Geographic priorities for research and development on dryland cereals and legumes

Dryland cereal and legume crops have often received less attention than maize, wheat and rice in terms of research and development priorities. But these crops are important globally because they serve populations living in poverty and particular socioeconomic and environmental niches. Compared to other crops, less is known about the global distribution of dryland cereal and legume crops and the conditions where they are grown. This research reports on an international effort to compile geographic information on cereal and legume crops and the conditions under which they are cultivated.. The study suggested that dryland cereal and legume crops should be given priority in 18 farming systems worldwide, representing 160 million ha. The priority regions include the drier areas of South Asia, West and East Africa, Middle East and North Africa, Central America and other parts of Asia. These regions are prone to drought and heat stress, among other biotic and abiotic constraints. They represent 60% of the global poor and malnourished and make up half of the global population

Investigating the use of high resolution multi-spectral satellite imagery for crop mapping in Nigeria crop and landuse classification using WorldView-3 high resolution multispectral imagery and LANDSAT8 data

Imagery from recently launched high spatial resolution WorldView-3 offers new opportunities for crop identification and landcover assessment. Multispectral WorldView-3 at 1.6m spatial resolution and LANDSAT8 images covering an extent of 100Km² in humid ecology of Nigeria were used for crop and landcover identification. Three supervised classification techniques (maximum likelihood(MLC), Neural Net clasifier(NNC) and support vector machine(SVM)) were used to classify WorldView-3 and LANDSAT8 into four crop classes and seven non-crop classes. For accuracy assessment, kappa coefficient, producer and user accuracies were used to evaluate the performance of all three supervised classifiers. NNC performed best with an overall accuracy(OA) of 92.20, kappa coefficient(KC) of 0.83 in landcover identification using WorldView-3. This was closely followed by SVM with an OA of 91.77%, KC of 0.83. MLC performed slightly lower at an OA of 91.25% and KC of 0.82. Classification of crops and landcover with LANDSAT8 was best with MLC classifier with an OA of 92.12% , KC of 0.89. Cassava at younger than 3 months old could not be identified correctly by all classifiers using WorldView-3 and LANDSAT8 products. In summary WorldView-3 and LANDSAT8 data had satisfactory performance in identifying different crop and landcover types though at varying degrees of accuracies

Regression Modeling of Gross Domestic Products

This research work is structured towards modeling the GDP of Nigeria basing on the major sectors of economy (Agriculture, Manufacturing Sector, Trade and Services) with a view to  determine the contribution of different Sectors of Economy to the Nigeria Economic Growth .The multivariate regression model is used to determine relationship between dependent variable and explanatory variables .Global validation Test is adopted to diagnose the autocorrelation and multicollinearity and the Studentized Breusch-Pagan test is applied to diagnose the presence of heteroscedasticity and its remedies which leading to building a predictive model. DOI: 10.7176/MTM/14-1-03 Publication date: April 30th 202

A multicriteria GIS site selection for sustainable cocoa development in West Africa: a case study of Nigeria

Cocoa occupies 6 million ha in coastal humid West Africa where 70% of world supply is grown, 90% on 2 million family farms, of 2 ha or less. Here, at least 16 million people mostly depend on cocoa but earn only $100/person/year from the crop. There is need to optimize the farming system, minimize the environmental impact of technologies, and improve socio-economic dynamics. This study identifies areas with potential for intensified cocoa farming and where maximum impact to household income could be achieved without deforestation. The selection involves defining suitability criteria, preparing an inventory of available data, determining suitability based on identified criteria, and combining suitability into hierarchical preferences based on weights proposed by local experts. GIS and Multi-Criteria land Evaluation technique using biophysical, socioeconomic, and demographic variables were employed in selection. Nineteen administrative units (20,000 km²) were selected in Nigeria where the intervention project could be implemented

A Simple Algorithm for Large-Scale Mapping of Evergreen Forests in Tropical America, Africa and Asia

The areal extent and spatial distribution of evergreen forests in the tropical zones are important for the study of climate, carbon cycle and biodiversity. However, frequent cloud cover in the tropical regions makes mapping evergreen forests a challenging task. In this study we developed a simple and novel mapping algorithm that is based on the temporal profile analysis of Land Surface Water Index (LSWI), which is calculated as a normalized ratio between near infrared and shortwave infrared spectral bands. The 8-day composites of MODIS Land Surface Reflectance data (MOD09A1) in 2001 at 500-m spatial resolution were used to calculate LSWI. The LSWI-based mapping algorithm was applied to map evergreen forests in tropical Africa, America and Asia (30°N–30°S). The resultant maps of evergreen forests in the tropical zone in 2001, as estimated by the LSWI-based algorithm, are compared to the three global forest datasets [FAO FRA 2000, GLC2000 and the standard MODIS Land Cover Product (MOD12Q1) produced by the MODIS Land Science Team] that are developed through complex algorithms and processes. The inter-comparison of the four datasets shows that the area estimate of evergreen forest from the LSWI-based algorithm fall within the range of forest area estimates from the FAO FRA 2000, GLC2000 and MOD12Q1 at a country level. The area and spatial distribution of evergreen forests from the LSWI-based algorithm is to a large degree similar to those of the MOD12Q1 produced by complex mapping algorithms. The results from this study demonstrate the potential of the LSWI-based mapping algorithm for large-scale mapping of evergreen forests in the tropical zone at moderate spatial resolution

A Simple Algorithm for Large-Scale Mapping of Evergreen Forests in Tropical America, Africa and Asia

The areal extent and spatial distribution of evergreen forests in the tropical zones are important for the study of climate, carbon cycle and biodiversity. However, frequent cloud cover in the tropical regions makes mapping evergreen forests a challenging task. In this study we developed a simple and novel mapping algorithm that is based on the temporal profile analysis of Land Surface Water Index (LSWI), which is calculated as a normalized ratio between near infrared and shortwave infrared spectral bands. The 8-day composites of MODIS Land Surface Reflectance data (MOD09A1) in 2001 at 500-m spatial resolution were used to calculate LSWI. The LSWI-based mapping algorithm was applied to map evergreen forests in tropical Africa, America and Asia (30°N–30°S). The resultant maps of evergreen forests in the tropical zone in 2001, as estimated by the LSWI-based algorithm, are compared to the three global forest datasets [FAO FRA 2000, GLC2000 and the standard MODIS Land Cover Product (MOD12Q1) produced by the MODIS Land Science Team] that are developed through complex algorithms and processes. The inter-comparison of the four datasets shows that the area estimate of evergreen forest from the LSWI-based algorithm fall within the range of forest area estimates from the FAO FRA 2000, GLC2000 and MOD12Q1 at a country level. The area and spatial distribution of evergreen forests from the LSWI-based algorithm is to a large degree similar to those of the MOD12Q1 produced by complex mapping algorithms. The results from this study demonstrate the potential of the LSWI-based mapping algorithm for large-scale mapping of evergreen forests in the tropical zone at moderate spatial resolution