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Modelllng Ralny SEASON Characterlstlcs AND .0%&--*/(3"*/:4&"40/$)"3"$5&3*45*$4"/% %306()5*/3&-"5*0/50$301130%6$5*0/*/5)& -6767)63*7&3$"5$).&/50'5)&-*.10101307*/$& 77 MODELLING RAINY SEASON CHARACTERISTICS AND DROUGHT IN RELATION TO CROP PRODUCTION IN THE LUVUVHU RIVER CATCHMENT OF THE LIMPOPO PROVINCE Report to the Water Research Commission by ME Moeletsi, TE Masupha, FP Tshililo, MP Thavhana, ZP Shabalala, KM Nape, SM Mazibuko, MI Tongwane & M Tsubo Agricultural Research Council – Institute for Soil, Climate and Water WRC Report No. TT 771/18 October 2018 Obtainable from Water Research Commission Private Bag X03 Gezina, 0031 [email protected] or download from www.wrc.org.za This Project emanates from a project entitled Modelling rainy season characteristics and drought in relation to crop production in the Luvuvhu River catchment of the Limpopo Province (WRC Project No: K5/2403) DISCLAIMER This report has been reviewed by the Water Research Commission (WRC) and approved for publication. Approval does not signify that the contents necessarily reflect the views and policies of the WRC, nor does mention of trade names or commercial products constitute endorsement or recommendation for use. ISBN 978-0-6392-0052-1 Printed in the Republic of South Africa © WATER RESEARCH COMMISSION ii ACKNOWLEDGEMENTS The authors and project team would like to acknowledge the Water Research Commission of South Africa for funding this research project. The staff of the Limpopo Provincial Department of Agriculture have been very helpful towards the implementation of the project. Special thanks to Mr Ramogondo, Mr Muthala, Mr Kwinda, Mr Sambo, Mr Mulelu, Mr Mudau, Mr Hlungwani and Mr Mashaku. The authors would like to thank the following farmers who permitted the project to establish pilot sites on their farms: Farm location Farmer 1. Thononda Mrs Alilali Mutshatshi 2. Elim (Mpheni) Mr Selamulela and Mr Samson Muleya 3. Lamvi Mr Heison Nemasisi 4. Malamulele Mr Lovemore Masia 5. Mhinga Mr Aron Ngobeni 6. Sanari Mr Ndateni Reckson Maliehe The project benefitted immensely from the participation of the Reference Group members: Dr E Archer, Dr S Mgquba and Dr G Backeberg, under the chairmanship of Prof. S Mpandeli. Thanks to the staff of Agricultural Research Council – Institute for Soil, Climate and Water who assisted with the implementation of the project. Editorial assistance from Dr T Fyfield is gratefully acknowledged. iii EXECUTIVE SUMMARY Weather and climate variability are the major factors that affect the inter-annual performance of crop production and yield in all environments. As a result, climate information has to be considered properly in the planning of agricultural activities and decision-making. In addition, many practices such as the use of irrigation, application of manure, improved cultivation and improved crop varieties have been developed over the years to enable agriculture to adapt to climate variability and climate change. Agricultural productivity can be improved further, costs of production can be reduced and crop failures can be avoided by using weather and climate information. The challenge that South Africa is currently facing is to use climate information for risk management strategies that increase preparedness and reduce vulnerability to climate variability. Planning and management of sustainable agricultural production systems require detailed agroclimatological information that is presented in a clear manner. Agricultural production varies significantly from year to year mainly due to the climate risks that affect the country. The frequencies, means, extremes, deviations, exceedance of thresholds, spatial variability and trends of agroclimatological parameters are important for assessing and managing agricultural risk. Rainy season characteristics of importance to agriculture include onset, cessation and length of the growing season, rainfall amount, and the probability of a dry spell occurrence during the growing season. Delayed onset of the rainy season, especially in largely semi-arid southern Africa, extends the growing period of summer crops into winter. Rainfall seasons in semi-arid environments are characterised by frequent drought incidences. Drought is one of the most disastrous climate-related hazards that has a significant impact on global agriculture, environment, infrastructure and socio-economic activities. In South Africa, recurring droughts have always been an endemic feature of climate, affecting all sectors of society. Agriculture is usually the first sector to be affected by drought in the country as it primarily depends on precipitation for crop growth and production. Long-term downward trends in South Africa’s crop production are often associated with periods of meteorological droughts. Thus, better agricultural decisions can be made when determining drought incidences. Climate variability and climate change have a direct impact on the productivity of many socio- economic activities. Due to its reliance on climate variables, it is projected that the agricultural sector in South Africa will be significantly affected by anticipated climate change. Drought risk is expected to increase in drought-prone areas, particularly in subtropical climates, which places stress on food security systems. Thus, high intra- and inter-seasonal climate variability, recurrent droughts and floods that affect both crops and livestock, and persistent poverty that limits the capacity to adapt to climate change, also contribute to increasing this vulnerability. Rainfall variability poses a threat to farmers’ livelihoods and agricultural production. The reliance of farmers on rainfall, which varies annually, makes them especially vulnerable to rainfall variability. Resource-poor farmers mostly conduct their agricultural activities without considering weather and climate information properly. These farmers are highly vulnerable to impacts of climate variability and climate change and thus the project assisted them in making informed choices on activities such as timing and planting as well as appropriate crop cultivars. iv This study was necessitated by the common lack of useful packaging for the agroclimatological information to help farmers and decision makers. It particularly focuses on rainy season characteristics, drought and flood risk affecting crop production in the Luvuvhu River catchment in the Limpopo Province. The main aim of the project was to investigate rainfall characteristics, drought and floods with reference to crop production to assist the farmers to maximise productivity through utilisation of weather and climate knowledge. Rainy season characteristics of the study area were determined with reference to maize production in the catchment. The aims of the project were: 1. To identify and quantify the rainy season characteristics with reference to crop production in the Luvuvhu River catchment. 2. To calibrate crop models to suit the environmental and management conditions of the study area. 3. To investigate evolution of drought and rainy season characteristics in the Luvuvhu River catchment under climate change. 4. To investigate changes in crop productivity under climate change. 5. To develop a decision support tool for drought and rainy season characteristics for the Luvuvhu River catchment. Climate, soil and hydrological data for the weather stations within the Luvuvhu River catchment was obtained from the Agricultural Research Council (ARC), the Department of Water and Sanitation and the South African Weather Service (SAWS). Other parameters such as evapotranspiration, which are also important for agroclimatological risk, were estimated using the Hargreaves method. All daily climate data was subjected to quality checks. The stations that had more than 30 years of data since 1970 were selected. The ARC stand-alone patching tool was developed to patch missing climate data. This tool patches missing values of daily minimum and maximum temperatures, rainfall, solar radiation, water vapour pressure and wind speed. The tool employs commonly used techniques including arithmetic averaging, normal ratio, inverse distance weighted (IDW), correlation coefficient, multiple linear regression (MLR) and UK traditional method (UK). It compares the best method of estimating the daily data by using mean absolute error, root mean square error (RMSE) and correlation coefficient (r). For the Luvuvhu River catchment, the IDW and MLR methods provided better results for temperature than other methods. Simulated daily climate change data (solar radiation, rainfall, minimum and maximum temperature) for the period 1980/1981 to 2099/2100 were obtained from the Climate Change, Agriculture and Food Security climate data portal. The data was the output of the high resolution projections of the Conformal-Cubic Atmospheric Model (CCAM), which is a regional model that is downscaled from the coupled global climate model CSIRO Mark3.6.0. The Instat+ v 3.36 statistical program was used to calculate onset, cessation and length of the rainy season, and dry spells characteristics. Statistica software was used to generate descriptive statistics as well as to calculate probability of exceedance and non-exceedance for the rainy season characteristics. The Anderson–Darling goodness-of-fit test was performed to determine the distribution model that best represents the data. The resultant probabilities of exceedance v were then computed from the distribution models that best fit the data. A non-parametric Spearman rank correlation coefficient test was used to analyse data for trends
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