Integration of Agro-Ecological and Groundwater Resources for the Assessment of Crop Suitability Potential Modeling: The Case of Limpopo Province, South Africa

A comprehensive subtropical fruit potential model (IUM) was established through novel integration of groundwater resources with multi-criterion predictive parameters. Equal-weights overlay was applied to reclassified and ranked rasters to institute IUM. Avocado and litchi had the least spatial extent that concealed the micro-climatic zones of high rainfall (>1000 mmpa) in Vhembe, Mopane, and Waterberg districts; meanwhile, mango and citrus were the crops with the most extensive province-wide distribution. Subsequent potential was apportioned in these sequences by constituency: Waterberg (1719019 ha), Mopane (977741 ha), Vhembe (764044 ha), Capricorn (579506 ha), and Sekhukhune (379968 ha). The IUM resulted in the demarcation of 8.7 million ha to produce the selected crops, which reflected an increase of 7.7 million from the rainfed suitability model. The integrated model would result in the creation of 10.87 million direct employments. The IUM expanded the agrarian sector with positive spinoffs for agribusiness development.

Design and Construction of Crop Suitability Prediction System Using Fuzzy Logic Classifier Method

The potential of land in Indonesia which is quite large and has not been used optimally is one of the problems. this study focused on developing fuzzy logic models to predict plants that are suitable for planting on agricultural land to enable the land use more optimal. In conducting this study, there were two stages of implementation, namely hardware design, and software design which included system workflow design using the Fuzzy Logic Classifier method where three input variables were used, namely soil pH, soil temperature, and humidity. The findings of this study are in the form of predictions consisting of eight outputs, namely Unfavorable Land, Spinach, Cayenne Pepper, Beans, Long Beans, Cucumber, Eggplant, and Tomatoo. The prediction results generated were directly displayed on the LCD of the instrument that has been designed.

Analysis of Rainfall and Temperature Changes and Variability over Glen Farm, South Africa

In the agricultural domain, decision-making is greatly guided by agricultural meteorology, which is the science that applies knowledge of weather and climate to qualitative and quantitative improvement in agricultural efficiency. The study area is challenged with increasing multifaceted agricultural production risks and complex agricultural ecosystems, which require analysis and understanding of local rainfall and temperature patterns. Digital technologies, such as the automatic weather station, play a pivotal role to monitor the physical environment, successively. This study engaged on a thorough analysis and interpretation of long-term rainfall and temperature data. The results would enable farmers and other users to comprehend valuable knowledge for improved productivity. The objectives of this paper were to analyse long-term climate data for Glen automatic weather station. To determine decadal climate patterns and trends, determine seasonal shifts, climate variability and climate change and quantify the frequency of the occurrence of weather extremes and develop suitable adaptation strategies relating to agronomic, phenological and physiological data necessary for crop modelling, operational evaluation and statistical analysis. The applied methods entailed Microsoft Excel and INSTAT Plus statistical software, which used to detect the interactions of environmental factors and suitable agricultural productivity. Understanding of rainfall and temperature patterns is required for agricultural management decisions, on planting date selection, crop suitability, livestock adaptation, ecosystem conservation. Agro meteorological knowledge derived from meteorological parameters, temperature, rainfall, wind and weather extremes, and may enhance agricultural productivity. Analysis of long-term and decadal trends in the time series indorse a sequence of alternately increasing and decreasing in mean annual rainfall and air temperature in Glen Farm.

Automated Mapping for Long-Term Analysis of Shifting Cultivation in Northeast India

Assessment of the spatio-temporal dynamics of shifting cultivation is important to understand the opportunities for land restoration. The past studies on shifting cultivation mapping of North-East (NE) India lack systematic assessment techniques. We have developed a decision tree-based multi-step threshold (DTMT) method for consistent and long-term mapping of shifting cultivation using Landsat data from 1975 to 2018. Widely used vegetation indices such as normalized difference vegetation index (NDVI), Normalized Burn Ratio (NBR) and its relative difference NBR (RdNBR) were integrated with the suitable thresholds in the classification, which yielded overall accuracy above 85%. A significant decrease in total shifting cultivation area was observed with an overall reduction of 75% from 1975–1976 to 2017–2018. The methodology presented in this study is reproducible with minimal inputs and can be useful to map similar changes by optimizing the index threshold values to accommodate relative differences for other landscapes. Furthermore, the crop-suitability maps generated by incorporating climate and soil factors prioritizes suitable land use of shifting cultivation plots. The Google Earth Engine (GEE) platform was employed for automatic mapping of the shifting cultivation areas at desired time intervals for facilitating seamless dissemination of the map products. Besides the novel DTMT method, the shifting cultivation and crop-suitability maps generated in this study, can aid in sustainable land management.

Climate change and agriculture in Burkina Faso

The impacts of climate change (CC) are expected to be higher in developing countries (e.g. Sub-Saharan Africa). However, these impacts will depend on agriculture development and resilience. Therefore, this paper provides a comprehensive analysis of the multifaceted relationships between CC and agriculture in Burkina Faso (BF). A search performed in March 2020 on the Web of Science yielded 1,820 documents and 217 of them were included in the systematic review. The paper provides an overview on both bibliometrics (e.g. journals, authors, institutions) and topics addressed in the literature viz. agriculture subsectors, climate trends in BF, agriculture and CC mitigation (e.g. agriculture-related emissions, soil carbon sequestration), impacts of CC on agriculture (e.g. natural resources, crop suitability, yields, food security) as well as adaptation strategies. BF is experiencing CC as evidenced by warming and an increase in the occurrence of climate extremes. The literature focuses on crops, while animal husbandry and, especially, fisheries are often overlooked. Moreover, most of the documents deal with CC adaptation by the Burkinabe farmers, pastoralists and rural populations. Analysed adaptation options include conservation agriculture and climate-smart agriculture, irrigation, crop diversification, intensification, livelihoods diversification and migration. However, the focus is mainly on agricultural and individual responses, while livelihoods strategies such as diversification and migration are less frequently addressed. Further research is needed on the dual relation between agriculture and CC to contribute to the achievement of the Sustainable Development Goals. Research results are crucial to inform policies aimed at CC mitigation and/or adaptation in rural BF.

Evaluating the Influence of Soil and Environmental Parameters in Terms of Crop Suitability using Machine Learning

Background: The key source of income in India is agriculture, so farming is called as backbone of Indian economy. To satisfy the need of increasing population increase in the crop yield is very important. India country programming framework stated that, the annual soil loss in India is about 5.3 billion tonnes. Methods: Majority farmers are small or marginal scale and are dependent on natural resources like soil-quality, rainfall and environmental condition etc. for their yield. Based on experience farmers decide which crop to be adopted. Government is arranging trainings and exhibitions to enhance the skillset of farmers. Result: A land which gives poor yield for one crop may produce adequate yield for some other crop/crops. To know the possible suitable crop/crops proposed machine learning model focuses current and potential suitability evaluation for available scenario.

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. Using big data and Internet of Things (IoT) improves the accuracy and reliability of LSA methods. The review expects to provide researchers and decision-makers with the most robust methods and standard parameters required in developing LSA for NUS. Qualitative and quantitative approaches must be integrated into unique hybrid land evaluation systems to improve LSA.

Multi-criteria suitability analysis for neglected and underutilised crop species in South Africa

Several neglected and underutilised species (NUS) provide solutions to climate change and creating a Zero Hunger world, the Sustainable Development Goal 2. Several NUS are drought and heat stress-tolerant, making them ideal for improving marginalised cropping systems in drought-prone areas. However, owing to their status as NUS, current crop suitability maps do not include them as part of the crop choices. This study aimed to develop land suitability maps for selected NUS [sorghum, (Sorghum bicolor), cowpea (Vigna unguiculata), amaranth and taro (Colocasia esculenta)] using Analytic Hierarchy Process (AHP) in ArcGIS. Multidisciplinary factors from climatic, soil and landscape, socio-economic and technical indicators overlaid using Weighted Overlay Analysis. Validation was done through field visits, and area under the curve (AUC) was used to measure AHP model performance. The results indicated that sorghum was highly suitable (S1) = 2%, moderately suitable (S2) = 61%, marginally suitable (S3) = 33%, and unsuitable (N1) = 4%, cowpea S1 = 3%, S2 = 56%, S3 = 39%, N1 = 2%, amaranth S1 = 8%, S2 = 81%, S3 = 11%, and taro S1 = 0.4%, S2 = 28%, S3 = 64%, N1 = 7%, of calculated arable land of SA (12 655 859 ha). Overall, the validation showed that the mapping exercises exhibited a high degree of accuracies (i.e. sorghum AUC = 0.87, cowpea AUC = 0.88, amaranth AUC = 0.95 and taro AUC = 0.82). Rainfall was the most critical variable and criteria with the highest impact on land suitability of the NUS. Results of this study suggest that South Africa has a huge potential for NUS production. The maps developed can contribute to evidence-based and site-specific recommendations for NUS and their mainstreaming. Also, the maps can be used to design appropriate production guidelines and to support existing policy frameworks which advocate for sustainable intensification of marginalised cropping systems through increased crop diversity and the use of stress-tolerant food crops.