Evaluation of land suitability methods with reference to neglected and underutilised crop species: A scoping review

In agriculture, land use and land classification address questions such as “where”, “why” and “when” a particular crop is grown within a particular agroecology. To date, there are several land suitability analysis (LSA) methods, but there is no consensus on the best method for crop suitability analysis. We conducted a scoping review to evaluate methodological strategies for LSA. Secondary to this, we assessed which of these would be suitable for neglected and underutilised crop species (NUS). The review classified LSA methods reported in articles as traditional (26.6%) and modern (63.4%). Modern approaches, including multi-criteria decision-making (MCDM) methods such as analytical hierarchy process (AHP) (14.9%) and fuzzy methods (12.9%); crop simulation models (9.9%) and machine learning related methods (25.7%) are gaining popularity over traditional methods. The MCDM methods, namely AHP and fuzzy, are commonly applied to LSA while crop models and machine learning related methods are gaining popularity. A total of 67 parameters from climatic, hydrology, soil, socio-economic and landscape properties are essential in LSA. Unavailability and the inclusion of categorical datasets from social sources is a challenge

Crop suitability mapping for underutilized crops in South Africa.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Several neglected and underutilised species (NUS) provide solutions to climate change and create a Zero Hunger world, the Sustainable Development Goal 2. However, limited information describing their agronomy, water use, and evaluation of potential growing zones to improve sustainable production has previously been cited as the bottlenecks to their promotion in South Africa's (SA) marginal areas. Therefore, the thesis outlines a series of assessments aimed at fitting NUS in the dryland farming systems of SA. The study successfully mapped current and possible future suitable zones for NUS in South Africa. Initially, the study conducted a scoping review of land suitability methods. After that, South African bioclimatic zones with high rainfall variability and water scarcity were mapped. Using the analytic hierarchy process (AHP), the suitability for selected NUS sorghum (Sorghum bicolor), cowpea (Vigna unguiculata), amaranth and taro (Colocasia esculenta) was mapped. The future growing zones were used using the MaxEnt model. This was only done for KwaZulu Natal. Lastly, the study assessed management strategies such as optimum planting date, plant density, row spacing, and fertiliser inputs for sorghum. The review classified LSA methods reported in articles as traditional (26.6%) and modern (63.4%). Modern approaches, including multicriteria decision-making (MCDM) methods such as AHP (14.9%) and fuzzy methods (12.9%), crop simulation models (9.9%) and machine-learning-related methods (25.7%), are gaining popularity over traditional methods. The review provided the basis and justification for land suitability analysis (LSA) methods to map potential growing zones of NUS. The review concluded that there is no consensus on the most robust method for assessing NUS's current and future suitability. South Africa is a water-scarce country, and rainfall is undoubtedly the dominating factor determining crop production, especially in marginal areas where irrigation facilities are limited for smallholder farmers. Based on these challenges, there is a need to characterise bioclimatic zones in SA that can be qualified under water stress and with high rainfall variation. Mapping high-risk agricultural drought areas were achieved by using the Vegetation Drought Response Index (VegDRI), a hybrid drought index that integrates the Standardized Precipitation Index (SPI), Temperature Condition Index (TCI), and the Vegetation Condition Index (VCI). In NUS production, land use and land classification address questions such as “where”, “why”, and “when” a particular crop is grown within particular agroecology. The study mapped the current and future suitable zones for NUS. The current land suitability assessment was done using Analytic Hierarchy Process (AHP) using multidisciplinary factors, and the future was done through a machine learning model Maxent. The maps developed can contribute to evidence-based and site-specific recommendations for NUS and their mainstreaming. Several NUS are hypothesised to be suitable in dry regions, but the future suitability remains unknown. The future distribution of NUS was modelled based on three representative concentration pathways (RCPs 2.6, 4.5 and 8.5) for the years between 2030 and 2070 using the maximum entropy (MaxEnt) model. The analysis showed a 4.2-25% increase under S1-S3 for sorghum, cowpea, and amaranth growing areas from 2030 to 2070. Across all RCPs, taro is predicted to decrease by 0.3-18 % under S3 from 2050 to 2070 for all three RCPs. Finally, the crop model was used to integrate genotype, environment and management to develop one of the NUS-sorghum production guidelines in KwaZulu-Natal, South Africa. Best sorghum management practices were identified using the Sensitivity Analysis and generalised likelihood uncertainty estimation (GLUE) tools in DSSAT. The best sorghum management is identified by an optimisation procedure that selects the optimum sowing time and planting density-targeting 51,100, 68,200, 102,500, 205,000 and 300 000 plants ha-1 and fertiliser application rate (75 and 100 kg ha-1) with maximum long-term mean yield. The NUS are suitable for drought-prone areas, making them ideal for marginalised farming systems to enhance food and nutrition security

Exploring Locational Criteria to Optimise Biofuel Production Potential in Nigeria

Energy is one of the important building blocks of any economy and the sustainability of its supply is crucial. Renewable energy sources are being explored with the objective of harnessing their potential to address demand shortages and provide sustainable clean energy. Biofuels, as one of these renewables, continue to expand and their share in global energy consumption continues to increase. Apart from lower net carbon emissions compared to fossil fuels and their role as transitional fuel sources in global shift towards renewable energy, biofuels offer other benefits such as increasing the volume of liquid fuels, improving air quality, expanding trade, import substitution and energy diversification. Therefore, there are strong environmental and economic arguments for the Nigerian Government to embark on deployment of renewable energy, including biofuels. Despite abundant biomass resources, biofuel programmes have not been fully operationalised in the country, partly because biofuels vary in their favourability profiles which depend on local conditions and practices, as well as spatial conflicts between land designed for energy production and other land uses such as agriculture or nature reserves. Consequently, there is a need for robust and detailed approaches to this location-related problem. Although Spatial Multi-criteria Analysis (SMCA) as a support tool has been applied to biofuel production analysis, accounting for multiple stakeholder opinions has been one of the major challenges. In Nigeria, there have been few attempts to apply spatial analysis to locational problems related to biofuel production. In addition, these studies are limited in terms of scope, were based on feedstock other than energy crops, and provided superficial analysis of suitability of the identified sites. The goal of this thesis was to show how to improve the robustness and transparency of spatial analysis in Nigeria through answering some spatial questions about biofuel production, which extends our knowledge of GIS and is relevant to practice. Robustness implies detailed exploration of the required environmental criteria and incorporation of the expert decisions on the criteria preferences. This work transparently demonstrates detailed application of the combined geospatial and multi-criteria methods to make the academic contribution transferable. The technical goal of the work was to conduct spatial optimisation for biofuel production in the country through detailed assessment of environmental criteria, modelling land suitability for cultivating sweet sorghum, sugarcane, cassava, oil palm and jatropha as biofuel crops in Nigeria and modelling optimal sites for biofuel processing and/or blending. This will provide support for spatial decisions regarding establishing biofuel processing plants or expanding the existing ones. Analytical Hierarchy Process (pairwise comparison) was adopted as the multi-criteria analysis method due to its robustness regarding stakeholder inclusion. Weighted overlay was adopted as method of land suitability modelling and supply area modelling was adopted as the method of site optimisation. The analysis showed that northcentral geo-political zone of Nigeria has the largest areas of land that is very suitable for cultivating sugarcane, cassava, oil palm and jatropha, while northeast has the largest areas of land that is very suitable for cultivating sweet sorghum. Based on these, three sizes of service area were considered assuming worst, average and highest crop yields scenarios to optimise processing/blending sites. Existing petroleum depots were considered as the candidate sites. Ilorin petroleum depot was found to be the most optimal location for processing/blending biofuel in Nigeria based on all the crop yields scenarios, within 300 km service area. However, assuming worst case yields scenario within 100 km service area, Maiduguri depot was found to be the best location for sweet sorghum and sugarcane biofuel processing/blending, but Yola depot was suggested as replacement for sugarcane. Ibadan was found to be the best for oil palm and jatropha, but Ikot Abasi depot was suggested as replacement for oil palm. Aba was found to be the best for cassava, but Makurdi was suggested as replacement. This work had demonstrated how robust integration of GIS tools with MCDM techniques could improve the effectiveness of spatial decision-making process regarding positioning biofuel production in developing countries like Nigeria. It is therefore concluded that this work will serve as a point of reference for state-of-the-art application of spatial multi-criteria evaluation analysis, not only for the biofuel industry, but also for other sectors of environmental management such as river basin management, land use or settlement planning. The tendency of a biofuel programme in Nigeria to succeed would greatly be enhanced by adopting sustainability strategies along its value chain through climate smart agriculture, designing and/or adopting a suitable feedstock supply model, effective land use management, realigning policy objectives, enforcing policy directives and balancing between strong and weak sustainability strategies. This will create a conducive environment for stimulating biofuel programme, delivering energy source diversification, economic growth and sustainable development for Nigeria

Exploring Locational Criteria to Optimise Biofuel Production Potential in Nigeria

Energy is one of the important building blocks of any economy and the sustainability of its supply is crucial. Renewable energy sources are being explored with the objective of harnessing their potential to address demand shortages and provide sustainable clean energy. Biofuels, as one of these renewables, continue to expand and their share in global energy consumption continues to increase. Apart from lower net carbon emissions compared to fossil fuels and their role as transitional fuel sources in global shift towards renewable energy, biofuels offer other benefits such as increasing the volume of liquid fuels, improving air quality, expanding trade, import substitution and energy diversification. Therefore, there are strong environmental and economic arguments for the Nigerian Government to embark on deployment of renewable energy, including biofuels. Despite abundant biomass resources, biofuel programmes have not been fully operationalised in the country, partly because biofuels vary in their favourability profiles which depend on local conditions and practices, as well as spatial conflicts between land designed for energy production and other land uses such as agriculture or nature reserves. Consequently, there is a need for robust and detailed approaches to this location-related problem. Although Spatial Multi-criteria Analysis (SMCA) as a support tool has been applied to biofuel production analysis, accounting for multiple stakeholder opinions has been one of the major challenges. In Nigeria, there have been few attempts to apply spatial analysis to locational problems related to biofuel production. In addition, these studies are limited in terms of scope, were based on feedstock other than energy crops, and provided superficial analysis of suitability of the identified sites. The goal of this thesis was to show how to improve the robustness and transparency of spatial analysis in Nigeria through answering some spatial questions about biofuel production, which extends our knowledge of GIS and is relevant to practice. Robustness implies detailed exploration of the required environmental criteria and incorporation of the expert decisions on the criteria preferences. This work transparently demonstrates detailed application of the combined geospatial and multi-criteria methods to make the academic contribution transferable. The technical goal of the work was to conduct spatial optimisation for biofuel production in the country through detailed assessment of environmental criteria, modelling land suitability for cultivating sweet sorghum, sugarcane, cassava, oil palm and jatropha as biofuel crops in Nigeria and modelling optimal sites for biofuel processing and/or blending. This will provide support for spatial decisions regarding establishing biofuel processing plants or expanding the existing ones. Analytical Hierarchy Process (pairwise comparison) was adopted as the multi-criteria analysis method due to its robustness regarding stakeholder inclusion. Weighted overlay was adopted as method of land suitability modelling and supply area modelling was adopted as the method of site optimisation. The analysis showed that northcentral geo-political zone of Nigeria has the largest areas of land that is very suitable for cultivating sugarcane, cassava, oil palm and jatropha, while northeast has the largest areas of land that is very suitable for cultivating sweet sorghum. Based on these, three sizes of service area were considered assuming worst, average and highest crop yields scenarios to optimise processing/blending sites. Existing petroleum depots were considered as the candidate sites. Ilorin petroleum depot was found to be the most optimal location for processing/blending biofuel in Nigeria based on all the crop yields scenarios, within 300 km service area. However, assuming worst case yields scenario within 100 km service area, Maiduguri depot was found to be the best location for sweet sorghum and sugarcane biofuel processing/blending, but Yola depot was suggested as replacement for sugarcane. Ibadan was found to be the best for oil palm and jatropha, but Ikot Abasi depot was suggested as replacement for oil palm. Aba was found to be the best for cassava, but Makurdi was suggested as replacement. This work had demonstrated how robust integration of GIS tools with MCDM techniques could improve the effectiveness of spatial decision-making process regarding positioning biofuel production in developing countries like Nigeria. It is therefore concluded that this work will serve as a point of reference for state-of-the-art application of spatial multi-criteria evaluation analysis, not only for the biofuel industry, but also for other sectors of environmental management such as river basin management, land use or settlement planning. The tendency of a biofuel programme in Nigeria to succeed would greatly be enhanced by adopting sustainability strategies along its value chain through climate smart agriculture, designing and/or adopting a suitable feedstock supply model, effective land use management, realigning policy objectives, enforcing policy directives and balancing between strong and weak sustainability strategies. This will create a conducive environment for stimulating biofuel programme, delivering energy source diversification, economic growth and sustainable development for Nigeria

Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture

Big streams of Earth images from satellites or other platforms (e.g., drones and mobile phones) are becoming increasingly available at low or no cost and with enhanced spatial and temporal resolution. This thesis recognizes the unprecedented opportunities offered by the high quality and open access Earth observation data of our times and introduces novel machine learning and big data methods to properly exploit them towards developing applications for sustainable and resilient agriculture. The thesis addresses three distinct thematic areas, i.e., the monitoring of the Common Agricultural Policy (CAP), the monitoring of food security and applications for smart and resilient agriculture. The methodological innovations of the developments related to the three thematic areas address the following issues: i) the processing of big Earth Observation (EO) data, ii) the scarcity of annotated data for machine learning model training and iii) the gap between machine learning outputs and actionable advice. This thesis demonstrated how big data technologies such as data cubes, distributed learning, linked open data and semantic enrichment can be used to exploit the data deluge and extract knowledge to address real user needs. Furthermore, this thesis argues for the importance of semi-supervised and unsupervised machine learning models that circumvent the ever-present challenge of scarce annotations and thus allow for model generalization in space and time. Specifically, it is shown how merely few ground truth data are needed to generate high quality crop type maps and crop phenology estimations. Finally, this thesis argues there is considerable distance in value between model inferences and decision making in real-world scenarios and thereby showcases the power of causal and interpretable machine learning in bridging this gap.Comment: Phd thesi

An application of GIS and remote sensing for land use evaluation and suitability mapping for yam, cassava, and rice in the Lower River Benue Basin, Nigeria

Agricultural production has contributed over time to food security and rural economic development in developing countries particularly supporting the countryside. Evidence show that crop yields are declining in the Lower River Benue Basin of Nigeria. This study conducted a land use evaluation and suitability mapping for production of yam, cassava and also assessed the possible socioeconomic impediments that may hinder or enhance sustainable agricultural development in the Lower River Benue Basin. The study adopted physical assessments and socioeconomic approach coupled with mapping which incorporated processing of satellite imagery. Statistical methods were used to measure the status, trends, level of dispersion, and relationships between the variables of physical and socioeconomic parameters. Modelling techniques for determining potential impacts assessment, agricultural suitability index, adaptive capacity index, finally producing suitability maps. Geo-informatics processes were used to produce a digital elevation model, land use and land cover map, and normalised difference vegetation index map. The results were thematic maps, weighted percentages of attribute data, and suitability maps produced through weighted overlay. An intensive analysis of climatological data depicted a progressive intensity of rainfall, and a decreasing trend in the number of rain days; a gradual temperature rise; and high relative humidity during the planting season which is about 168 days. Laboratory analysis show that soils in the study area require fertility enhancement with inorganic fertilisers to encourage better crop yield. Results show that the Lower River Benue Basin is suitable for yam, cassava, and rice cultivation as classified on maps of suitable areas. Rice had the highest suitability percentages (38.30%). The study area was found to be moderately suitable for each of the crops examined by more than 40% for each crop. Cassava had the least suitability percentages (34.47%). Evidence suggests that agricultural development in the Lower River Benue Basin is under threat from potential impacts of climate variability and change, population growth, and infectious diseases. The agricultural suitability index of the study area regards the study area as suitable (70.5%) and the adaptive capacity index of the study area was moderate (50.83%), but it was found that serious attention need to be given to farm technology and infrastructure. Mitigation strategies and recommendations which are beneficial to the sustainable development of agriculture have been provided in line with the established characteristics of the Lower River Benue Basin.Environmental SciencesD. Phil. (Environmental Management

Forecasting carrot yield with optimal timing of Sentinel 2 image acquisition

Accurate, non-destructive forecasting of carrot yield is difcult due to its subterranean growing habit. Furthermore, the timing of forecasting usually occurs when the crop is mature, limiting the opportunity to implement alternative management decisions to improve yield (during the growing season). This study aims to improve the accuracy of carrot yield forecasting by exploring time series and multivariate approaches. Using Sentinel-2 satellite imagery in three Australian vegetable regions, we established a time series of carrot phenological stages (PhS) from 'days after sowing' (DAS) to enhance prediction timing. Numerous vegetation indices (VIs) were analyzed to derive temporal growth patterns. Correlations with yield at diferent PhS were established. Although the average root yield (t ha−1) did not signifcantly difer across the regions, the temporal VI signatures, indicating diferent regional crop growth trends, did vary as well as the PhS at when the maximum correlation with yield occurred (PhSR2max) with two of the regions producing a delayed PhSR2max (i.e. 90–130 DAS). The best multivariate model was identifed at 70 DAS, extending the forecasting window before harvest between 20 to 60 days. The performance of this model was validated with new crops producing an average error of 16.9 t ha−1 (27% of total yield). These results demonstrate the potential of the model at such early stage under varying growing conditions ofering growers and stakeholders the chance to optimize farming practices, make informed decisions on selling, harvesting, and labor planning, and adopt precision agriculture methods

UAV and field spectrometer based remote sensing for maize phenotyping, varietal discrimination and yield forecasting.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Maize is the major staple food crop in the majority of Sub-Saharan African (SSA) countries. However, production statistics (croplands and yields) are rarely measured, and where they are recorded, accuracy is poor because the statistics are updated through the farm survey method, which is error-prone and is time-consuming, and expensive. There is an urgent need to use affordable, accurate, timely, and readily accessible data collection and spatial analysis tools, including robust data extraction and processing techniques for precise yield forecasting for decision support and early warning systems. Meeting Africa’s rising food demand, which is driven by population growth and low productivity requires doubling the current production of major grain crops like maize by 2050. This requires innovative approaches and mechanisms that support accurate yield forecasting for early warning systems coupled with accelerated crop genetic improvement. Recent advances in remote sensing and geographical information system (GIS) have enabled detailed cropland mapping, spatial analysis of land suitability, crop type, and varietal discrimination, and ultimately grain yield forecasting in the developed world. However, although remote sensing and spatial analysis afforded us unprecedented opportunities for detailed data collection, their application in maize in Africa is still limited. In Africa, the challenge of crop yield forecasting using remote sensing is a daunting task because agriculture is highly fragmented, cropland is spatially heterogeneous, and cropping systems are highly diverse and mosaic. The dearth of data on the application of remote sensing and GIS in crop yield forecasting and land suitability analysis is not only worrying but catastrophic to food security monitoring and early warning systems in a continent burdened with chronic food shortages. Furthermore, accelerated crop genetic improvement to increase yield and achieve better adaptation to climate change is an issue of increasing urgency in order to satisfy the ever-increasing food demand. Recently, crop improvement programs are exploring the use of remotely sensed data that can be used cost-effectively for varietal evaluation and analysis in crop phenotyping, which currently remains a major bottleneck in crop genetic improvement. Yet studies on evaluation of maize varietal response to abiotic and biotic stresses found in the target production environments are limited. Therefore, the aim of this study was to model spatial land suitability for maize production using GIS and explore the potential use of field spectrometer and unmanned aerial vehicles (UAV) based remotely sensed data in maize varietal discrimination, high-throughput phenotyping, and yield prediction. Firstly, an overview of major remote-sensing platforms and their applicability to estimating maize grain yield in the African agricultural context, including research challenges was provided. Secondly, maize land suitability analysis using GIS and analytical hierarchical process (AHP) was performed in Zimbabwe. Finally, the utility of proximal and UAV-based remotely sensed data for maize phenotyping, varietal discrimination, and yield forecasting were explored. The results showed that the use of remote sensing data in estimating maize yield in the African agricultural systems is still limited and obtaining accurate and reliable maize yield estimates using remotely sensed data remains a challenge due to the highly fragmented and spatially heterogeneous nature of the cropping systems. Our results underscored the urgent need to use sensors with high spatial, temporal and spectral resolution, coupled with appropriate classification techniques and accurate ground truth data in estimating maize yield and its spatiotemporal dynamics in heterogeneous African agricultural landscapes for designing appropriate food security interventions. In addition, using modern spatial analysis tools is effective in assessing land suitability for targeting location-specific interventions and can serve as a decision support tool for policymakers and land-use planners regarding maize production and varietal placement. Discriminating maize varieties using remotely sensed data is crucial for crop monitoring, high throughput phenotyping, and yield forecasting. Using proximal sensing, our study showed that maize varietal discrimination is possible at certain phenological growth stages at the field level, which is crucial for yield forecasting and varietal phenotyping in crop improvement. In addition, the use of proximal remote sensing data with appropriate pre-processing algorithms such as auto scaling and generalized least squares weighting significantly improved the discrimination ability of partial least square discriminant analysis, and identify optimal spectral bands for maize varietal discrimination. Using proximal sensing was not only able to discriminate maize varieties but also identified the ideal phenological stage for varietal discrimination. Flowering and onset of senescence appeared to be the most ideal stages for accurate varietal discrimination using our data. In this study, we also demonstrated the potential use of UAV-based remotely sensed data in maize varietal phenotyping in crop improvement. Using multi-temporal UAV-derived multispectral data and Random Forest (RF) algorithm, our study identified not only the optimal bands and indices but also the ideal growth stage for accurate varietal phenotyping under maize streak virus (MSV) infection. The RF classifier selected green normalized difference vegetation index (GNDVI), green Chlorophyll Index (CIgreen), Red-edge Chlorophyll Index (CIred-edge), and the Red band as the most important variables for classification. The results demonstrated that spectral bands and vegetation indices measured at the vegetative stage are the most important for the classification of maize varietal response to MSV. Further analysis to predict MSV disease and grain yield using UAV-derived multispectral imaging data using multiple models showed that Red and NIR bands were frequently selected in most of the models that gave the highest prediction precision for grain yield. Combining the NIR band with Red band improved the explanatory power of the prediction models. This was also true with the selected indices. Thus, not all indices or bands measure the same aspect of biophysical parameters or crop productivity, and combining them increased the joint predictive power, consequently increased complementarity. Overall, the study has demonstrated the potential use of spatial analysis tools in land suitability analysis for maize production and the utility of remotely sensed data in maize varietal discrimination, phenotyping, and yield prediction. These results are useful for targeting location-specific interventions for varietal placement and integrating UAV-based high-throughput phenotyping systems in crop genetic improvement to address continental food security, especially as climate change accelerates