Investigating the feasibility of using remote sensing in index-based crop insurance for South Africa’s smallholder farming systems

Crop farming in Sub-Saharan Africa (SSA) is largely practiced by resource-poor farmers under rain-fed and unpredictable weather conditions. Since agriculture is the mainstay of SSA’s economy, the lack of improved and adapted agricultural technologies in this region sets back economic development and the fight against poverty. Overcoming this constraint and achieving the sustainable development goal to end poverty, requires innovative tools that can be used for weather risk management. One tool that has been gaining momentum recently is index-based crop insurance (IBCI). Since the launch of the first IBCI program in Africa around 2005, the number of IBCI programs has increased. Unfortunately, these programs are constrained by poor product design, basis risk, and low uptake of contracts. When these issues were first pointed-out in the earliest IBCI programs, many reports suggested satellite remote sensing (RS) as a viable solution. Hence, the first objective of this study was to assess how RS has been used in IBCI, the challenges RS faces, and potential contributions of RS that have not yet been meaningfully exploited. The literature shows that IBCI programs are increasingly adopting RS. RS has improved demarcation of unit areas of insurance and enabled IBCI to reach inaccessible areas that do not have sufficient meteorological infrastructure. However, the literature also shows that IBCI is still tainted by basis risk, which emanates from poor contract designs, the influence of non-weather factors on crop yields, imperfect correlations between satellite-based indices and crop yields, and the lack of historical data for calibration. Although IBCI reports cover vegetation and crop health monitoring, few to none cover crop type and crop area mapping. Furthermore, areas including high-resolution mapping, data fusion, microwave RS, machine learning, and computer vision have not been sufficiently tested in IBCI. The second objective of this study was to assess how RS and machine learning techniques can be used to enhance the mapping of smallholder crop farming landscapes. The findings show that machine learning ensembles and the combination of optical and microwave data can map a smallholder farming landscape with a maximum accuracy of 97.71 percent. The third objective was to identify factors that influence crop yields and crop losses in order to improve IBCI design. Results demonstrated that the pervasive notion that low yields in smallholder farms are related to rainfall is an oversimplification. Factors including fertilizer use, seed variety, soil properties, soil moisture, growing degree-days, management, and socioeconomic conditions are some of the most important factors influencing crop yields and crop losses in smallholder farming systems. This shows why IBCI needs to be part of a comprehensive risk management system that understands and approaches smallholder crop farming as complex by linking insurance with advisories and input supplies. Improved inputs and good farming practices could reduce the influence of non-weather factors on crop losses, and thereby reduce basis risk in weather-based index insurance (WII) contracts. The fourth objective of this study was to assess how well the combination of synthetic aperture radar (SAR) and optical indices estimate soil moisture. As stated earlier, soil moisture was found to be one of the most important factors affecting crop yields. Although this method better estimated soil moisture over the first half of the growing season, estimation accuracies were comparable to those found in studies that had used similar datasets (RMSE = 0.043 m3 m-3, MAE = 0.034 m3 m- 3). Further interrogation of interaction effects between the variables used in this study and consideration of other factors that affect SAR backscatter could improve the method. More importantly, incorporating high-resolution satellite-based monitoring of soil moisture into IBCI could potentially reduce basis risk. The fifth objective of this study was to develop an IBCI for smallholder crop farming systems. The proposed IBCI scheme covers maize and derives index thresholds from crop water requirements and satellite-based rainfall estimates. It covers rainfall deficits over the vegetative, mid-season, and late-season stages of maize growth. The key contribution of this system is the derivation of index thresholds from CWR and site-specific rainfall conditions. The widely used approach, which calibrates IBCI by correlating yields and rainfall, exposes contracts to basis risk because, by simply correlating yield and rainfall data, it overlooks the influence of non-weather factors on crop yields and losses. The proposed system must be linked or bundled with non-weather variables that affect crop yields. Effectively, this means that the insurance must be linked or bundled with advisories and input supplies to address the influence of non-weather factors on crop losses. This system also incorporates a crop area-mapping component, which was found to be lacking in many IBCI systems. In conclusion, an IBCI that is based on crop water requirements, which incorporates crop area mapping and links insurance with non-weather crop yield-determining factors, is potentially capable of improving crop insurance for smallholder farming systems.Thesis (PhD) -- Faculty of Science and Agriculture, 202

Investigating the feasibility of using remote sensing in index-based crop insurance for South Africa’s smallholder farming systems

Crop farming in Sub-Saharan Africa (SSA) is largely practiced by resource-poor farmers under rain-fed and unpredictable weather conditions. Since agriculture is the mainstay of SSA’s economy, the lack of improved and adapted agricultural technologies in this region sets back economic development and the fight against poverty. Overcoming this constraint and achieving the sustainable development goal to end poverty, requires innovative tools that can be used for weather risk management. One tool that has been gaining momentum recently is index-based crop insurance (IBCI). Since the launch of the first IBCI program in Africa around 2005, the number of IBCI programs has increased. Unfortunately, these programs are constrained by poor product design, basis risk, and low uptake of contracts. When these issues were first pointed-out in the earliest IBCI programs, many reports suggested satellite remote sensing (RS) as a viable solution. Hence, the first objective of this study was to assess how RS has been used in IBCI, the challenges RS faces, and potential contributions of RS that have not yet been meaningfully exploited. The literature shows that IBCI programs are increasingly adopting RS. RS has improved demarcation of unit areas of insurance and enabled IBCI to reach inaccessible areas that do not have sufficient meteorological infrastructure. However, the literature also shows that IBCI is still tainted by basis risk, which emanates from poor contract designs, the influence of non-weather factors on crop yields, imperfect correlations between satellite-based indices and crop yields, and the lack of historical data for calibration. Although IBCI reports cover vegetation and crop health monitoring, few to none cover crop type and crop area mapping. Furthermore, areas including high-resolution mapping, data fusion, microwave RS, machine learning, and computer vision have not been sufficiently tested in IBCI. The second objective of this study was to assess how RS and machine learning techniques can be used to enhance the mapping of smallholder crop farming landscapes. The findings show that machine learning ensembles and the combination of optical and microwave data can map a smallholder farming landscape with a maximum accuracy of 97.71 percent. The third objective was to identify factors that influence crop yields and crop losses in order to improve IBCI design. Results demonstrated that the pervasive notion that low yields in smallholder farms are related to rainfall is an oversimplification. Factors including fertilizer use, seed variety, soil properties, soil moisture, growing degree-days, management, and socioeconomic conditions are some of the most important factors influencing crop yields and crop losses in smallholder farming systems. This shows why IBCI needs to be part of a comprehensive risk management system that understands and approaches smallholder crop farming as complex by linking insurance with advisories and input supplies. Improved inputs and good farming practices could reduce the influence of non-weather factors on crop losses, and thereby reduce basis risk in weather-based index insurance (WII) contracts. The fourth objective of this study was to assess how well the combination of synthetic aperture radar (SAR) and optical indices estimate soil moisture. As stated earlier, soil moisture was found to be one of the most important factors affecting crop yields. Although this method better estimated soil moisture over the first half of the growing season, estimation accuracies were comparable to those found in studies that had used similar datasets (RMSE = 0.043 m3 m-3, MAE = 0.034 m3 m- 3). Further interrogation of interaction effects between the variables used in this study and consideration of other factors that affect SAR backscatter could improve the method. More importantly, incorporating high-resolution satellite-based monitoring of soil moisture into IBCI could potentially reduce basis risk. The fifth objective of this study was to develop an IBCI for smallholder crop farming systems. The proposed IBCI scheme covers maize and derives index thresholds from crop water requirements and satellite-based rainfall estimates. It covers rainfall deficits over the vegetative, mid-season, and late-season stages of maize growth. The key contribution of this system is the derivation of index thresholds from CWR and site-specific rainfall conditions. The widely used approach, which calibrates IBCI by correlating yields and rainfall, exposes contracts to basis risk because, by simply correlating yield and rainfall data, it overlooks the influence of non-weather factors on crop yields and losses. The proposed system must be linked or bundled with non-weather variables that affect crop yields. Effectively, this means that the insurance must be linked or bundled with advisories and input supplies to address the influence of non-weather factors on crop losses. This system also incorporates a crop area-mapping component, which was found to be lacking in many IBCI systems. In conclusion, an IBCI that is based on crop water requirements, which incorporates crop area mapping and links insurance with non-weather crop yield-determining factors, is potentially capable of improving crop insurance for smallholder farming systems.Thesis (PhD) -- Faculty of Science and Agriculture, 202

An evaluation of USA Learns and its lesson plan based on content-based instruction

This paper presents a lesson plan based on the content-based instruction（CBI）approach in an English class, combining“ USA Learns,” a website for English as a second language learners. USA Learns is a free website developed by the Sacramento County Office of Education in collaboration with other institutions that assists adults in learning English autonomously. However, it also provides instructors with a page where they can monitor their students’ learning records. This fact enables the instructor to conduct CBI classes for university students in class. This article provides an introduction to usage-based language learning, a broad overview of USA Learns, a theoretical evaluation of CBI, and a lesson plan that incorporates CBI into USA Learns.departmental bulletin pape

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Formation of Intersubunit Disulfide Bonds in the TM Domain of Resting α_IIbβ₃

<div><p>(A) 293T cells were transiently transfected with the indicated integrin constructs and metabolically labeled, and were untreated (–) or oxidized with Cu-phenanthroline on ice for 10 min (+), and then lysates were immunoprecipitated with mouse mAb 10E5 against α_IIbβ₃, followed by SDS-7.5% PAGE under nonreducing conditions and fluorography. Positions of molecular size markers are shown on the left.</p> <p>(B) Disulfide bond formation efficiency. For each residue pair, the radioactivity of the αβ heterodimer band divided by the total radioactivity (sum of α, β, and αβ bands) was used to calculate the disulfide bond formation efficiency and is depicted by a gray scale (white for 0% to black for 100% efficiency). The upper and lower halves of the circle indicate the efficiency before (constitutive) and after (oxidized) Cu-phenanthroline treatment at 0 °C, respectively. Residue pairs that form inducible disulfides (i.e., efficiency increases more than 10% after oxidation) are denoted by asterisks. Results are the mean of at least two independent experiments. Solid line shows the predicted TM boundary; dotted line indicates boundary between residues that form constitutive and inducible disulfide bonds.</p> <p>(C) Relative orientation of the α_IIb and β₃ TM helices near their N-terminal ends. The TM domains are depicted schematically as α helices, and experimental results from cysteine scanning were used to deduce their relative orientation. The resultant schematic model is shown in both top and side views. Residue pairs that form disulfide bonds at greater than 50% efficiency are connected by solid (constitutive disulfides) or dotted (inducible disulfides) red lines. The gray dotted line represents the boundary between residues that form constitutive and inducible disulfide bonds. Residues are color coded based on the number of constitutive or inducible disulfide bonds formed at greater than 50% efficiency: multiple bonds (interacting residues, red), only one bond (peripheral residues, pink), and no bonds (outside residues, blue).</p> <p>(D) Homodimer formation by the W967C mutant of α_IIb. Transfection, radiolabeling, and immunoprecipitation was performed as in (A). Full-length α_IIb with the W967C mutation (α-W967C) but not the truncated active mutant α_IIb with W697C (α*-W967C) produced a homodimer band (α–α) larger than the heterodimer band (α–β). The α972C/βL697C combination that produces efficient inducible heterodimer is shown as a standard (lanes 1 and 2).</p></div

Formation of Intersubunit Disulfide Bonds in the TM Domain of α_IIb*β₃ and Effect on Ligand Binding and LIBS Epitopes

<div><p>(A) Immunoprecipitation. Immunoprecipitation of [³⁵S]-labeled receptors and nonreducing SDS-PAGE and fluorography was as described in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020153#pbio-0020153-g002" target="_blank">Figure 2</a>.</p> <p>(B) FITC-fibrinogen binding. Binding was determined by immunofluorescence as described in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020153#pbio-0020153-g003" target="_blank">Figure 3</a>.</p> <p>(C) LIBS exposure. Three different anti-LIBS mAbs (LIBS6, D3, and AP5) were used to probe the conformational state. mAb binding is expressed as the mean fluorescence intensity in the absence (control, open bars) or presence (+Mn/RGD, black bars) of Mn²⁺ and RGD peptide.</p> <p>(D) Disulfide bond formation efficiency. Disulfide bond formation in α_IIb*β₃ heterodimers with the indicated residues mutated to cysteine was determined as described in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020153#pbio-0020153-g002" target="_blank">Figure 2</a>B.</p></div

Disulfide-bonded Receptors Can Be Activated from Outside the Cell

<p>Transiently transfected 293T cells expressing wild-type (αwt/βwt) or mutant α_IIbβ₃ heterodimers that form constitutive disulfide bonds (α965C/β693C and α968C/β693C) or are reported elsewhere to be activated (αwt/βG708N) (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020153#pbio-0020153-Li3" target="_blank">R. Li et al. 2003</a>) were incubated with FITC-fibrinogen in a physiological buffer (control, white bars) or in the presence of 1 mM Mn²⁺ and the activating mAb PT25–2 (+Mn/PT25–2, black bars). Binding of FITC-fibrinogen was determined by flow cytometry as the mean fluorescence intensity and normalized by dividing by the mean fluorescence intensity with Cy3-labeled anti-β₃ mAb AP3 and multiplying by 100.</p

Formation of an Intersubunit Disulfide Bridge within the Membrane Reverses the Active Phenotype of the α_IIb*β₃ Receptor

<div><p>(A) Radiolabeled 293T cells expressing the indicated mutant integrins were treated with Cu-phenanthroline at 0 °C or 37 °C, followed by immunoprecipitation with anti-α_IIbβ₃, SDS-PAGE, and fluorography to probe disulfide bond formation.</p> <p>(B) Efficiency of intramembranous disulfide bond formation in the context of the truncated α_IIb*β₃ receptor was assessed after Cu-phenanthroline oxidation at 0 °C or 37 °C and expressed as in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020153#pbio-0020153-g002" target="_blank">Figure 2</a>B.</p> <p>(C) Ligand binding by wild-type or mutant α_IIb*β₃ expressed on 293T cells was determined before (–) and after (+) Cu-phenanthroline oxidation at 37 °C and expressed as in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020153#pbio-0020153-g003" target="_blank">Figure 3</a>.</p></div

Sequences of the α_IIb and β₃ TM Regions

<p>Segments predicted as TM by TMHMM version 2.0 (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020153#pbio-0020153-Krogh1" target="_blank">Krogh et al. 2001</a>) are boxed. The more C-terminal KVGFF sequence in α_IIb and KLLITI sequence in β₃ are additionally predicted to be in the membrane by <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020153#pbio-0020153-Armulik1" target="_blank">Armulik et al. (1999)</a>. Charged residues involved in intersub-unit salt bridges (dotted lines) in the NMR cytoplasmic domain structure (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0020153#pbio-0020153-Vinogradova2" target="_blank">Vinogradova et al. 2002</a>) are marked with ovals. Residues used for cysteine scanning in this study are indicated by heavy dots. Arrows show the boundary between residues forming disulfide bonds constitutively and after oxidation. The se-quences of the α_IIb* GFFKR truncation and α_IIb" GFFKR/GAAKR mutants are also shown.</p

Electron microscopic analysis of the Nrx1α ectodomain (NX1α_EC).

<p>(A) A raw image of the negatively stained NX1α_EC. Scale bar: 50 nm. (B) Spontaneous degradation of purified NX1α_EC. SDS-PAGE analysis of NX1α_EC protein immediately after purification (left) and again after 2 months storage at 4°C (right) shows that the protein undergoes proteolytic cleavage to produce N-terminal (30 kDa) and C-terminal (110-kDa) fragments. (C) Two-dimensional class averages (upper row images) obtained from multiple electron micrographs and corresponding projection views produced from the 3-D volume map (lower row images). Scale bar, 10 nm. (D) Three-dimensional reconstruction of the NX1α_EC created from multiple oriented particles. (E) Predicted 3D domain organization within the NX1α_EC fragment. Atomic coordinates for NX1α(III) are manually fitted to the densities corresponding to the LNS5-6 and LNS3-4 segments, keeping the C-terminus of LNS4 and the N-terminus of LNS5 close enough to be connected. Domain connectivity was also considered when fitting the LNS2 domain structure (PDB ID: 2H0B) into the density at the bottom. LNS1+EGF1 segment disappeared in the reconstructed volume was not assigned.</p