Modelling spatial variability of coffee (Coffea Arabica L.) crop condition with multispectral remote sensing data.

Doctor of Philosophy in Environmental Science. University of KwaZulu-Natal, Pietermaritzburg, 2017.Abstract available in PDF file

Near Real-Time Biophysical Rice (Oryza sativa L.) Yield Estimation to Support Crop Insurance Implementation in India

Immediate yield loss information is required to trigger crop insurance payouts, which are important to secure agricultural income stability for millions of smallholder farmers. Techniques for monitoring crop growth in real-time and at 5 km spatial resolution may also aid in designing price interventions or storage strategies for domestic production. In India, the current government-backed PMFBY (Pradhan Mantri Fasal Bima Yojana) insurance scheme is seeking such technologies to enable cost-efficient insurance premiums for Indian farmers. In this study, we used the Decision Support System for Agrotechnology Transfer (DSSAT) to estimate yield and yield anomalies at 5 km spatial resolution for Kharif rice (Oryza sativa L.) over India between 2001 and 2017. We calibrated the model using publicly available data: namely, gridded weather data, nutrient applications, sowing dates, crop mask, irrigation information, and genetic coefficients of staple varieties. The model performance over the model calibration years (2001–2015) was exceptionally good, with 13 of 15 years achieving more than 0.7 correlation coefficient (r), and more than half of the years with above 0.75 correlation with observed yields. Around 52% (67%) of the districts obtained a relative Root Mean Square Error (rRMSE) of less than 20% (25%) after calibration in the major rice-growing districts (>25% area under cultivation). An out-of-sample validation of the calibrated model in Kharif seasons 2016 and 2017 resulted in differences between state-wise observed and simulated yield anomalies from –16% to 20%. Overall, the good ability of the model in the simulations of rice yield indicates that the model is applicable in selected states of India, and its outputs are useful as a yield loss assessment index for the crop insurance scheme PMFBY

Drivers of diversity and community structure of bees in an agroecological region of Zimbabwe

Worldwide bees provide an important ecosystem service of plant pollination. Climate change and land-use changes are among drivers threatening bee survival with mounting evidence of species decline and extinction. In developing countries, rural areas constitute a significant proportion of the country's land, but information is lacking on how different habitat types and weather patterns in these areas influence bee populations.This study investigated how weather variables and habitat-related factors influence the abundance, diversity, and distribution of bees across seasons in a farming rural area of Zimbabwe. Bees were systematically sampled in five habitat types (natural woodlots, pastures, homesteads, fields, and gardens) recording ground cover, grass height, flower abundance and types, tree abundance and recorded elevation, temperature, light intensity, wind speed, wind direction, and humidity. Zero-inflated models, censored regression models, and PCAs were used to understand the influence of explanatory variables on bee community composition, abundance, and diversity.Bee abundance was positively influenced by the number of plant species in flower (p < .0001). Bee abundance increased with increasing temperatures up to 28.5°C, but beyond this, temperature was negatively associated with bee abundance. Increasing wind speeds marginally decreased probability of finding bees.Bee diversity was highest in fields, homesteads, and natural woodlots compared with other habitats, and the contributions of the genus Apis were disproportionately high across all habitats. The genus Megachile was mostly associated with homesteads, while Nomia was associated with grasslands.Synthesis and applications. Our study suggests that some bee species could become more proliferous in certain habitats, thus compromising diversity and consequently ecosystem services. These results highlight the importance of setting aside bee-friendly habitats that can be refuge sites for species susceptible to land-use changes

Remote Sensing Based Yield Estimation of Rice (Oryza Sativa L.) Using Gradient Boosted Regression in India

Accurate and spatially explicit yield information is required to ensure farmers’ income and food security at local and national levels. Current approaches based on crop cutting experiments are expensive and usually too late for timely income stabilization measures like crop insurances. We, therefore, utilized a Gradient Boosted Regression (GBR), a machine learning technique, to estimate rice yields at ~500 m spatial resolution for rice-producing areas in India with potential application for near real-time estimates. We used resampled intermediate resolution (~5 km) images of the Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and observed yields at the district level in India for calibrating GBR models. These GBRs were then used to downscale district yields to 500 m resolution. Downscaled yields were re-aggregated for validation against out-of-sample district yields not used for model training and an additional independent data set of block-level (below district-level) yields. Our downscaled and re-aggregated yields agree well with reported district-level observations from 2003 to 2015 (r = 0.85 & MAE = 0.15 t/ha). The model performance improved further when estimating separate models for different rice cropping densities (up to r = 0.93). An additional out-of-sample validation for the years 2016 and 2017, proved successful with r = 0.84 and r = 0.77, respectively. Simulated yield accuracy was higher in water-limited, rainfed agricultural systems. We conclude that this downscaling approach of rice yield estimation using GBR is feasible across India and may complement current approaches for timely rice yield estimation required by insurance companies and government agencies

The impact of land-use/land cover changes on water balance of the heterogeneous Buzi sub-catchment, Zimbabwe

The nature of interactions between ecological, physical and hydrological characteristics that determine the effects of land cover change on surface and sub-surface hydrology is not well understood in both natural and disturbed environments. The spatiotemporal dynamics of water fluxes and their relationship with land cover changes between 2009 and 2017 in the headwater Buzi sub-catchment in Zimbabwe is evaluated. To achieve this, land cover dynamics for the area under study were characterised from the 30 m Landsat data, using the eXtreme Gradient Boosting (XGBoost) algorithm. After the land cover classification, the key water balance components namely; interception, transpiration and evapotranspiration (ET) contributions for each class in 2009 and 2017 were estimated. Image classification of Landsat data achieved good overall accuracies above 80% for the two periods. Results showed that the percentage of the plantation land cover types decreased slightly between 2009 (25.4%) and 2017 (22.5%). Partitioning the annual interception, transpiration and ET according to land cover classes showed that the highest amounts of ET in the basin were from plantation where land cover types with tea had the highest interception, transpiration and ET in the catchment. Higher ET, interception and transpiration were observed in the eastern parts of the catchment. At catchment level, results show that 2017 had a higher water balance than 2009, which was partly explained by the decrease in plantation cover type

Climate change and specialty coffee potential in Ethiopia

Current climate change impact studies on coffee have not considered impact on coffee typicities that depend on local microclimatic, topographic and soil characteristics. Thus, this study aims to provide a quantitative risk assessment of the impact of climate change on suitability of five premium specialty coffees in Ethiopia. We implement an ensemble model of three machine learning algorithms to predict current and future (2030s, 2050s, 2070s, and 2090s) suitability for each specialty coffee under four Shared Socio-economic Pathways (SSPs). Results show that the importance of variables determining coffee suitability in the combined model is different from those for specialty coffees despite the climatic factors remaining more important in determining suitability than topographic and soil variables. Our model predicts that 27% of the country is generally suitable for coffee, and of this area, only up to 30% is suitable for specialty coffees. The impact modelling showed that the combined model projects a net gain in coffee production suitability under climate change in general but losses in five out of the six modelled specialty coffee growing areas. We conclude that depending on drivers of suitability and projected impacts, climate change will significantly affect the Ethiopian speciality coffee sector and area-specific adaptation measures are required to build resilience

The impact of land-use/land cover changes on water balance of the heterogeneous Buzi sub-catchment, Zimbabwe

The nature of interactions between ecological, physical and hydrological characteristics that determine the effects of land cover change on surface and sub-surface hydrology is not well understood in both natural and disturbed environments. The spatiotemporal dynamics of water fluxes and their relationship with land cover changes between 2009 and 2017 in the headwater Buzi sub-catchment in Zimbabwe is evaluated. To achieve this, land cover dynamics for the area under study were characterised from the 30 m Landsat data, using the eXtreme Gradient Boosting (XGBoost) algorithm

Wildlife-vehicle collisions in hurungwe safari area, northern zimbabwe

This study is the first to assess wildlife-vehicle collisions (WVC) in Zimbabwe. The study analysed the impact and factors that influence vehicle collisions with large wild mammals along the Harare-Chirundu road section in the protected Hurungwe Safari Area, northern Zimbabwe. Data were retrieved from the Hurungwe Safari Area records and covered the period between 2006 and 2013. Descriptive statistics were used to analyse the recorded variables across the sampled area and to show trends of the prevalence of large wild mammals roadkill over time. Using STATISTICA version 10 for Windows, a two-tailed Mann-Whitney U test was used to determine differences between the number of wild mammal animal roadkills and seasons. A total of 47 large wild mammal animals were killed between 2006 and 2013. The large wild mammal animals that died as a result of vehicle collisions constituted a total of 11 species, with the African buffalo and spotted hyena being the most hit and killed animal species. Most WVC involved heavy haulage trucks and passenger buses. There was no significance difference (P = 0.936) between number of large wild mammal animals killed from WVC between dry and wet seasons. The large wild mammal animals were mostly killed in areas near water sources. We recommend for the inclusion of wildlife protection safeguards in road infrastructure network design and development, particularly on roads that traverse across protected areas in Zimbabwe and beyond. © 2020 The Author(s

Landuse and landcover change assessment in the Upper Runde sub-catchment, Zimbabwe and possible impacts on reservoir sedimentation

This work assesses land cover changes on the Upper Runde sub-catchment, Zimbabwe, and associated effects on sedimentation rates and risks. The model was implemented using the common Geographic Information Systems tools. To achieve this objective, mean annual and monthly rainfall, as well as sediment data, were used (December 2016 and April 2017). Land use and land cover changes were assessed using time-series Landsat data acquired between the years 2000 and 2016. The Revised Universal Soil Loss (RUSLE) model was used to model sedimentation rates in the catchmen

Factors affecting soil quality among smallholder macadamia farms in Malawi

Declining soil fertility limits smallholder macadamia productivity in Malawi. To reverse this trend, it is essential to apply organic and inorganic fertilisers in an efficient and effective manner. Yet, fertilizer recommendations for smallholder macadamia (Macadamia integrifolia) production in Malawi are not site-specific. Nutrient imbalances can occur if fertilisers are applied without a clear understanding of whether they are required or not. This may lead to yield losses, unnecessary costs, and other environmental issues associated with excess fertiliser application. To address this research need/ knowledge gap, our study examined the current soil fertility status among smallholder macadamia farms in Malawi. Specifically, the objective was to establish an evidence base for promoting soil fertility restoration interventions for smallholder macadamia production. One hundred and eighty nine soil samples at a depth of 0–15 cm were collected from sixty three smallholder macadamia farms belonging to the Highlands Macadamia Cooperative Union Limited members in central and southern Malawi. We found that the majority of the soils were sandy loams (52%), strongly acidic (mean pH ≤ 5.1), and deficient in essential nutrients required for the healthy growth of macadamia. The soils had an average low cation exchange capacity of 1.67 cmol ( +) kg−1, which is inadequate for macadamia cultivation. More than half of the sampled soils had very low organic matter content (≤ 1%). The low soil organic matter content, coupled with the sandy texture and high acidity, contributed to the observed low concentrations of essential nutrients and cation exchange capacity. Poor agronomic practices and inherent soil characteristics are responsible for this low soil fertility. Altogether, our findings underscore the urgent need to identify and implement more sustainable and effective soil nutrient management practices that help to improve the soil fertility of macadamia farms under smallholder systems