Agroforestry, Forest and Agricultural Sustainability

Mixed pasture establishment is considered as a key point to increase livestock production due to the higher quality it has compared with unmanaged shrubby species. They also provide a set of ecosystem services such as carbon sequestration and higher biodiversity. Moreover, herbaceous pasture establishment effectively contributes to biomass production while replacing shrubs therefore reducing forest fire risk. Two of the main factors promoting the establishment of the herbaceous pasture are sowing and fertilisation. This study evaluates the understory composition evolution of a silvopastoral system established with cocksfoot and clover in an acidic forest soil under Pinus radiata D. Don with four treatments that consist of no fertilization, fertilization with mineral fertilizers (500 kg of mineral 8:24:16) and fertilization with three doses of sewage sludge (160, 320 and 480 kg N ha-1), in Galicia (NW Spain). Botanical composition data were analysed through species abundance diagrams in a time window of 20 years. Results showed a clear evolution in understory composition from the initial situation. Organic fertilization was found to ease sown-mixture persistence on time as well as avoiding shrub colonisation with regard to mineral and no fertilization treatments. Nutritional and sun light high-demanding species were mostly replaced in favour of autochthonous ones after sewage sludge fertilization stopped and tree canopy intercepted light that reached the understory.

Agroforestry is a traditional land use system that may represent the answer to many present and future environmental problems. However, many farmers who practice agroforestry do not identity it as agroforestry nor even accept such identification. So far, there is has not been an organized training method on agroforestry apart the Agrof MM method. The Agrof MM, "Agroforesterie – Formation - Mediterannee et Montagne", was a 3-year KA-2 ERASMUS+ educational project that aimed to i. Train between 130 and 150 agricultural professionals in Europe, ii. Improve and develop the education tools to enable agroforestry training to be sustainable, and, iii. Develop a unique agroforestry qualification program in each European country. It was coordinated by AgroSup Dijon, France. Thirteen partners from ten different countries participated in the program by contributing a wide range of knowledge, experiences and ideas. Within the framework of the Agrof MM project, European stakeholders were trained based on a general format (Core content) that differentiated based on the location. The trainings contained lectures, examples and a field trip per training.

The organic carbon percentage is concomitant indicating the mineralization of nutrients and the ability of the soil to hold nutrients cations, structural stability, and water holding capacity. It is necessary to know the quantity of carbon for healthy soil and avoid the production related problems which can affect the sustainable agriculture model. In existing approaches, to quantitively calculate soil carbon, sample collection and in-situ laboratory testing are performed. In this work, a novel framework is proposed which is based on Partial Least Square Regression and Long Short-Term Memory networks to quantify soil organic carbon from the LUCAS dataset. Samples of LUCAS dataset are used as input to this framework. The samples are pre-processed by PLS to reduce their dimensions. These pre-processed samples are then passed to the LSTM, a Deep learning framework to build an efficient prediction model. The proposed framework performed more accurately, and its effectiveness is shown by comparing it with existing regression models.

Inhomogeneities of soil properties are responsible for within-field variations on the growth and final yield of crops. In this paper the variations of a Normalized Difference Vegetation Index (NDVI) field map, obtained through a camera mounted on an Unmanned Aerial Vehicle, were correlated with soil and plant properties measured in a typical maize cultivated field. Subsequently, the perspective of organizing the field into differential management zones through the NDVI was evaluated. The coefficient of variation for sand, silt, clay and soil organic matter content at five points was found to exceed 11% while the corresponding value for biomass and yield were greater than 14%, indicating significant spatial field soil heterogeneity and variations on plant growth. When correlated to NDVI, sand content exhibited a negative correlation (r=-0.86), while in the case of silt, clay, organic matter content, biomass and yield the correlation was positive (r>0.8). Lastly, the NDVI confirmed to be a powerful tool for the delineation of deferential management zones.