Using the VS‐Fast methodology for soil degradation assessment: a case study from Senegal

Soil degradation threatens sustainable food production and accelerates global warming. Poorer countries, whose agricultural sectors are highly dependent on their natural resource bases, are hit particularly hard by declining soil productivity. Calls for soil‐quality monitoring are therefore, justified and this could inform decision‐makers on the preparation of appropriate interventions. However, the provision of monitoring methodologies is not an easy task. Soil degradation affects several soil characteristics that at larger scales cannot be evaluated with models or remote‐sensing techniques. Therefore, this study focuses on investigating the use of field‐based soil assessment methodologies to differentiate degrees of soil degradation. Specifically, we test the Visual Soil Field Assessment Tool (VS‐Fast) for detecting and monitoring soil degradation using a cross‐section of 71 sites in Senegal, the soil quality of which were classed by local experts. We found low correlation between VS‐Fast classes and expert assessments. By using an ordered logit model to quantify class boundaries, we show that experts categorized areas as ‘degraded’ for a wider range and higher VS‐Fast scores than the corresponding VS‐Fast class. Yet, from general linear models and analysis of variance procedures we found that areas classed by experts as ‘degraded’ had statistically significant lower VS‐Fast scores compared to those that were judged as ‘normal’ and ‘good’, while differences of the VS‐Fast scores between the latter two were negligible. It is remarkable that the visual assessment, the cheaper component of the VS‐Fast score, performs better in differentiating degradation status than its measured counterpart. The results support the need to investigate the applicability of other VSA methodologies that only use field observations and tactile methods.     

Influence of irrigation on the distribution and control of the nematode Meloidogyne javanica by the biocontrol bacterium Pasteuria penetrans in the field

Pasteuria penetrans, a bacterial parasite of plant-parasitic nematodes, is used to control root-knot nematode Meloidogyne spp. populations in vegetable crops. But its efficiency is variable, mostly because of the patchy distribution of the bacteria in arable fields. As the infective P. penetrans are non-motile bacteria in soil, abiotic soil factors can affect the bacteria–nematode relationships. An epidemiological study, conducted in a vegetable field, showed that abiotic factors such as irrigation, soil water holding capacity and texture, affected the efficiency of P. penetrans. A correspondence analysis between these abiotic factors and the density of P. penetrans spores in the soil, and the proportion of Meloidogyne javanica juveniles infected by the bacteria, revealed that irrigation affected directly the distribution of the spores in soil pores related to their passive transport by water flow. Laboratory experiments conducted on the passive transport of spores confirmed that intensive irrigation leached the spores down the soil profile and decreased the percentage of infected Meloidogyne juveniles.     

Predicting Rice Yield Under Salinity Stress Using K/Na Ratio Variable in Plant Tissue

Estimation of yield reduction in crop caused by the salinity stress is mostly based on variations of soil electrical conductivity and the severity of water stress. Crop response curves to salinity were developed without considering ion toxicity and nutritional imbalance in the plant. The objective of this study was to explore the possibility of using the ratio of the concentration of potassium by sodium in rice leaf (leaf-K/Na) to predict yield under the salinity stress. The rice (Oryza sativa L.) yield under fresh and saline condition and the leaf-K/Na related database was created. Data were collected from consecutive three seasons of a field experiment in the Africa Rice Center experimental farm in Senegal (16° 11? N, 16° 15?W). We studied the relationship between the relative yield (Y_r), a ratio of yield under the salinity stress to the potential yield and the leaf-K/Na (x). Furthermore, we did regression analyses and F-test to determine the best fitting function. Results indicate that the exponential function [i.e. Y_r = 100 exp (-b x)] was the best fitting model with the lowest root mean square error (9.683) and the highest R² value (0.90). Example applications on independent data from published papers showed relatively good predictions, suggesting that the model can be used to predict rice yield in saline soils.     

Antidiabetic properties of aqueous barks extract of Parinari excelsa in alloxan-induced diabetic rats

The aqueous extract of the Parinari excelsa barks at doses of 100 and 300 mg/kg/day for 7 days has a significant antihyperglycemic effect on alloxan-induced diabetic rats. At the same dose the acute oral administration of aqueous extract of the P. excelsa barks (100 and 300 mg/kg) induced a significant decrease of blood glucose on glucose-loaded normoglycaemic rats. Our results seem to confirm the rational bases for its use in traditional medicine.     

Vegetative propagation studies of gum arabic trees. 1. Propagation of Acacia senegal (L.) Willd. using lignified cuttings of small diameter with eight nodes

In order to study and monitor rates of rooting, callusing and survival, small diameter stem cuttings (less than 0.4 cm) with eight nodes (12–14 cm long) were taken from trees in the field at different times of the year in tests with small batches of cuttings. Among the four tested substrates, well drained crushed basalt powder placed on a bed of gravel was the best rooting medium. Rooting was generally poor or even non-existent with this type of cutting, but the presence of leaves both promoted rooting and significantly (using Chi square test) improved survival of the cutting. Four leaves were sufficient to assure the necessary metabolic functions. Only stem cuttings collected during the rainy season gave roots and then, only when hormonal treatments were applied to the cuttings. 8%-IBA resulted in significantly better rooting than did 2%-IBA, 0.2%-NAA and 1%-IAA. With 8%-IBA, the rate of rooting varied between 50 and 70% for leafy cuttings collected in the rainy season. Hormonal treatments had a significant effect on the survival rate of the stem cuttings during the two months observation period. Further investigations are being conducted with different types of cuttings.     

A metagenomic survey of microbes in honey bee colony collapse disorder

In colony collapse disorder (CCD), honey bee colonies inexplicably lose their workers. CCD has resulted in a loss of 50 to 90% of colonies in beekeeping operations across the United States. The observation that irradiated combs from affected colonies can be repopulated with naive bees suggests that infection may contribute to CCD. We used an unbiased metagenomic approach to survey microflora in CCD hives, normal hives, and imported royal jelly. Candidate pathogens were screened for significance of association with CCD by the examination of samples collected from several sites over a period of 3 years. One organism, Israeli acute paralysis virus of bees, was strongly correlated with CCD.     

Alley Cropping of Maize and Gliricidia sepium in the Sudanese Sahel Region: Some Technical Feasibility Aspects

An association in an alley cropping experiment of a short-term maize variety and a tree legume (Gliricidia sepium) adapted to the Sudanese Sahel region was studied from the point of view of the nitrogen balance and plot yields. Isotopic labelling applied in the field enabled the contribution to maize nitrogen nutrition from different nitrogen sources (fertilizer, prunings, and soil) to be quantified. For equal cultivated areas, alley cropping provides a maize yield greater than that of maize grown in pure stand without nitrogen fertilizer. However this yield is only 40% of that obtained in pure stands with fertilizer nitrogen. Alley cropping gives a very favourable Land Equivalent Ratio (LER) of 0.90 and 1.58 compared with N-fertilized and non-N-fertilized plots, respectively. These LERs demonstrate the increased biological efficiency of the cultivated soil in an agroforestry system. The percentage of the total nitrogen in the maize coming from prunings (Nfdp) varies between 30 and 35% and the true coefficient of nitrogen utilization of the prunings (TCUp) varies from 15-25%. In the environment of central Senegal, the percentage of total nitrogen of G. sepium coming from N 2 fixation is quite low (Ndffix = 25%), and consequently, in the maize, the nitrogen coming from N 2 fixation (Ndffix) is only 8%. It is therefore necessary to improve the efficiency of nitrogen fixation of G. sepium in this zone to assure the sustainability of the agroforestry system.     

Support Vector Machine and Nonlinear Regression Methods for Estimating Saturated Hydraulic Conductivity

Pedotransfer functions (PTFs) are widely used for hydrological calculations based on the known basic properties of soils and sediments. The choice of predictors and the mathematical calculus are of particular importance for the accuracy of calculations. The aim of this study is to compare PTFs with the use of the nonlinear regression (NLR) and support vector machine (SVM) methods, as well as to choose predictor properties for estimating saturated hydraulic conductivity (K_s). K_s was determined in direct laboratory experiments on monoliths of agrosoddy-podzolic soil (Umbric Albeluvisol Abruptic, WRB, 2006) and calculated using PTFs based on the NLR and SVM methods. Six classes of predictor properties were tested for the calculated prognosis: K_s-1 (predictors: the sand, silt, and clay contents); K_s-2 (sand, silt, clay, and soil density); K_s-3 (sand, silt, clay, soil organic matter); K_s-4 (sand, silt, clay, soil density, organic matter); K_s-5 (clay, soil density, organic matter); and K_s-6 (sand, clay, soil density, organic matter). The efficiency of PTFs was determined by comparison with experimental values using the root mean square error (RMSE) and determination coefficient (R²). The results showed that the RMSE for SVM is smaller than the RMSE for NLR in predicting K_s for all classes of PTFs. The SVM method has advantages over the NLR method in terms of simplicity and range of application for predicting K_s using PTFs.