Anne Gobin

Modelling the impact of agricultural management on soil carbon stocks at the regional scale: The role of lateral fluxes

Agricultural management has received increased attention over the last decades due to its central role in carbon (C) sequestration and greenhouse gas mitigation. Yet, regardless of the large body of literature on the effects of soil erosion by tillage and water on soil organic carbon (SOC) stocks in agricultural landscapes, the significance of soil redistribution for the overall C budget and the C sequestration potential of land management options remains poorly quantified. In this study, we explore the role of lateral SOC fluxes in regional scale modelling of SOC stocks under three different agricultural management practices in central Belgium: conventional tillage (CT), reduced tillage (RT) and reduced tillage with additional carbon input (RT+i). We assessed each management scenario twice: using a conventional approach that did not account for lateral fluxes and an alternative approach that included soil erosion-induced lateral SOC fluxes. The results show that accounting for lateral fluxes increased C sequestration rates by 2.7, 2.5 and 1.5xa0gxa0Cxa0m(-2) xa0yr(-1) for CT, RT and RT+i, respectively, relative to the conventional approach. Soil redistribution also led to a reduction of SOC concentration in the plough layer and increased the spatial variability of SOC stocks, suggesting that C sequestration studies relying on changes in the plough layer may underestimate the soils C sequestration potential due to the effects of soil erosion. Additionally, lateral C export from cropland was in the same of order of magnitude as C sequestration; hence, the fate of C exported from cropland into other land uses is crucial to determine the ultimate impact of management and erosion on the landscape C balance. Consequently, soil management strategies targeting C sequestration will be most effective when accompanied by measures that reduce soil erosion given that erosion loss can balance potential C uptake, particularly in sloping areas.

Effort for money? Farmers' rationale for participation in agri-environment measures with different implementation complexity

European agri-environment programmes are based on the common principle that farmers deliver environmental services for which society pays. Due to the voluntary nature of agri-environment measures (AEM), the issue of farmers motives or reasons for participation has been an important topic of investigation in past years. The present paper examines farmers rationale for participation in AEM against the backdrop of continued debate over whether to develop relatively simple measures that can be readily applied by many farmers or give greater priority to measures that are more targeted - i.e. to the specific management requirement of particular habitats or species - but are often more complex. The paper draws on empirical material from a case study in the Dyle valley, Belgium, including in-depth interviews, expert consultations and a mail survey. It was sought not only to identify and quantify the importance of separate reasons for participation, but also to reveal how these reasons and other elements of relevance were logically interrelated in the explanation that farmers themselves give for their participation. As a result, six modes or styles of participation were identified: opportunistic, calculative, compensatory, optimising, catalysing and engaged. The analyses suggest that there were notable differences in that both separate reasons for and modes of participation do vary with the complexity of the measures requirements. Overall, the study demonstrates that participation in AEM is not simply a matter of weighing the money against the effort for adoption. Whereas money is an important driver for participation (in particular, for those adopting complex AEM) it plays widely differing roles depending on the level of farmers reasoning (farm enterprise, single practice or landscape feature) and the importance they give to other considerations (environmental effect, production potential of land, goodness of fit, etc.). Practical implications are drawn for both policy makers and programme managers who develop and make available tailor-made support.

Crop Area Mapping Using 100-m Proba-V Time Series

A method was developed for crop area mapping inspired by spectral matching techniques (SMTs) and based on phenological characteristics of different crop types applied using 100-m Proba-V NDVI data for the season 2014–2015. Ten-daily maximum value NDVI composites were created and smoothed in SPIRITS (spirits.jrc.ec.europa.eu). The study sites were globally spread agricultural areas located in Flanders (Belgium), Sria (Russia), Kyiv (Ukraine) and Sao Paulo (Brazil). For each pure pixel within the field, the NDVI profile of the crop type for its growing season was matched with the reference NDVI profile based on the training set extracted from the study site where the crop type originated. Three temporal windows were tested within the growing season: green-up to senescence, green-up to dormancy and minimum NDVI at the beginning of the growing season to minimum NDVI at the end of the growing season. Post classification rules were applied to the results to aggregate the crop type at the plot level. The overall accuracy (%) ranged between 65 and 86, and the kappa coefficient changed from 0.43–0.84 according to the site and the temporal window. In order of importance, the crop phenological development period, parcel size, shorter time window, number of ground-truth parcels and crop calendar similarity were the main reasons behind the differences between the results. The methodology described in this study demonstrated that 100-m Proba-V has the potential to be used in crop area mapping across different regions in the world.

Crowdsourcing for agricultural applications: A review of uses and opportunities for a farmsourcing approach

Abstract Crowdsourcing, understood as outsourcing tasks or data collection by a large group of non-professionals, is increasingly used in scientific research and operational applications. In this paper, we reviewed crowdsourcing initiatives in agricultural science and farming activities and further discussed the particular characteristics of this approach in the field of agriculture. On-going crowdsourcing initiatives in agriculture were analysed and categorised according to their crowdsourcing component. We identified eight types of agricultural data and information that can be generated from crowdsourcing initiatives. Subsequently we described existing methods of quality control of the crowdsourced data. We analysed the profiles of potential contributors in crowdsourcing initiatives in agriculture, suggested ways for increasing farmers’ participation, and discussed the on-going initiatives in the light of their target beneficiaries. While crowdsourcing is reported to be an efficient way of collecting observations relevant to environmental monitoring and contributing to science in general, we pointed out that crowdsourcing applications in agriculture may be hampered by privacy issues and other barriers to participation. Close connections with the farming sector, including extension services and farm advisory companies, could leverage the potential of crowdsourcing for both agricultural research and farming applications. This paper coins the term of farmsourcing asxa0a professional crowdsourcing strategy in farming activities and provides a source of recommendations and inspirations for future collaborative actions in agricultural crowdsourcing.

Testing the Contribution of Stress Factors to Improve Wheat and Maize Yield Estimations Derived from Remotely-Sensed Dry Matter Productivity

According to Monteith’s theory, crop biomass is linearly correlated with the amount of absorbed photosynthetically active radiation (APAR) and a constant radiation use efficiency (RUE) down-regulated by stress factors such as CO2 fertilisation, temperature and water stress. The objective was to investigate the relative importance of these stress factors in relation to regional biomass production and yield. The production efficiency model Copernicus Global Land Service-Dry Matter Productivity (CGLS-DMP), which follows Monteith’s theory, was modified and evaluated for common wheat and silage maize in France, Belgium and Morocco using SPOT VEGETATION for the period 1999–2012. For each study site the stress factor that has the highest correlation with crop yield was retained. The correlation between crop yield data and cumulative modified DMP, CGLS-DMP, fAPAR, and NDVI values were analysed for different crop growth stages. A leave-one-year-out cross validation was used to test the robustness of the model. On average, R2 values increased from 0.49 for CGLS-DMP to 0.68 for modified DMP, RMSE (t/ha) decreased from 0.84–0.61, RRMSE (%) reduced from 13.1–8.9, MBE (t/ha) decreased from 0.05–0.03 and the index of model performance (E1) increased from 0.08–0.28 for the selected sites and crops. The best results were obtained by including combinations of the most appropriate stress factors for each selected region and cumulating the modified DMP during part of the growing season that includes the reproductive stage. Though no single solution to the improvement of a global product could be demonstrated, our findings encourage an extension of the methodology to other regions of the world.

Water Footprint of main crops in Austria / Wasser-Fußabdruck wichtiger Nutzpflanzen in Österreich

Summary Water is a key resource for human activities and a critical trigger for the welfare of the whole society. The agricultural sector makes up the main share in global freshwater consumption and is therefore responsible for a large part of the water scarcity in many drought prone regions. As an indicator that relates human consumption to global water resources, the “Water Footprint” (WF) concept can be used, where in case of crop production the total consumed water of crop fields for the crop growing seasons is related to the harvested dry matter crop yield (such as grains). In our study, we simulated the green and primary blue WF of selected main crops for Austrian conditions. Different irrigation scheduling scenarios, demonstrated for a main agricultural production area and various crops in Austria with significant irrigation acreage, were studied. The impact of climate and soil conditions on the green crop WFs of reference crops over the whole territory of Austria were simulated in a second step. Sunflower, winter wheat and grain maize showed the highest WF in the semi-arid study regions, especially on soils with low water capacity. In more humid regions, low temperatures were the main limiting factor on the crop yield potential and frequently led to higher WFs due to lower yields.

Select indicators and prioritize solutions for desertification and drought in Binh Thuan, Vietnam

ABSTRACT The link between climate change and desertification is an important global problem for the United Nations Framework Convention on Climate Change (UNFCCC) and the United Nations Convention to Combat Desertification (UNCCD). The coastal provinces in Southern Centre Vietnam are strongly influenced by desertification, which is further exacerbated by climate change. The Delphi method was used to select sustainability indicators and then a method of an analytical hierarchy process was applied to prioritize sustainable solutions in Binh Thuan experts to elicit the causes and effects of desertification. According to the panel of 23 experts, 27 cause (e.g. rainfall and sunshine duration) and 22 effect (e.g. rice and agriculture) indicators were of particular importance to desertification and drought in the province. The highest priorities for cause are given to emigration, followed by income from industry and water supply for household use. The highest priorities for effect in the area are given to socioeconomic criteria, and in particular to the sub-criteria resettlement due to development projects, building hydropower reservoirs, and implementing irrigation schemes. The system of indicators provides useful information for the future studies. This study provides a means to uncover the most important criteria perceived as major contributors to desertification and as such enables policy-makers to prioritize solutions for Binh Thuan (Vietnam) by 2015 and a vision in 2020.

Risk Assessment of Desertification for Binh Thuan Province, Vietnam

ABSTRACT Vietnam is one among five ranked countries with high-risk injury due to the phenomenon of climate change. Geographically, Binh Thuan, a coastal province, is located in the Southern Centre area. Currently, natural disasters have become more frequent, particularly drought. Desertification has become more serious. Besides the natural causes as described above, there are several affected by human activities such as high population and poverty, inappropriate cultivating technique, deforestation, ill-adopted legal framework, weak management capacity, lack of adequate knowledge, and a lack of awareness in local population. To assess risks of desertification for the Binh Thuan province (Vietnam), a risk assessment model based on a Leopold matrix was applied. As a result, a model of cause and effect showed six degrees of impacts of environmental and social conditions on the socioeconomic developments from very highly to very unlikely significance. Risk assessment allows for a general figure of various impacts of desertification on the socioeconomic developments in Binh Thuan, Vietnam.

Potato monitoring in Belgium with “WatchITGrow”

WatchITGrow is a web-based application developed for potato monitoring in Belgium. The different components encompass a back-end with biophysical parameters derived from high resolution satellite imagery, agrometeorological algorithms, phenological development and crop models; and a front-end with dashboards to visualize spatio-temporal information and insert potato field information.

Assessment of AquaCrop for winter wheat using satellite derived fCover data

The integration of crop growth models with remote sensing has presented great potential in (regional) crop yield forecasting; although so far few field-level applications exist. Based on crowd/farm-sourced observations (phenological stages and yield measurements) and a basic assimilation procedure using satellite (DMC) and digital hemispherical pictures (DHP) derived green fractional cover data (fCover), the AquaCrop plug-in model was assessed for winter wheat fields in Belgium. A semi-automated R-environment was developed to simultaneously run, assess and evaluate the ensemble of field-level simulations. The root mean square error (RMSE) was 0.8 ton/ha. It was concluded that the presented approach might be promising for large scale field-level yield forecasting.