Climate Dataset for South Africa by the Agricultural Research Council

Moeletsi, Mokhele Edmond; Myeni, Lindumusa; Kaempffer, Ludwig Christian; Vermaak, Derick; Nysschen, Gert De; Henningse, Chrisna; Nel, Irene; Rowswell, Dudley

doi:10.3390/data7080117

Cited by 10 publications

(5 citation statements)

References 24 publications

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“…2). As described by Moeletsi et al [46], the missing values in climate data are due to technical malfunctions (such as sensor failures) and environmental factors (such as floods). The data quality was rigorously monitored, and any missing values were substituted with the mean values obtained from the agrometeorological stations in close proximity [30].…”

Section: Study Areamentioning

confidence: 99%

Assessing the Impact of Agricultural Drought on Yield over Maize Growing Areas, Free State Province, South Africa, Using the SPI and SPEI

Makuya,

Tesfuhuney,

Moeletsi

et al. 2024

Sustainability

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Maize (Zea mays L.) is an essential crop in South Africa serving as a staple food; however, agricultural drought threatens its production, resulting in lower yields. This study aimed to assess the impact of agricultural drought on maize yield in the major areas (Bethlehem, Bloemfontein, and Bothaville) that produce maize in the Free State Province from 1990 to 2020. The study used the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) to examine drought occurrences and severity during the maize growing season (October–March). The Standardized Yield Residuals Series (SYRS), Crop Drought Resilient Factor (CDRF), Spearman’s Rank Correlation (rs), and yield loss rate were employed to emphasize agricultural drought impact on maize yield. The results based on the SPI and SPEI show that drought frequently occurred in Bethlehem, followed by Bloemfontein and Bothaville. Drought severity indicated that moderate droughts were prevalent in Bethlehem, while severe droughts were in all areas (Bethlehem, Bloemfontein, and Bothaville) and extreme droughts in Bloemfontein. The agricultural drought’s impact on maize varied across growth seasons and areas. Notably, the lowest SYRS value of −2.38 (1991/92) was observed in Bethlehem. An extremely strong significant correlation (rsSPEI-6 vs SYRS = 0.83, p = 1.07 × 10−8) was observed between the SPEI and SYRS in Bloemfontein during the October–November–December–January–February–March (ONDJFM) season. The CDRF indicated that maize yield was severely non-resilient (CDRF < 0.8) to drought in Bethlehem (CDRF = 0.27) and Bloemfontein (CDRF = 0.33) and resilient (CDRF = 1.16) in Bothaville. The highest maize yield loss of −88.62% was observed in Bethlehem due to extreme agricultural drought. The results suggest that, historically, agricultural drought was a threat to maize production in the studied areas, particularly in Bethlehem and Bloemfontein. This underscores the implementation of sustainable agricultural practices, such as drought-resistant varieties in these areas, to mitigate the impacts of climate change, especially drought, and ensure food security. This is a step toward achieving Sustainable Development Goal 2 (Zero Hunger).

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Section: Study Areamentioning

confidence: 99%

Assessing the Impact of Agricultural Drought on Yield over Maize Growing Areas, Free State Province, South Africa, Using the SPI and SPEI

Makuya,

Tesfuhuney,

Moeletsi

et al. 2024

Sustainability

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“…The climate data utilized in this study were acquired from the Agricultural Research Council climate databank [64]. The dataset encompassed a 30-year span (1979/80-2017/18) of daily temperature and rainfall records.…”

Section: Data and Quality Controlmentioning

confidence: 99%

“…A rigorous examination of the data was undertaken to identify inconsistencies and errors, leading to the removal of faulty values. Any gaps in the data were addressed using the ARC Stand-Alone Data Patch function, which utilizes the Inverse Distance Weighting method with neighbouring stations [64,66]. The weather station data were quantified through comparison with hourly field ERA5-Land climate reanalysis data [67].…”

Section: Data and Quality Controlmentioning

confidence: 99%

Analysis of Climate Variability and Its Implications on Rangelands in the Limpopo Province

Maluleke,

Moeletsi,

Tsubo

2023

Climate

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In recent decades, southern Africa has experienced a shift towards hotter and drier climate conditions, affecting vital sectors like agriculture, health, water, and energy. Scientific research has shown that the combination of high temperatures and unreliable rainfall can have detrimental effects on agricultural production. Thus, this study focused on assessing climate variability, with implications on rangelands in the Limpopo Province of South Africa over 38 years. Historical climate data from 15 stations, including rainfall and minimum and maximum temperatures from 1980 to 2018, were analysed. To achieve the main objective, various statistics including mean, standard deviation, and coefficient of variation (CV) were computed for all variables across four seasons. The results highlighted significant variability in rainfall, with Musina (71.2%) and Tshiombo (88.3%) stations displaying the highest variability during the September-to-April season. Both minimum and maximum temperatures displayed low variability. The Mann–Kendall test revealed both increasing and decreasing trends in minimum temperatures and rainfall across different stations. Notably, there was a significant increase in maximum temperatures. This study provides valuable climate information for decision makers, aiding in the planning and management of agricultural activities, particularly in understanding how climate variations affect forage availability in rangelands.

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“…The development of the newsletter entailed assimilating value-added products based on rainfall, vegetation activity, active fire, and surface water resources data covering the whole of South Africa. The rainfall products offered include those obtained from the combined inputs of the automatic weather station network of the ARC [18], while the vegetation conditions and active fire information are represented with remotely sensed products extracted from data archived in the CRID using automated Python scripts. In addition, the surface water resources products are also derived from remote sensing imagery, and supported with advanced machine and deep learning techniques.…”

Section: Operational Frameworkmentioning

confidence: 99%

“…Monthly precipitation data are primarily obtained from the ARC's in-house agro-climate databank (see [18] for details regarding this databank and the weather stations therein). Near real-time, public-good, 10-daily rainfall data from the South African Weather Service (SAWS) and the Kruger National Park are also utilized in the monthly production of GIS rainfall surfaces, which also include satellite-derived rainfall estimates (Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data [19]) as the input to the combined final product at a 10-daily time scale (see [20] for a detailed description on how the monthly GIS rainfall surfaces are created).…”

Section: Rainfallmentioning

confidence: 99%

The Umlindi Newsletter: Disseminating Climate-Related Information on the Management of Natural Disaster and Agricultural Production in South Africa

Maake,

Malherbe,

Masupha

et al. 2023

Climate

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The Umlindi newsletter was developed to provide information towards climate advisories, considering, for instance, drought conditions, presented in a relevant manner for the agricultural and disaster sectors in South Africa. This newsletter, which is disseminated on a monthly basis, provides information derived from climate-related monitoring products obtained from an integration of remote sensing and in situ data from weather stations. It contains useful indicators, such as rainfall, vegetation, and fire conditions, that provide an overview of conditions across the country. The present study demonstrates how these natural resource indices are integrated and consolidated for utilization by farmers, policy-makers, private organizations, and the general public to make day-to-day decisions on the management and mitigation of natural disasters. However, there is a need to expand these baseline observation initiatives, including the following: (1) forecasting future conditions to strengthen coping mechanisms of government, farmers, and communities at large; and (2) incorporating information on other natural disasters such as floods and extreme heat. In the context of South Africa, this information is important to improve disaster preparedness and management for agricultural productivity. In a global context, the Umlindi newsletter can be insightful for developing and disseminating natural resources information on adaptation to and mitigation of climate change and variability impacts to other regions facing similar risks. Furthermore, while international organizations also provide natural resource information, the Umlindi newsletter may be distinguished by its regional focus and linkages to individual communities. It bridges the gap between global environmental data and local decision-making by illustrating how global scientific knowledge may be applied locally.

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Climate Dataset for South Africa by the Agricultural Research Council

Cited by 10 publications

References 24 publications

Assessing the Impact of Agricultural Drought on Yield over Maize Growing Areas, Free State Province, South Africa, Using the SPI and SPEI

Assessing the Impact of Agricultural Drought on Yield over Maize Growing Areas, Free State Province, South Africa, Using the SPI and SPEI

Analysis of Climate Variability and Its Implications on Rangelands in the Limpopo Province

The Umlindi Newsletter: Disseminating Climate-Related Information on the Management of Natural Disaster and Agricultural Production in South Africa

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