Food systems are under pressure to produce more food of higher quality while reducing the pressure on natural resources. Currently, land degradation is widespread, especially in areas with ...smallholder farming. Agricultural extension may help farmers to adopt sustainable practices. However, adoption rates of interventions are often low in smallholder farming. This paper carries out an ex-ante analysis to determine to what extent the perception of soil characteristics (land cover, slope, soil organic matter, surface stoniness, and texture) and soil variability (how much acid or basic is the soil, i.e., pH level, and level of electric conductivity-EC) limit adoption with the final goal to design better policies to crop productivity. Differences in the perception of soil characteristics and variability between farmers and agricultural specialists, may result in situations where technological packages arrive at farms that are not likely to adopt or do not arrive at farmers that are likely to adopt. This paper studies differences in perception of soil characteristics and soil variability between agricultural specialists and farmers. Smallholder farmers in Central America producing beans were found to have different perceptions of soil characteristics relative to agricultural specialists. In addition, soil variability was considerable. As a result, it is concluded that soil perception and soil variability are likely causes of low adoption rates. Reducing the information gap with respect to the real needs of farmers will make policies more effective, resulting in higher adoption rates of new technologies and increased productivity.
•Reducing the information gap with respect to the real needs of farmers will make policies more effective.•Strategies that do not consider farmers' perceptions are unlikely to be effective.
Aims
This study looks whether the response of soil management (liming and nitrogen fertilization) on the incidence of Fusarium wilt (Foc Race 1) in Gros Michel banana (
Musa
AAA) varies with ...different soil properties.
Methods
The effect of inoculation with Foc Race 1 was studied in a factorial greenhouse trial with soil samples from eight representative soil types from the Costa Rican banana region, two pH levels; and three levels of N-fertilization. After an 8-week period, plant biomass and a disease index were measured.
Results
There were significant effects of soil pH and N, and their interactions on disease expression. Low pH levels and high N-fertilization increased the disease expression. The response to changes in soil pH and N-fertilization differed considerably between the different soils.
Conclusions
Although soil pH and N influence Fusarium wilt in banana, each soil differs in its response to these soil properties. This complicates the development of standard soil management strategies in terms of e.g., N-fertilization and liming to mitigate or fight the disease.
The 1:50,000 national soil survey of the Netherlands, completed in the early 1990s after more than three decades of mapping, is gradually becoming outdated. Large-scale changes in land and water ...management that took place after the field surveys have had a great impact on the soil. Especially oxidation of peat soils has resulted in a substantial decline of these soils. The aim of this research was to update the national soil map for the province of Drenthe (2680 km
2) without additional fieldwork through digital soil mapping using legacy soil data. Multinomial logistic regression was used to quantify the relationship between ancillary variables and soil group. Special attention was given to model-building as this is perhaps the most crucial step in digital soil mapping. A framework for building a logistic regression model was taken from the literature and adapted for the purpose of soil mapping. The model-building process was guided by pedological expert knowledge to ensure that the final regression model is not only statistically sound but also pedologically plausible. We built separate models for the ten major map units, representing the main soil groups, of the national soil map for the province of Drenthe. The calibrated models were used to estimate the probability of occurrence of soil groups on a 25 m grid. Shannon entropy was used to quantify the uncertainty of the updated soil map, and the updated soil map was validated by an independent probability sample. The theoretical purity of the updated map was 67%. The estimated actual purity of the updated map, as assessed by the validation sample, was 58%, which is 6% larger than the actual purity of the national soil map. The discrepancy between theoretical and actual purity might be explained by the spatial clustering of the soil profile observations used to calibrate the multinomial logistic regression models and by the age difference between calibration and validation observations.
Improving fertilizer recommendations for farmers is essential to increase food security in smallholder landscapes. Currently, blanket recommendations are provided across agro-ecological zones, ...although fertilizer response and nutrient use efficiency by maize crop are spatially variable. We aimed to identify factors that could help to refine fertilizer recommendation by analyzing the variability in fertilizer response (FR) and the agronomic nitrogen use efficiency (N-AE). A literature search for on-farm studies across Kenya and Sub-Sahara Africa (SSA), excluding Kenya, yielded 71 publications. The variability in FR was studied using a meta-analysis whereas key factors that influence FR and N-AE were studied with linear regression models. On average, the FR was 2, but it varied considerably from 1 to 28.5 (excluding outliers). In SSA, 18% of the plots were non-responsive plots with an FR < 1. The main factors affecting N-AE for Kenya were P-Olsen, silt content, soil pH, clay and rainfall, whereas only soil pH, exchangeable K and texture were important for SSA. However, our study indicates that available data on soil, climate and management factors could explain only a small part (< 33%) of the variation in FR and N-AE. Soil pH, P-Olsen, silt content, and rainfall had significant but low levels of power in explaining variation in FR and N-AE. Our findings indicate that strategies to refine fertilizer recommendation should include information on soil types and soil properties.
This study compared the efficiency of geostatistical digital soil mapping (DSM) with conventional soil mapping (CSM) for updating soil class and property maps of a cultivated peatland in the ...Netherlands. For digital soil class mapping, the generalized linear geostatistical model was used. Digital mapping of the soil organic matter (SOM) content and peat thickness was done by universal kriging. The conventional soil class map was created by free survey, while the property maps were created with the representative profile description (RPD) and map unit means (MUM) methods. For each method, we computed the effort invested in the mapping in terms of the sampling and cost densities. The accuracies of the created soil maps were estimated from independent probability sample data. The results showed that for DSM, the cost density could be reduced by a factor of three compared with CSM without compromising accuracy. The map purity of both maps was around 55%. For conventional soil property mapping, the MUM maps were more accurate than the RPD maps. For SOM, CSM-MUM (RMSE 7.5%) performed better than DSM (RMSE 12.1%), although accuracy differences were not significant. For peat thickness, DSM (RMSE 23.3 cm) performed slightly better than CSM-MUM (RMSE 24.9 cm). Despite the differences in accuracy being small, the digital soil property maps were produced more efficiently. The cost density was a factor of 3.5 smaller. We conclude that for updating conventional soil maps in the Dutch peatlands, geostatistical DSM can be more efficient, although not necessarily more accurate, than CSM.
Desertification is defined as land degradation occurring in the global drylands. It is one of the global problems targeted under the Sustainable Development Goals (SDG 15). The aim of this article is ...to review the history of desertification and to evaluate the scientific evidence for desertification spread and severity. First quantitative estimates of the global extent and severity of desertification were dramatic and resulted in the establishment of the UN Convention to Combat Desertification (UNCCD) in 1994. UNCCD’s task is to mitigate the negative impacts of desertification in drylands. Since the late 1990s, science has become increasingly critical towards the role of desertification in sustainable land use and food production. Many of the dramatic global assessments of desertification in the 1970s and 1980s were heavily criticized by scientists working in drylands. The used methodologies and the lack of ground-based evidence gave rise to critical reflections on desertification. Some even called desertification a myth. Later desertification assessments relied on remote sensing imagery and mapped vegetation changes in drylands. No examples of large areas completely degraded were found in the scientific literature. In science, desertification is now perceived as a local feature that certainly exists but is not as devastating as was earlier believed. However, the policy arena continues to stress the severity of the problem. Claims that millions of hectares of once productive land are annually lost due to desertification are regularly made. This highlights the disconnection between science and policy, and there is an urgent need for better dialogue in order to achieve SDG 15.
•Global models underrepresent tillage practices and its effect on N2O emissions.•Nitrogen processes affected by tillage and their data requirements are identified.•Process knowledge to model tillage ...and N2O emissions at a global scale is available.•There are viable options to include tillage in global models for studying N2O emissions.
Strategies on agricultural management can help to reduce global greenhouse gas (GHG) emissions. However, the potential of agricultural management to reduce GHG emissions at the global scale is unclear. Global ecosystem models often lack sufficient detail in their representation of management, such as tillage. This paper explores whether and how tillage can be incorporated in global ecosystem models for the analysis of nitrous oxide (N2O) emissions. We identify the most important nitrogen processes in soils and their response to tillage. We review how these processes and tillage effects are described in field-scale models and evaluate whether they can be incorporated in the global-scale models while considering the data requirements for a global application. The most important processes are described in field-scale models and the basic data requirements can be met at the global scale. We therefore conclude that there is potential to incorporate tillage in global ecosystem models for the analysis of N2O emissions. There are several options for how the relevant processes can be incorporated into global ecosystem models, so that generally there is potential to study the effects of tillage on N2O emissions globally. Given the many interactions with other processes, modelers need to identify the modelling approaches that are consistent with their modelling framework and test these.
Digital soil mapping (DSM) approaches provide soil information by utilising the relationship between soil properties and environmental variables. Calibration of DSM models requires measurements that ...may often have substantial measurement errors which propagate to the DSM outputs and need to be accounted for. This study applied a geostatistical‐based DSM approach that incorporates measurement error variances in the covariance structure of the spatial model, weights measurements in accordance with their measurement accuracies and assesses the effects of measurement errors on the accuracies of DSM outputs. The method was applied in the Western Cameroon, where soil samples from 480 locations were collected and analysed for pH, clay and soil organic carbon (SOC) using conventional and mid‐infrared spectroscopy methods. Variogram parameters and regression coefficients were estimated using residual maximum likelihood under two scenarios: with and without taking measurement errors into account. Performance of the spatial models in the two scenarios was compared using validation metrics obtained with three types of cross‐validation. Acknowledging measurement errors impacted the regression coefficients and influenced the variogram parameters by reducing the nugget and sill variance for the three soil properties. Validation metrics including mean error, root mean square error and model efficiency coefficient were quite similar in both scenarios, but the prediction uncertainties were more realistically quantified by the models that account for measurement errors, as indicated by accuracy plots. There were relatively small absolute differences in predicted values of soil properties of up to 0.1 for pH, 1.6% for clay and 2 g/kg for SOC between the two scenarios. We emphasised the need of incorporating measurement errors in DSM approaches to improve uncertainty quantification, particularly when applying spectroscopy for estimating soil properties. Further development of the approach is the extension to non‐linear machine learning regression methods.
Highlights
Errors in soil measurements are usually not accounted for and may affect DSM results.
Measurement error variances were incorporated in the geostatistical models of three soil properties.
Quantifying measurement errors in DSM allows to weigh measurements in accordance with their accuracy.
Accounting for measurement errors in DSM better assesses prediction accuracy.
Tyre granulate used as infill for artificial turf is hailed by some as a good example of reuse, while others see it as a baleful means to dispose of discarded tyres. Because the particles are applied ...loosely to the surface, they will inevitably disperse into the environment. The possible environmental and health impacts of the particles are a source of societal concern. In response to this, policies to limit particle losses are being developed at the European level. To make informed decisions, data on the quantity of tyre granulate released into the environment are required. So far, however, there are no systematic reviews on or estimates of these losses. The aim of the present study was to identify the various pathways through which infill leaves a football turf and, subsequently, to estimate the quantity of infill leaving the turf by each of these pathways. Data on the pathways including the associated volumes were collected in a systematic literature review following the PRISMA method. The quality of the evidence reported in the retrieved literature was assessed using the GRADE method. The resulting pathways and corresponding quantities were captured in a mass balance. This study estimates that, without mitigation measures, approximately 950 kg/year (min. 570 kg/year, max. 2280 kg/year) of infill leaves the surface of an average artificial football turf via known pathways. Clearing snow can result in an additional loss of 830 kg/year (min. 200 kg/year, max. 2760 kg/year) of infill material. To mitigate the dispersion of infill, one could focus on snow removal, brushing and granulate picked up by players. Mitigation measures for these pathways are well-established and relatively easy to implement and maintain. Although the amount of granulate picked up from the turf by players is relatively small, the measure will promote environmental awareness among the players.
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