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AbstractEnergy crops are currently promoted as potential sources of alternative energy that can help mitigate the climate change caused by greenhouse gases (GHGs). The perennial crop Miscanthus × giganteus is considered promising due to its high potential for biomass production under conditions of low input. However, to assess its potential for GHG mitigation, a better quantification of the crop's contribution to soil organic matter recycling under various management systems is needed. The aim of this work was to study the effect of abscised leaves on carbon (C) and nitrogen (N) recycling in a Miscanthus plantation. The dynamics of senescent leaf fall, the rate of leaf decomposition (using a litter bag approach) and the leaf accumulation at the soil surface were tracked over two 1‐year periods under field conditions in Northern France. The fallen leaves represented an average yearly input of 1.40 Mg C ha−1 and 16 kg N ha−1. The abscised leaves lost approximately 54% of their initial mass in 1 year due to decomposition; the remaining mass, accumulated as a mulch layer at the soil surface, was equivalent to 7 Mg dry matter (DM) ha−1 5 years after planting. Based on the estimated annual leaf‐C recycling rate and a stabilization rate of 35% of the added C, the annual contribution of the senescent leaves to the soil C was estimated to be approximately 0.50 Mg C ha−1yr−1 or 10 Mg C ha−1 total over the 20‐year lifespan of a Miscanthus crop. This finding suggested that for Miscanthus, the abscised leaves contribute more to the soil C accumulation than do the rhizomes or roots. In contrast, the recycling of the leaf N to the soil was less than for the other N fluxes, particularly for those involving the transfer of N from the tops of the plant to the rhizome.

This paper presents a new method for the isolation of displacement from atmospheric fringes. This novel approach is based on complex correlation of interferograms. Compared to other methods, this method has the advantage of requiring only a few interferograms (two at least). Its main limitation is the hypothesis of temporally varying atmospheric conditions between acquisitions (not the same heterogeneities on different interferograms). In the city of Paris, this method reveals 2 subsiding zones. Both have the same location as an important underground working site, which took place from 1995 to 1997. The existence of subsidence in the area was known previously from ground truth data. Their spatial extent can now be mapped by interferometry, and the temporal evolution of the subsidence is also examined here.

Abstract Profound physiological changes occur in the body of a dairy cow during the transition period. Most of the metabolic disorders and infectious diseases are diagnosed during this time compromising the lactation overall success. Even though it has been proposed methods to evaluate the transition programme in multiparous dairy cows, the same has not been observed for dairy heifers. The present paper aimed at determining which Dairy Herd Improvement (DHI) metrics have the largest impact on first test day milk yield (FT_MILK) of primiparous dairy cows as well as building and comparing predictive models of FT_MILK from animals in this category. We used information from the first test day of 3267 Holstein primiparous dairy cows collected between 2014 and 2017. Data were split into training (n = 2345) and validation (n = 780) data set that were used to estimate model parameters and to evaluate the models, respectively. Variables associated to FT_MILK were identified using regularized regression via elastic net. Three types of models were evaluated: multivariate linear regression (MLR), random forest (RF), and artificial neural network (ANN). The resulting models were evaluated based on six fit-statistics and 10-fold cross validation. In addition, Pearson correlation coefficient and Lin's concordance correlation coefficient were calculated between predicted and observed values, as well as the comparison between observed and predicted median FT_MILK. Statistical significance was declared at an error level α

Ecological intensification is a new concept in agriculture that addresses the double challenge of maintaining a level of production sufficient to support needs of human populations and respecting the environment in order to conserve the natural world and human quality of life. This article adapts this concept to fish farming using agroecological principles and the ecosystem services framework. The method was developed from the study of published literature and applications at four study sites chosen for their differences in production intensity: polyculture ponds in France, integrated pig and pond polyculture in Brazil, the culture of striped catfish in Indonesia and a recirculating salmon aquaculture system in France. The study of stakeholders' perceptions of ecosystem services combined with environmental assessment through Life Cycle Assessment and Emergy accounting allowed development of an assessment tool that was used as a basis for co-building evolution scenarios. From this experience, ecological intensifica-tion of aquaculture was defined as the use of ecological processes and functions to increase productivity, strengthen ecosystem services and decrease disservices. It is based on aquaecosystem and biodiversity management and the use of local and traditional knowledge. Expected consequences for farming systems consist of greater autonomy, efficiency and better integration into their surrounding territories. Ecological intensification requires territorial governance and helps improve it from a sustainable development perspective. International audience

Abstract Date palm trees are an important economic crop in the Arabian Peninsula, Middle East, and North Africa. Counting the numbers and determining the locations of date palm trees are important for predicting the date production and plantation management. In this paper, we exploit the effective use of the state-of-the-art CNN, YOLO-V5, in detecting date palm trees in images captured by a camera onboard of a drone flying 122 m above farmlands in the Northern Emirates of the United Arab Emirates (UAE). In the dataset preparation process, we randomly selected 125 captured images and divided them into three datasets: training (60%), validation (20%), and testing (20%). The images of date palm trees in the training and validation datasets were manually annotated and those in the training dataset were used to train the four sub-versions of YOLO-V5 CNNs. The validation dataset was used during the training process to assess how well the network was performing during training. Finally, the images in the test dataset were used to evaluate the performance of the trained models. The results of using YOLO-V5 for date palm tree detection in drone imagery are compared with those obtainable with other popular CNN architectures, YOLO-V3, YOLO-V4, and SSD300, both quantitatively and qualitatively. The results show that for the amount of training data used, YOLO-V5m (medium depth) model records the highest accuracy, resulting in a mean average precision of 92.34%. Further it provides the ability to detect and localize date palm trees of different sizes, in crowded, overlapped environments and areas where the date palm tree distribution is sparse. Therefore, it is concluded that the method can be a useful component of an automated plantation management system and help forecast the quantities of date production and condition monitoring of the date palm trees.

International audience; Monitoring the position of animals in the outdoors can provide useful information in ecology and in agriculture. A common method is to use active sensors, such as GPS, to record their positions at constant intervals of time. But using active sensors can rapidly become expensive when several animals have to be monitored at the same time. Another method is to use a passive sensor to monitor the entire flock of animals. In this article, we propose a method to process images taken by a commercial drone in order to automate the tracking of animal activities. We developed a method that automatically detects goats from the images and tracks their activity using a combination of thresholding and supervised classification methods. We tested our method on 571 drone images taken over 11 days and found a sensitivity of 74% for animal detection and 78.3% for activity detection.

Protected cultivation or Controlled Environment Agriculture (CEA) system has increased tenfold in the last 25 years thanks to tremendous scientific and technical breakthroughs, mainly directed to increasing crop yields and adapting to tough ambient conditions. Currently, greenhouse areas are still spreading and the CEA system is one of the keystones for the agriculture forecasting scenario. However, the environmental acceptability of this very intensive agro-ecosystem is now being questioned. Energy inputs are source of the main economic and environmental weakness, especially for high-tech greenhouses, where IPM is well-established. Pesticides, common in low-cost greenhouses systems, are a major barrier-to IPM. To design and manage more robust CEA systems, both technological and ecological approaches have been chosen. This allowed for increased consideration of IPM issues among global greenhouse engineering innovation and better use of greenhouse system capacities to enhance dedicated IPM high-tech tools and management practices. On the other hand, ecological concepts were used to determine and characterize complex biotic interactions that lead to question the tenant of biological control as soon as IPM is implemented in greenhouses with sub-optimum physical pest control means. More specifically, microclimate at the boundary layer level has been investigated both from a physical and biological point of view in order to determine the best climate preferences of the main pests and beneficials. By the same token, the efficiency of diverse biocontrol plants to provide accurate shelter to natural enemies has been assessed International audience