Soil quality and soil fertility status in major soil groups at the Tombel area, South-West Cameroon

Open Access Journal; Published online: 27 Feb 2020Among the greatest challenges of Sub-Saharan Africa is the need for more crop production for supplying the increasing demand of its growing population. For this purpose, knowledge on soil resources and their agricultural potentials is important for defining proper and appropriate land use and management. We thus investigated on the status of soil fertility in Tombel area, in order to produce such knowledge through understanding and monitoring the impact of physicochemical properties of soil. Diverse analyses performed on various datasets demonstrated the direct impact of physicochemical properties of soil and derived soil fertility parameters on major constraints for plant growth and optimal crop production such as water retention capacity, roots development, soils aeration, nutrients availability, nutrients abundance and cations balance. Based on physicochemical soil properties, fertility parameters and Soil Quality Index (SQI), four soil fertility classes were identified in the area: (i) very good fertility soils (66 km2) that corresponds to Dystric Vitric Andosols (Melanic) above 500m asl; (ii) good fertility soils (506 km2), grouping Dystric Vitric Andosols (Melanic) below 500m asl and Leptic Fragic Umbrisols; (iii) fairly good fertile soils (787 km2) including Dystric Fragic Cambisols (Humic), Rhodic Acrisols (Cutanic Humic), Fragic Umbrisols (Arenic), and Mollic Ferralsols (Eutric Humic); (iv) poorly fertile soils (375 km2) including Umbric Andosols (Fragic) and Umbric Pisoplinthic Plinthosols (Haplic Dystric). The principal indicators controlling soil quality in the Tombel area as derived from ANOVA and PCA analyses, are: Ca, Mg, pH water, organic matter (OM), available P, total Nitrogen and CEC. Four of the seven indicators (Ca, pH, OM, P) were also identified as important indicators for assessing the fertility status of the different soils groups in the Tombel area

Quantified soil evolution under shifting agriculture in southern Cameroon

Open Access JournalIn the tropical rain forest zone of Southern Cameroon, shifting cultivation and perennial plantations of cocoa are the main farming systems practiced by small-scale farmers to ensure subsistence food crop production and a small income. This research used scientific modeling tools to produce quantitative information on the evolution of soils under this shifting agricultural system. An analysis of farming system led to the development of a conceptual model of the spatio-temporal dynamics of shifting agriculture, including transition matrices of rotational cycles that guided the sampling strategy for the study of soil evolution under the system. The study of soil variability showed that 30–35% of the total variance of some topsoil (0–20 cm) properties was due to the influence of land use practices. Five soil properties (pH, calcium, available phosphorus, bulk density and organic carbon) that are the most sensitive to these agricultural practices were empirically modeled and linear/quadratic fractional rational functions were successfully fitted to time series soil variables to derive quantitative measures on temporal changes in soil with land use. Data and methods produced are useful for soil quality assessment and spatio-temporal dynamic simulation in order to guide decision-making for sustainable land-use planning and soil resources management

Growth response of Moringa oleifera Lam. as affected by various amounts of compost under greenhouse conditions

Published online: 15 Dec 2017The study was carried out to assess the effect of domestic animal composts on growth performance of potted moringa. The study was conducted in the greenhouse for 55 days. Various quantities of composts (100–300 g) added to 800 g of soil were applied. A Randomized Complete Block Design (RCBD) with 14 treatments (each of which was replicated 12 times) was used, giving a total of 168 experimental units. Plant height, stem diameter, leave length and number of leaves were assessed for each treatment. Results indicate that cow dung compost (100 g, 200 g and 300 g) significantly increased the stem diameter of moringa by respectively 33.09%, 33.09%, and 29.93 as compared to that of the control 55 days after in the greenhouse. An increase in the number of leaves by 48.54% due to application of 100 g cow manure compost was observed after 40 days compared to the control. There were significant differences between treatments effects (p < .05) on all the parameters. Organic amendments in general and cow dung compost in particular could constitute the best fertilizer to improve growth moringa in pots

African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning

Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at five spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples (N ≈ 150, 000) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc(Zn)—silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the mlr (Machine Learning in R) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images—SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature—however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmental programs, or targeting of nutrition interventions

Looking back and moving forward: 50 years of soil and soil fertility management research in sub-Saharan Africa

Article purchased; Published online: 02 Nov 2017Low and declining soil fertility has been recognized for a long time as a major impediment to intensifying agriculture in sub-Saharan Africa (SSA). Consequently, from the inception of international agricultural research, centres operating in SSA have had a research programme focusing on soil and soil fertility management, including the International Institute of Tropical Agriculture (IITA). The scope, content, and approaches of soil and soil fertility management research have changed over the past decades in response to lessons learnt and internal and external drivers and this paper uses IITA as a case study to document and analyse the consequences of strategic decisions taken on technology development, validation, and ultimately uptake by smallholder farmers in SSA. After an initial section describing the external environment within which soil and soil fertility management research is operating, various dimensions of this research area are covered: (i) ‘strategic research’, ‘Research for Development’, partnerships, and balancing acts, (ii) changing role of characterization due to the expansion in geographical scope and shift from soils to farms and livelihoods, (iii) technology development: changes in vision, content, and scale of intervention, (iv) technology validation and delivery to farming communities, and (v) impact and feedback to the technology development and validation process. Each of the above sections follows a chronological approach, covering the last five decades (from the late 1960s till today). The paper ends with a number of lessons learnt which could be considered for future initiatives aiming at developing and delivering improved soil and soil fertility management practices to smallholder farming communities in SSA