The aim of this study was to identify genomic regions that associated with beef tenderness in Nellore cattle. Phenotypes were obtained according to the standard USDA Quality Grade (1999). Data from 909 genotyped Nellore bulls were used in the Genome-Wide Association Study (GWAS) undertaken using a single-step approach including also a pedigree file composed of 6276 animals. The analyses were performed using the Blupf90 software, estimating the effect of genomic windows of 10 consecutive markers. The GWAS results identified 18 genomic regions located on 14 different chromosomes (1, 4, 6, 7, 8, 10, 18, 19, 20, 21, 22, 25, 26 and 29), which explained more than 1% of the total additive genetic variance; several candidate genes were located in these regions including SLC2A9, FRAS1, ANXA3, FAM219A, DNAI, AVEN, SHISA7, UBE2S, CDC42EP5, CNTN3, C16orf96, UBALD1, MGRN1 and SNORA1 With the single-step GWAS, it was possible to identify regions and genes related to meat tenderness in Nellore beef cattle.

This paper proposes a methodology aiming at using historical yield data to improve yield sampling and yield estimation. The sampling method is based on a collaboration between historical data (at least three years) and yield measurements of the year performed on some sites within the field. It assumes a temporal stability of within field yield spatial patterns over the years. The first factor of a principal component analysis (PCA) is used to summarize the stable temporal patterns of within field yield data and it represents a large part of the variability of the different years assuming yield temporal stability and a high positive correlation between this factor and the yield. This main factor is then used to choose the best sites to sample (target sampling). Yield measurements are then used to calibrate a model that relates yield values to coordinates on the first factor of the PCA. This sampling method was tested on three vine fields (Vitis vinifera L.) in Chile and France with different varieties (Chardonnay, Cabernet Sauvignon and Syrah). For each of these fields, yield data of several years were available at the within field level. After temporal stability of yield patterns was verified for almost all the fields, the proposed sampling method was applied. Results were compared to those of a classical random sampling method showing that the use of historical yield data allows sampling sites selection to be optimized. Errors in yield estimations were reduced by more than 10% in all the cases, except when yield stable patterns are affected by specific events, i.e. early frost occurring on Chardonnay field.

For centuries, animal breeders have very effectively been selecting livestock species, making use of the natural variation that exists within the population. As part of the developments towards broader breeding goals, the RobustMilk project was designed to develop new practical technologies to allow breeders to re-focus their selection to include milk quality and dairy cow robustness and to evaluate the consequences of selection for these traits taking cognisance of various milk production systems. Here we introduce the background to robustness, the value of expanding milk quality analysis (including the possibility of using milk quality characteristics as proxy measures for robustness traits), interactions between robustness and milk quality traits and the need for different breeding tools to enable delivery of these concepts to the industry. Developing a database with phenotypes from research herds across Europe, phenotyping tools using mid-infrared red spectroscopic analysis of milk, and the development of statistical and genomic tools for robustness and milk quality formed the core of the project. In the following papers you will read the outcomes and developments that happened during the project.

Intensive agriculture has the objective to increase nutrients use efficiency. Nitrogen (N) is a key nutrient for crops and the estimations of crop N status allow adjusting the fertilization levels to crop requirements, while reducing the environmental costs and optimizing the benefits for farmers. In this work the N status of wheat in a commercial plot has been monitored, varying the N supply taking into account the variability of the soil. The N content in the cover has been monitored simultaneously by sampling at field level and by using vegetation indices based on reflectance in the red-edge band. The results of the field campaign along a crop growth cycle show that the REP, MTCI, AIVI and CCCI calculated from narrow spectral bands show good linear correlations (R2>0.93) with respect to N content (g·m−2). These indices are stable when passing to broad bands as the case of Sentinel 2 with R2>0.9.

In this paper, novel geospatial services are presented which are able to process on the server-side numerous remote sensing data based on big data frameworks like Hadoop and Rasdaman. The developed system itself features several software modules that orchestrate the different image processing algorithms responsible for the production of consistent value-added maps like canopy greenness and leaf area index. Through distributed multitemporal analysis, the entire crop growth cycle can be continuously monitored through the analysis of time-series observations. These observations cover multiple crop growth cycles, offering invaluable information by linking weather statistical data with the start, the end and the duration of each growth cycle enabling critical decisions by direct comparison with the current crop growth state.

Precision agriculture for banana crops has been little investigated so far. The main difficulty to implement precision agriculture methods lies in the asynchronicity of this crop: after a few cycles, each plant has its own development stage in the field. Indeed, maps of agronomical interest are difficult to produce from plant responses without implementing new methods. The present study explores the feasibility to derive a spatially relevant indicator from the date of flowering and the date of maturity (time to harvest). The time between these dates (TFM) may give insight in spatial distribution of vigor. The study was carried out using production data from 2015 acquired in a farm from Cameroon. Data from individual plants that flowered at different weeks were gathered so as to increase the density of TFM sampling. The temporal variability of TFM, which is induced by weather and operational constraints, was compensated by centering TFM data on their medians (TFMc). The mapping of TFMc was obtained using a classical kriging method. Spatial structures highlighted by TFMc either at the farm level or at the plot level, suggest that such maps could be used to support agronomic decisions.

Nitrous oxide reductase (N2OR), the enzyme responsible for the reduction of N2O to N2 in denitrification, uses copper (Cu) as its cofactor. Its activity is lowered under conditions of Cu deficiency. In general, high organic matter (OM) soil decreases Cu availability. The present study investigated different Cu forms, namely organic (ORG) v. inorganic (INO), and associated concentrations (750, 550, 125, 60 μg Cu/g soil) for their efficacy in affecting denitrification and especially N2OR activity in high OM peat soil in a water saturated anaerobic condition for 24 h. Gas and liquid samples were taken every 8 h and analyzed for NO3−, NO2−, N2O and N2. Inorganic Cu treatments did not affect N transformation rates and N2OR activity among the different treatments (P > 0.05) throughout the incubation compared with the control (CON). The ORG Cu treatments increased NO3− (P < 0.05), NO2− (P < 0.05) and N2O (P < 0.05) transformation rates compared with CON. These changes were ORG Cu dose dependent. N2OR activity increased first in the 750 μg ORG Cu treatment (P < 0.05) during 8 to 16 h followed by the other ORG Cu treatments (P < 0.05) during 16 to 24 h compared with CON. These results highlight the importance of Cu form and concentration on N transformation rate during denitrification. The findings can potentially be applied to systems like soil, wastewater, constructed wetlands, etc., in which reactions of the denitrification pathway are manipulated.

Over-application of nitrogen (N) in rice (Oryza sativa L.) production in China is common, leading to low N use efficiency (NUE) and high environmental risks. The objective of this work was to evaluate the ability of the CERES-Rice crop growth model to simulate N response in the cool climate of Northeast China, with the long term goal of using the model to develop optimum N management recommendations. Nitrogen experiments were conducted from 2011–2015 in Jiansanjiang, Heilongjiang Province in Northeast China. The CERES-Rice model was calibrated for 2014 and 2015 and evaluated for 2011 and 2013 experiments. Overall, the model gave good estimations of yield across N rates for the calibration years (R2=0.89) and evaluation years (R2=0.73). The calibrated model was then run using weather data from 2001–2015 for 20 different N rates to determine the N rate that maximized the long term marginal net return (MNR) for different N prices. The model results indicated that the optimum mean N rate was 120–130 kg N ha–1, but that the simulated optimum N rate varied each year, ranging from 100 to 200 kg N ha–1. Results of this study indicated that the CERES-Rice model was able to simulate cool season rice growth and provide estimates of optimum regional N rates that were consistent with field observations for the area.

To explore the potential for site-specific crop management in Australian potato production, soil apparent electrical conductivity (ECa) and high resolution elevation data were used to first define the variation in soil and landscape resources in two regions in Tasmania. Variation in crop production was estimated using in-season aerial multispectral VIS-NIR reflectance measurements and then measured using a first generation on-harvester yield monitoring system. During the season, soil and crop physical, chemical and pathogen properties were measured to groundtruth the sensor-derived data. Substantial within-field and between field variation was found in soil physical, chemical and pathogen properties, elevation and crop yield. The average potato yield for the study fields was 64 t/ha, with over three-fold within-field variation recorded. The in-season aerial crop reflectance significantly correlated with soil physical variability and pathogen load when gathered early in the season and to variation in plant physical and chemical properties, as well as important soil nutrient properties and crop yield when gathered from week 14 onwards. A set of general rules for instigating Site-specific crop management (SSCM) in potato production has been devised based initially on nutrient and pathogen management with irrigation management as an option.

Activity of brown and beige adipocytes could contribute to breed differences in fat deposition. Therefore, we compared the abundance of markers for adipocyte types in steers from three cattle breeds differing in fat deposition (Japanese Black, Holstein, Charolais). Markers for white (leptin (LEP)), beige (transmembrane protein 26 (TMEM26), uncoupling protein 1 (UCP1)), and brown adipocytes (Zic family member 1 (ZIC1), UCP1) were analysed by quantitative reverse transcription PCR in subcutaneous fat (SCF), intramuscular fat (IMF), intermuscular fat (IRMF), perirenal fat (PF) and visceral fat (VF). LEP messenger RNA (mRNA) was less abundant in VF compared with other depots (P<0.05). TMEM26 was weakly but evenly expressed in all depots in all animals, whereas UCP1 mRNA showed higher individual variation in some depots. ZIC1 was not detectable in VF and PF but abundant in SCF, IMF and even more abundant in IRMF (P<0.05). No significant breed differences were detected. Using antibodies against UCP1, TMEM26 and ZIC1, we demonstrated that fat depots of 26-month-old cattle still comprise different adipocyte types. However, our results did not support the hypothesis that higher energy expenditure associated with higher abundance or activity of beige or brown adipocytes contributed to differences in fat deposition.

High spatial variability of soil properties restricts the benefits of process-oriented modelling for management recommendations on field scale due to rare information about the soil inventory and its distribution. Geo-electrical mapping provides with a certain spatial pattern, but results are influenced by various factors. The model HERMES was applied to 60 of altogether 80 soil sampling points of a well-documented field in North-Rhine Westphalia characterised by a wide range of soil texture. Validation of HERMES resulted in satisfactory root mean square errors (RMSE) for yield (0.76 t ha-1), water (34.70 mm) and nitrogen in soil (51.44 kg ha-1) over the whole simulation period. At the same field, values of conducted electrical conductivity (ECa) mapping ranged from 20 to 90 mS m-1 and have been assigned to the soil sampling points to proof statistical relations. Clay and sand contents of three soil layers were highly correlated with measured ECa values. Derived regression models show R2 values between 0.73 and 0.89. The subsequent calculation of soil texture at points of ECa mapping produced an improved resolution of this key value to initialize model simulation.

Yield maps are a powerful tool with regard to managing upcoming crop productions but can contain a large amount of defective data that might result in misleading decisions. The objective of this work is to help improve and compare yield data filtering algorithms by generating simulated datasets as if they had been acquired directly in the field. Two stages were implemented during the simulation process (i) the creation of spatially correlated datasets and (ii) the addition of known yield sources of errors to these datasets. A previously published yield filtering algorithm was applied on these simulated datasets to demonstrate the applicability of the methodology. These simulated datasets allow results of yield data filtering methods to be compared and improved.