Assessing the influence of land-use change on the variability of soil chemical properties in semi-arid zone of Ghana

The study aimed at providing basis to consider spatial variability in soil fertility to inform effective decision making in mineral fertilizer recommendations. The study area was classified into six land-use categories using a rural rapid appraisal technique with the aid of the farmers in the community, and by remote sensing satellite imagery (Quick bird). Land-use categories varied significantly in their nutrient, organic carbon content, and stock with coefficient of determination ranging from 0.14 to 0.46. This is reflected in decline in soil nutrient, organic carbon content, and stock with increasing number of years the land was put under cultivation except for permanently cultivated fields. Permanantly cultivated fields were located in the homestead and benefited from nutrient imports from the bush fields. The two farm types also differed significantly with respect to soil nutrients, organic carbon and stock. Soil organic carbon content was 8.2 and 4.5 g kg-1 for the homestead and bushfields, respectively. Soil organic carbon stock estimated for the two farm types were 125 and 74 t ha-1 for the homestead and bush fields, respectively. The study shows a distinct zone of nutrient enhancement within the homestead and bushfields that should be sampled separately when sampling the zone for fertilizer recommendations

Evaluation of soil properties of the Sudan Savannah ecological zone of Ghana for crop production

Low soil fertility and limited ability of farmers to purchase fertilizers in the Sudan savannah zone of Ghana has resulted in the decline in the yield of cereals over the years. There is, therefore, the need to identify soil parameters that are critical to crop production, to manage them effectively and improve fertilizer use efficiency to increase crop yield. To achieve this, an area of about 1.5 km2 was divided into grid cells (100m2) and characterised for their soil properties (organic carbon, pH, and soil texture). Data collected was used in a pedo-transfer function to estimate additional soil parameters that were not measured (i.e. wilting point, field capacity, available water and saturation). These were used as input to the crop simulation model (APSIM- Agriculture Productions Systems sIMulator) to simulate sorghum grain yield for each grid cell. Linear regression and factor analysis were also employed in explaining the data. Grain yield ranged from 402 to 1092 kg ha-1 with a mean of 673 kg ha-1 using 2005 weather data and 228 to 907 kg ha-1 with a mean of 427 kg ha-1 using 2000 weather data without fertilizer application. The model was sensitive to all input parameters. Soil texture and organic carbon were identified to have significant effect on crop yield. Soil organic carbon is, therefore, to be managed for the development of a good tilth and hence sustainable yields of sorghum at the study site

Mode of Biochar Application to Vertisols Influences Water Balance Components and Water Use Efficiency of Maize (Zea mays L.)

Vertisols belong to a group of soils with high fertility but poor physical properties of swelling when wet and shrinking and cracking when dry. The swelling inhibits infiltration, resulting in flooding, limiting the production of upland crops. Biochar (<BC) application has been shown to reduce the shrink-swell behaviour of Vertisols. However, the mode of biochar application to these soils may affect the effectiveness of the amendment. This study investigated the water relations and maize (Zea mays L.) growth under two BC application modes: (i) biochar applied into cracks that develop with drying, C, and (ii) biochar that was surface broadcast and incorporated into the topsoil, FM. A control treatment did not receive any BC amendment. Maize was grown on the BC-amended Vertisols using the two modes of application in a greenhouse under two seasonal water regimes of 610 and 450 mm. The results showed that the proportion of total water application lost to runoff was 37%, 49% and 53% for C, FM and control treatments, respectively. Both maize yield and Water Use Efficiency (WUE), for the C treatments were significantly (p < 0.05) higher than those for FM treatments. The maize yield under the C treatments was 19% over the control. Similarly, the WUE for the C treatments was 28% above the control treatment. It is concluded that the application of biochar into cracks is a more effective way of improving the water relations and upland crop productivity and WUE in Vertisols than the traditional surface incorporation

Using CERES-maize and ENSO as decision support tools to evaluate climate-sensitive farm management practices for maize production in the northern regions of Ghana

Open Access JournalMaize (Zea mays) has traditionally been a major cereal staple in southern Ghana. Through breeding and other crop improvement efforts, the zone of cultivation of maize has now extended to the northern regions of Ghana which, hitherto, were the home to sorghum and millet as the major cereals. Maize yield in the northern Ghana is hampered by three major biophysical constraints, namely, poor soil fertility, low soil water storage capacity and climate variability. In this study we used the DSSAT crop model to assess integrated water and soil management strategies that combined the pre-season El-Niño-Southern Oscillation (ENSO)-based weather forecasting in selecting optimal planting time, at four locations in the northern regions of Ghana. It could be shown that the optimum planting date for a given year was predictable based on February-to-April (FMA) Sea Surface Temperature (SST) anomaly for the locations with R2 ranging from 0.52 to 0.71. For three out of four locations, the ENSO-predicted optimum planting dates resulted in significantly higher maize yields than the conventional farmer selected planting dates. In Wa for instance, early optimum planting dates were associated with La Nina and El Niño (Julian Days 130-150; early May to late May) whereas late planting (mid June to early July) was associated with the Neutral ENSO phase. It was also observed that the addition of manure and fertilizer improved soil water and nitrogen use efficiency, respectively, and minimized yield variability, especially when combined with weather forecast. The use of ENSO-based targeted planting date choice together with modest fertilizer and manure application has the potential to improve maize yields and also ensure sustainable maize production in parts of northern Ghana

A Mathematical Investigation of Vaccination Strategies to Prevent a Measles Epidemic

The purpose of this project is to quantitatively investigate vaccination strategies to prevent measles epidemics. A disease model which incorporates susceptible, vaccinated, infected, and recovered populations (SVIR) is used to investigate the process of how an epidemic of measles can spread within a closed population where a portion of the population has been vaccinated. The model is used to predict the number of infections and resulting reproductive number for the measles based on a variety of initial vaccination levels.  The model is further used to investigate the concept of herd immunity, which states that if a certain percentage of the population is vaccinated then it will provide protection for the entire population.  Results generated from these modeling efforts suggest that approximately 95\% of the population should be vaccinated against the measles in order to establish a herd immunity

Weather-index based crop insurance as a social adaptation to climate change and variability in the Upper West Region of Ghana: Developing a participatory approach

Climate change and variability are major challenges to rain-fed crop production in Africa. This paper presents a report on a pilot project to test a concept for operationalizing weatherindex crop insurance as a social adaptation to the climate change and variability problem in the Upper West Region of Ghana. An analysis of long-term weather variables showed rising temperature of 1.7 oC over a period of 53 years as well as major shifts in rainfall patterns. Farmers face a new reality that cannot be addressed with their indigenous knowledge alone. The weather-index based crop insurance concept discussed herein was developed by combined effort of University of Ghana, the German International Cooperation (GIZ) and the Ghana National Insurance Commission (NIC) since 2010. This development was carried out via their filial, the Ghana Agricultural Insurance Pool (GAIP). The proposed concept sought to link various agricultural stakeholders such weather technical persons, farmers, agricultural extension officer, input dealers and other aggregators, and financial institutions as well as the insurance industry and focused on a participatory farmer led approach. The piloting of the concept was supported by the Climate Change and Food Security (CCAFs) project and was tested in the years 2012 and 2013 using a theatrical drama sketch in two districts in the Upper West Region of Ghana: Jirapa and Lawra. It was observed that training of farmers in the basic principles of weather (data collection, interpretation, etc.) facilitated the discussions on drought insurance, adding to the body of evidence supporting participatory design tools. The aim of this paper is to record this process and to put the results into recent context, through discussing them through the lens of insurance operations and research in Ghana. Ensuing discussions showed that although all stakeholders considered the participatory design tools to be meritorious, a number of logistical challenges were identified that need to be addressed for effective scaling. The study also highlighted the high spatial variability of rainfall in the Upper West region of Ghana, showing the necessity of satellite-derived rainfall products. Finally, the framework suggested in this report highlights the complexity and the institutional structures required to implement an effective insurance. In effect, our simple study has exposed the complexities and intricacies that must be overcome in establishing a sustainable insurance scheme in Ghana

Location and Land use effects on Soil Carbon Accretion and Productivity in the Coastal Savanna Agro-ecological Zone of Ghana

Land use type, climate and soil properties are major determinants of soil carbon storage and productivity, especially in low-input agriculture. In this study, we investigated the interactions among these factors at four (4) locations, namely Accra Metropolis, Ga West, Ga East and Shai Osudoku, within the Coastal-Savannah agro-ecological zone of Ghana. The land use types were maize-based cropping, cassava-based cropping, woodlot/plantations and natural forests. The impact of these on soil productivity at a given location was assessed in terms of soil carbon stocks and a Soil Productivity Index (SPI). The SPI is a composite value derived from routine soil properties such as: soil texture, available water capacity, pH, cation exchange capacity, soil organic carbon, available P, exchangeable K, potentially mineralizable nitrogen, and basic cations, among others. Principal component analysis was used to select soil properties that were used to estimate SPI. The results showed that the locations differed with respect to rainfall regimes and soil types. Locations with slightly heavier soil texture and relatively higher rainfall regimes (Ga East and Shai Osudoku) had significantly higher soil carbon storage and SPI values than the lighter soil textured locations (Accra Metropolis and Ga West). With regards to land use, forest had significantly higher soil carbon storage and SPI than all the other land use types, irrespective of location. The order of soil carbon storage and SPI were: forest > woodlot/plantation > cassava > maize. It was observed that though the Accra Metropolis location hosted the oldest forest, soil carbon was still low, apparently due to the lighter soil texture. We concluded that the soil productivity restorative ability is an interactive effect of carbon management (land use), soil texture and other properties. This interaction hitherto has not been adequately investigated, especially in low-input agriculture

Artificial intelligence in lung cancer diagnostic imaging: a review of the reporting and conduct of research published 2018–2019

Objective: This study aimed to describe the methodologies used to develop and evaluate models that use artificial intelligence (AI) to analyse lung images in order to detect, segment (outline borders of), or classify pulmonary nodules as benign or malignant. Methods: In October 2019, we systematically searched the literature for original studies published between 2018 and 2019 that described prediction models using AI to evaluate human pulmonary nodules on diagnostic chest images. Two evaluators independently extracted information from studies, such as study aims, sample size, AI type, patient characteristics, and performance. We summarised data descriptively. Results: The review included 153 studies: 136 (89%) development-only studies, 12 (8%) development and validation, and 5 (3%) validation-only. CT scans were the most common type of image type used (83%), often acquired from public databases (58%). Eight studies (5%) compared model outputs with biopsy results. 41 studies (26.8%) reported patient characteristics. The models were based on different units of analysis, such as patients, images, nodules, or image slices or patches. Conclusion: The methods used to develop and evaluate prediction models using AI to detect, segment, or classify pulmonary nodules in medical imaging vary, are poorly reported, and therefore difficult to evaluate. Transparent and complete reporting of methods, results and code would fill the gaps in information we observed in the study publications. Advances in knowledge: We reviewed the methodology of AI models detecting nodules on lung images and found that the models were poorly reported and had no description of patient characteristics, with just a few comparing models’ outputs with biopsies results. When lung biopsy is not available, lung-RADS could help standardise the comparisons between the human radiologist and the machine. The field of radiology should not give up principles from the diagnostic accuracy studies, such as the choice for the correct ground truth, just because AI is used. Clear and complete reporting of the reference standard used would help radiologists trust in the performance that AI models claim to have. This review presents clear recommendations about the essential methodological aspects of diagnostic models that should be incorporated in studies using AI to help detect or segmentate lung nodules. The manuscript also reinforces the need for more complete and transparent reporting, which can be helped using the recommended reporting guidelines

Formal Derivation of Concurrent Garbage Collectors

Concurrent garbage collectors are notoriously difficult to implement correctly. Previous approaches to the issue of producing correct collectors have mainly been based on posit-and-prove verification or on the application of domain-specific templates and transformations. We show how to derive the upper reaches of a family of concurrent garbage collectors by refinement from a formal specification, emphasizing the application of domain-independent design theories and transformations. A key contribution is an extension to the classical lattice-theoretic fixpoint theorems to account for the dynamics of concurrent mutation and collection.Comment: 38 pages, 21 figures. The short version of this paper appeared in the Proceedings of MPC 201