Browsing by Author "Dlamini, Luleka"

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    A Global Systematic Review of Improving Crop Model Estimations by Assimilating Remote Sensing Data: Implications for Small-Scale Agricultural Systems
    (Multidisciplinary Digital Publishing Institute, 2023-08-17) Dlamini, Luleka; Crespo, Olivier; van Dam, Jos; Kooistra, Lammert
    There is a growing effort to use access to remote sensing data (RS) in conjunction with crop model simulation capability to improve the accuracy of crop growth and yield estimates. This is critical for sustainable agricultural management and food security, especially in farming communities with limited resources and data. Therefore, the objective of this study was to provide a systematic review of research on data assimilation and summarize how its application varies by country, crop, and farming systems. In addition, we highlight the implications of using process-based crop models (PBCMs) and data assimilation in small-scale farming systems. Using a strict search term, we searched the Scopus and Web of Science databases and found 497 potential publications. After screening for relevance using predefined inclusion and exclusion criteria, 123 publications were included in the final review. Our results show increasing global interest in RS data assimilation approaches; however, 81% of the studies were from countries with relatively high levels of agricultural production, technology, and innovation. There is increasing development of crop models, availability of RS data sources, and characterization of crop parameters assimilated into PBCMs. Most studies used recalibration or updating methods to mainly incorporate remotely sensed leaf area index from MODIS or Landsat into the WOrld FOod STudies (WOFOST) model to improve yield estimates for staple crops in large-scale and irrigated farming systems. However, these methods cannot compensate for the uncertainties in RS data and crop models. We concluded that further research on data assimilation using newly available high-resolution RS datasets, such as Sentinel-2, should be conducted to significantly improve simulations of rare crops and small-scale rainfed farming systems. This is critical for informing local crop management decisions to improve policy and food security assessments.
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    Assessing seasonal forecast use in Western Cape viticulture: a risk framework approach
    (2025) Khumalo, Fidelity Zwelihle; Crespo, Olivier; Dlamini, Luleka
    Viticulture refers to the practice of cultivating grapevines for winemaking. With a rapidly changing climate and rising global temperatures, this type of farming is under significant threat. Seasonal forecasts (SF) offer the potential to reduce farmer's vulnerability to climate risks by offering advanced climate information. This study aims to use a risk management framework to identify and compare the level of risk involved in using or not using seasonal forecasts in viticulture. The study's primary data was collected through self-administered semi-structured interviews with viticulturists, agricultural consultants. and farm managers at different wineries within the Western Cape province, South Africa (SA). Wine grape farmers were interviewed to learn about their adaptation strategies in response to climate variability and their use or no-use of SF. The interviews explored the nature and types of risks that exist when climate conditions are above and below normal conditions, with and without the use of SF. A risk management framework, designed for this study's context, was used as a reference point, giving common ground to analysing the responses thematically and produce evidence toward each study objectives. The results show that climate variability poses a significant threat to the yield and quality of wine produced and has a negative impact on the labour and finances in- volved in running a vineyard. Analysing the responses showed that forecast uncertainty is the main driver of low SF uptake thus correlating with a lack of risk precautionary decision-making measures by farmers. Making them more vulnerable to unexpected climate impacts and risks. Farmers who make use of SF are mainly commercial who work with consultants and whose main income is made from selling grapes. The evidence presented suggests that the adoption of a risk framework could potentially aid farmers in making better decisions to help them identify, mitigate and/or avoid risks using SF technology to inform risk identification for present and future climate scenarios of their vine-yards.
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    Exploring the potential of using remote sensing data to model agricultural systems in data-limited areas
    (2020) Dlamini, Luleka; Crespo, Olivier
    Crop models (CMs) can be a key component in addressing issues of global food security as they can be used to monitor and improve crop production. Regardless of their wide utilization, the employment of these models, particularly in isolated and rural areas, is often limited by the lack of reliable input data. This data scarcity increases uncertainties in model outputs. Nevertheless, some of these uncertainties can be mitigated by integrating remotely sensed data into the CMs. As such, increasing efforts are being made globally to integrate remotely sensed data into CMs to improve their overall performance and use. However, very few such studies have been done in South Africa. Therefore, this research assesses how well a crop model assimilated with remotely sensed data compares with a model calibrated with actual ground data (Maize_control). Ultimately leading to improved local cropping systems knowledge and the capacity to use CMs. As such, the study calibrated the DSSAT-CERES-Maize model using two generic soils (i.e. heavy clay soil and medium sandy soil) which were selected based on literature, to measure soil moisture from 1985 to 2015 in Bloemfontein. Using the data assimilation approach, the model's soil parameters were then adjusted based on remotely sensed soil moisture (SM) observations. The observed improvement was mainly assessed through the lens of SM simulations from the original generic set up to the final remotely sensed informed soil profile set up. The study also gave some measure of comparison with Maize_control and finally explored the impacts of this specific SM improvement on evapotranspiration (ET) and maize yield. The result shows that when compared to the observed data, assimilating remotely sensed data with the model significantly improved the mean simulation of SM while maintaining the representation of its variability. The improved SM, as a result of assimilation of remotely sensed data, closely compares with the Maize_control in terms of mean but there was no improvement in terms of variability. Data assimilation also improved the mean and variability of ET simulation when compared that of Maize_control, but only with heavy clay soil. However, maize yield was not improved in comparison. This confirms that these outputs were influenced by other factors aside from SM or the soil profile parameters. It was concluded that remote sensing data can be used to bias correct model inputs, thus improve certain model outputs.