Agriculture de précision et petits exploitants

Le Dr Jetse Stoorvogel affi rme que les petits paysans appliquent déjà les principes de l'agriculture de précision sur leurs exploitations sans avoir besoin d'équipements de haute technologie

Fusarium wilt of banana, a recurring threat to global banana production

TR4 first emerged in Southeast Asia (Ploetz, 1990) and its current rapid spread was analysed by Ordóñez et al. (2015). Subsequent studies showed that the TR4 strain is extremely virulent towards many banana cultivars, including Cavendish cultivars grown in large-scale monoculture plantations for export markets and many banana varieties important for food security and domestic consumption. There are no readily available solutions to manage this disease. Moreover, this global threat connects export trade, strongly dependent on the susceptible Cavendish cultivars, to local production systems wherein a range of banana varieties contributing to food security are also impacted.This research topic aims to provide a platform for information exchange and knowledge sharing. The contributions demonstrate an active research community in search of effective control of FWB. Taken together, the papers provide an overview of our current understanding of the biology and epidemiology of TR4, its management and how integrated and innovative solutions are required and need to be embraced by all stakeholders in an effort to build a sustainable banana industry for the future

Introducing a Mechanistic Model in Digital Soil Mapping to Predict Soil Organic Matter Stocks in the Cantabrian Region (Spain)

ABSTRACT: Digital soil mapping (DSM) is an effective mapping technique that supports the increased need for quantitative soil data. In DSM, soil properties are correlated with environmental characteristics using statistical models such as regression. However, many of these relationships are explicitly described in mechanistic simulation models. Therefore, the mechanistic relationships can, in theory, replace the statistical relationships in DSM. This study aims to develop a mechanistic model to predict soil organic matter (SOM) stocks in Natura2000 areas of the Cantabria region (Spain). The mechanistic model is established in four steps: (a) identify major processes that influence SOM stocks, (b) review existing models describing the major processes and the respective environmental data that they require, (c) establish a database with the required input data, and (d) calibrate the model with field observations. The SOM stocks map resulting from the mechanistic model had a mean error (ME) of -2 t SOM ha−1 and a root mean square error (RMSE) of 66t SOM ha-1. The Lin's concordance correlation coefficient was 0.47 and the amount of variance explained (AVE) was 0.21. The results of the mechanistic model were compared to the results of a statistical model. It turned out that the correlation coefficient between the two SOM stock maps was 0.8. This study illustrated that mechanistic soil models can be used for DSM, which brings new opportunities. Mechanistic models for DSM should be considered for mapping soil characteristics that are difficult to predict by statistical models, and for extrapolation purposes.This research was financially supported by the Environmental Hydraulics Institute ‘IH Cantabria of Universidad de Cantabria’ and the CGIAR Research Programme on Climate Change, Agriculture and Food Security (CCAFS). The CCAFS project is carried out with support from CGIAR Fund Donors and through bilateral funding agreements. Besides the financial support, we would like to thank Sara Alcalde Aparicio for collaboration in the collection and analyses of soil samples

Ideotyping integrated aquaculture systems to balance soil nutrients

Due to growing land scarcity and lack of nutrient inputs, African farmers switched from shifting cultivation to continuous cropping and extended crop area by bringing fragile lands such as river banks and hill slopes into production. This accelerated soil fertility decline caused by erosion, harvesting and insufficient nutrient replenishment. We explored the feasibility to reduce nutrient depletion by increasing nutrient utilization efficiencies, while diversifying and increasing food production through the development of integrated aquaculture – agriculture (IAA). Considering the climatic conditions prevailing in Kenyan highlands, aquaculture production scenarios were ideotyped per agro-ecological zone. These aquaculture production scenarios were integrated into existing NUTrient MONitoring (NUTMON) farm survey data for the area. The nutrient balances and flows of the resulting IAA-systems were compared to present land use. The effects of IAA development on nutrient depletion and total food production were evaluated. With the development of IAA systems, nutrient depletion rates dropped by 23–35%, agricultural production increased by 2–26% and overall farm food production increased by 22–70%. The study demonstrates that from a bio-physical point of view, the development of IAA-systems in Africa is technically possible and could raise soil fertility and total farm production. Further studies that evaluate the economic feasibility and impacts on the livelihood of farming households are recommended

Simulating the effect of tillage practices with the global ecosystem model LPJmL (version 5.0-tillage)

The effects of tillage on soil properties, crop productivity, and global greenhouse gas emissions have been discussed in the last decades. Global ecosystem models have limited capacity to simulate the various effects of tillage. With respect to the decomposition of soil organic matter, they either assume a constant increase due to tillage or they ignore the effects of tillage. Hence, they do not allow for analysing the effects of tillage and cannot evaluate, for example, reduced tillage or no tillage (referred to here as “no-till”) practises as mitigation practices for climate change. In this paper, we describe the implementation of tillage-related practices in the global ecosystem model LPJmL. The extended model is evaluated against reported differences between tillage and no-till management on several soil properties. To this end, simulation results are compared with published meta-analyses on tillage effects. In general, the model is able to reproduce observed tillage effects on global, as well as regional, patterns of carbon and water fluxes. However, modelled N fluxes deviate from the literature values and need further study. The addition of the tillage module to LPJmL5 opens up opportunities to assess the impact of agricultural soil management practices under different scenarios with implications for agricultural productivity, carbon sequestration, greenhouse gas emissions, and other environmental indicators

The importance of management information and soil moisture representation for simulating tillage effects on N2O emissions in LPJmL5.0-tillage

No-tillage is often suggested as a strategy to reduce greenhouse gas emissions. Modeling tillage effects on nitrous oxide (N2O) emissions is challenging and subject to great uncertainties as the processes producing the emissions are complex and strongly nonlinear. Previous findings have shown deviations between the LPJmL5.0-tillage model (LPJmL: Lund–Potsdam–Jena managed Land) and results from meta-analysis on global estimates of tillage effects on N2O emissions. Here we tested LPJmL5.0-tillage at four different experimental sites across Europe and the USA to verify whether deviations in N2O emissions under different tillage regimes result from a lack of detailed information on agricultural management, the representation of soil water dynamics or both. Model results were compared to observational data and outputs from field-scale DayCent model simulations. DayCent has been successfully applied for the simulation of N2O emissions and provides a richer database for comparison than noncontinuous measurements at experimental sites. We found that adding information on agricultural management improved the simulation of tillage effects on N2O emissions in LPJmL. We also found that LPJmL overestimated N2O emissions and the effects of no-tillage on N2O emissions, whereas DayCent tended to underestimate the emissions of no-tillage treatments. LPJmL showed a general bias to overestimate soil moisture content. Modifications of hydraulic properties in LPJmL in order to match properties assumed in DayCent, as well as of the parameters related to residue cover, improved the overall simulation of soil water and N2O emissions simulated under tillage and no-tillage separately. However, the effects of no-tillage (shifting from tillage to no-tillage) did not improve. Advancing the current state of information on agricultural management and improvements in soil moisture highlights the potential to improve LPJmL5.0-tillage and global estimates of tillage effects on N2O emissions