact on the livelihoods and food security status of small holder farmers in ward 17 of Nyanga district. The identified climate change adaptation strategies implemented in the study area included food aid, use of traditional grains and other drought resistant crops, early planting, multiple planting, barter trade and livelihood diversification. The mitigation strategies used included afforestation and reforestation programs, avoiding veld fires and preservation of wetlands. The research identified challenges to climate change adaptation which include lack of markets to sell farming produce, inefficient institutions, poverty and high climate variability and increased uncertainty in the behavior of seasons. The findings of this study indicated the need for similar assessment in other parts of the country as impacts of climate change and responses thereof should vary from place to place.

No abstract