Disease detection and control is thus one of the main objectives of vineyard research in France. Monitoring diseases manually is fastidious and time consuming, so current research aims to develop an automatic detection of vineyard diseases. This project explored the use of a high-resolution multi-spectral camera embedded on a UAV (Unmanned Aerial Vehicle) to identify the infected zones in a field. In-field spectrometry studies were performed to identify the best spectral bands for the sensor design. The best models were found to be the function of the grapevine variety considered and the 520-600-650-690-730-750-800 nm bands were found to be the most efficient for all types of grapevines, with an overall classification accuracy of more than 94%.

Knowledge on spatial variability of soil properties is useful for the rational use of inputs, as in the site specific application of lime and fertilizer. Crop-livestock-forest integrated systems (CLFIS) provide a strategy of sustainable agricultural production which integrates annual crops, trees and livestock activities on a same area and in the same season. Since the lime and fertilizer are key factors for the intensification of agricultural systems in acid-soil in the tropics, precision agriculture (PA) is the tool to improve the efficiency of use of these issues. The objective of this research was to map and evaluate the spatial variability of soil properties, liming and fertilizer need of a CLFIS. The field study was carried out in a 30 ha area at Embrapa Pecuária Sudeste in São Carlos, SP, Brazil. Soil samples were collected at 0–0.2 m depth, and each sample represented a paddock. The spatial variability of soil properties and site-specific liming and fertilizer needs were modeled using semi-variograms, the soil fertility information were modeled. Spatial variability soil properties and site specific liming and fertilizer need were modeled by kriging and inverse distance weighting (IDW) techniques. Another approach used was based on lime and fertilizer recommendation considering the paddocks as the minimum management unit. The results showed that geostatistics and GIS were useful tools for revealing soil spatial variability and supporting management strategies. Soil nutrients were used to classify the soil spatial distribution map and design site-specific lime and fertilizer application zones. Spatial analyses of crop needs and requirement can provide management tools for avoiding potential environmental problems, caused by unbalanced nutrient supplies.

In the last decades, a new awareness on human nutrition has increased and the concept of ‘food’ has changed from ‘source of nutrients for body’s needs’ to ‘health promoter’. Fruits and vegetables have always been considered beneficial for human health. More recent studies have demonstrated that bioactive components are also present in animal-derived foods, such as milk and dairy products. A broader concept of ‘nutritional safety’ implies the knowledge of how the nutrients contained in animal-derived foods positively affect human health, and how to increase their content. The improvement of dairy products fatty acid (FA) composition can involve strategies in animal nutrition. This review aims to discuss the role of FAs supplementation in ameliorating milk fat composition, environmental impact and animal health. In particular, we have focused on the role of n-3 and CLA FAs and how animal nutrition strategies can positively affect both human and animal health. Several studies have demonstrated that through adequate nutritional strategies is possible to manipulate and improve FA composition of milk and derived products (cheese). Moreover, feeding animals with n-3 FAs has proved to reduce emission of methane (CH4), but further nutritional strategies are needed in order to address this crucial environmental issue. In relation to animal health, n-3 FAs have been proved to modulate immune and inflammatory response in dairy ruminants. Recent studies have addressed the potential programming effects of increased maternal n-3 polyunsaturated FAs intake on offspring’s immune functions showing that feeding bioactive FAs to pregnant animals can affect progeny health status.

Phosphorus (P) is an essential mineral that needs to be supplied in sufficient quantities for maintenance and growth and milk production in dairy cattle. However, over 60% of the P consumed can be excreted in faeces with a potential to cause environmental pollution. Concern over higher levels of P in intensively managed livestock systems has led to legislation such as the Water Framework Directive in the European Union. In this manuscript, several methods of reducing P pollution are discussed. A major source of environmental P pollution has been overfeeding P mainly due to addition of ‘safety margin’ over the animal's requirement and concerns related to fertility. Matching the animal's requirement and feeding in groups so that animals at the same physiological status are fed according to their requirement has a potential to reduce P excretion significantly. P can also be reduced by matching available P with the metabolizable energy content of the diet because more P can be incorporated into milk when P is utilized by rumen microbes, which are limited by energy. Plants contain phytate bound P that need to be broken up before they can be absorbed by the animal. Although ruminants can digest phytate, use of phytase enzyme could help either directly by acting on phytate P or improvement of feed digestibility. Pasture management can lead to improved nutrient cycling, particularly if the soil is deficient in P. However, overfertilizing pasture could result is higher runoff of dissolved reactive P. Management practices that leave adequate forage residue on the surface such as rotational grazing will improve infiltration and decrease runoff, reducing nutrient losses.

Bulk apparent soil electrical conductivity (ECa) sensors respond to multiple soil properties, including clay content, water content, and salt content (i.e. salinity). They provide a single sensor value for an entire soil profile down to a sensor-dependent measurement depth, weighted by a nonlinear response function. Because of this, it is generally difficult to elucidate strong relationships between ECa and the measured properties of individual soil layers. This research investigated inversion of the equations that govern the ECa-depth response relationship to reconstruct the soil conductivity in profile layers using data collected in multiple fields in the Midwest US. Layer conductivities obtained by inversion were first validated by comparison with true conductivities measured as a function of depth with an ECa-sensing penetrometer. Then, the validated layer conductivities were related to laboratory- measured soil properties. Inversion worked well but sometimes required iterative adjustment of initial conditions and other input parameters to obtain best results. Strong linear relationships (r2≥0.76) were obtained between inversion-estimated and measured layer conductivity data in all cases, sometimes with a truncated depth range. Layer conductivity data was successfully used to estimate soil texture fractions in the two alluvial fields examined. This was not the case for a claypan soil field, where there appeared to be parameters other than texture strongly affecting the EC response. Further examination of this approach is warranted to potentially provide improved ways to estimate depth-variable soil properties using ECa.

As the interest in Unmanned Aerial Systems (UAS) has increased, so has the interest in the application of these systems for use in agriculture. A variety of sensors, including Multi-Spectral, Near-Infrared, Thermal, and True-Color have the potential to benefit farmers when mounted to a UAS. But as this is an emerging field, there is little data available to demonstrate their use for early detection of plant diseases in crop production. In 2016, a preliminary study was launched to examine the potential of using aerial imagery from UAS to detect diseases in soybean crops. Two irrigated fields in Alabama were selected: Experiment 1, a 50-hectare field, and Experiment 2, a 5-hectare field. Each trial consisted of replicated plots using two foliar fungicide treatments and an untreated control. Aerial imagery (multi-spectral and true-color) was collected on a biweekly basis during this study. Using multi-spectral imagery, both the Normalized Difference Vegetative Index (NDVI) and Normalized Difference Red Edge Index (NDRE) were generated and compared to direct observations in the field. Disease severity of soybean rust, charcoal rot and Cercospora leaf blight were monitored on a biweekly basis and correlated to the UAS imagery. Preliminary results indicated plant stress can be detected using UAS imagery. In Experiment 1, stress associated with charcoal rot was visible in the NDRE imagery. This was of interest because at the time of flight, while it was noted that plants were yellowing, the root and stem disease itself had not been identified by direct observation. In Experiment 2, soybean rust was observed by direct observation and in both the NDRE and NDVI imagery. Soybean rust did have a negative impact on yield in Experiment 2, however severe drought conditions may have negated the yield loss likely caused by the development of charcoal rot in Experiment 1.

Vineyards worldwide are subjected to spatial variability, which can be exhibited in both low and high yield areas. We developed a variable rate drip irrigation (VRDI) concept to reduce such variability. The VRDI system divides the vineyard into 30×30-meter irrigation zones, enabling individual irrigation of each zone according to normalized difference vegetation index (NDVI) maps. The first VDRI system was installed in on a 1.2-hectare vineyard Syrah red grape in Israel. With the VRDI system, the yield, leaf area index (LAI), canopy size, water potential, and primary juice chemical analysis results were very uniformed in comparison to previous years without the system.

This paper proposes a simple method for categorizing fields on a regional level, with respect to intra-field variations. It aims to identify fields where the potential benefits of applying precision agricultural practices are highest from an economic and environmental perspective. The categorization is based on vegetation indices derived from Sentinel-2 satellite imagery. A case study on 7678 winter wheat fields is presented, which employs open data and open source software to analyze the satellite imagery. Furthermore, the method can be automated to deliver categorizations at every update of satellite imagery, hence coupling the geospatial data analysis to direct improvements for the farmers, contractors, and consultants.

The research concerns the changes of spectral characteristics of reflected radiation (360 to 1 000 nm) of spring wheat leaves under nitrogen deficiency and moderate soil drought. The efficiency of factorial influence (η2) on chlorophyll index was equal to 20% and 4% under nitrogen and water deficiency, respectively. Most significantly soil drought influenced the water index WRI (η2=55%) and the light diffusion index R800 (η2=28%), which was caused by changes in leaf structure. At low levels of nitrogen supply, these parameters did not change or changed only slightly (η2=2%). It may be deduced that the data base for crop monitoring in precision farming systems must contain a series of optical criteria for assessing specific and non-specific changes in optical characteristics of a crop canopy under the impact of various stress factors.

In this study, we investigated the potential of airborne imaging spectroscopy for in-season grassland yield estimation. We utilized an unmanned aerial vehicle and a hyperspectral imager to measure radiation, ranging from 455 to 780 nm. Initially, we assessed the spectral signature of five typical grassland species by principal component analysis, and identified a distinct reflectance difference, especially between the erectophil grasses and the planophil clover leaves. Then, we analyzed the reflectance of a typical Norwegian sward composition at different harvest dates. In order to estimate yields (dry matter, DM), several powered partial least squares (PPLS) regression and linear regression (LR) models were fitted to the reflectance data and prediction performance of these models were compared with that of simple LR models, based on selected vegetation indices and plant height. We achieved the highest prediction accuracies by means of PPLS, with relative errors of prediction from 9.1 to 11.8% (329 to 487 kg DM ha−1) for the individual harvest dates and 14.3% (558 kg DM ha−1) for a generalized model.

This paper will present a dynamic Variable Rate Irrigation System developed by the University of Georgia. The system consists of the EZZone management zone delineation tool, the UGA Smart Sensor Array (UGA SSA) and an irrigation scheduling decision support tool. An experiment was conducted in 2015 and 2016 in two different peanut fields to evaluate the performance of using the UGA SSA to dynamically schedule Variable Rate Irrigation (VRI). For comparison reasons strips were designed within the fields. These strips were irrigated according to either UGA SSA or Irrigator Pro recommendations. The results showed that Irrigator Pro is a conservative irrigation method which results in high yields. On the other hand the UGA SSA recommendations worked very well with the VRI system and in both years it recommended an average of 25% less irrigation water than the Irrigator Pro.

A phenotype describes the outcome of the interacting development between the genotype of an individual and its specific environment throughout life. Animal breeding currently exploits large data sets of phenotypic and pedigree information to estimate the genetic merit of animals. Here we describe rapid, low-cost phenomic tools for dairy cattle. We give particular emphasis to infrared spectroscopy of milk because the necessary spectral data are already routinely available on milk samples from individual cows and herds, and therefore the operational cost of implementing such a phenotyping strategy is minimal. The accuracy of predicting milk quality traits from mid-infrared spectroscopy (MIR) analysis of milk, although dependent on the trait under investigation, is particularly promising for differentiating between good and poor-quality dairy products. Many fatty acid concentrations in milk, and in particular saturated fatty acid content, can be very accurately predicted from milk MIR. These results have been confirmed in many international populations. Albeit from only two studied populations investigated in the RobustMilk project, milk MIR analysis also appears to be a reasonable predictor of cow energy balance, a measure of animal robustness; high accuracy of prediction was not expected as the gold standard method of measuring energy balance in those populations was likely to contain error. Because phenotypes predicted from milk MIR are available routinely from milk testing, longitudinal data analyses could be useful to identify animals of superior genetic merit for milk quality and robustness, as well as for monitoring changes in milk quality and robustness because of management, while simultaneously accounting for the genetic merit of the animals. These sources of information can be very valuable input parameters in decision-support tools for both milk producers and processors.