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Digital agriculture (DA) can contribute solutions to meet an increase in healthy, nutritious, and affordable food demands in an efficient and sustainable way. South America (SA) is one of the main grain and protein producers in the world but the status of DA in the region is unknown. A systematic review and case studies from Brazil, Argentina, Uruguay, and Chile were conducted to address the following objectives: (1) quantify adoption of existing DA technologies, (2) identify limitations for DA adoption; and (3) summarise existing metrics to benchmark DA benefits. Level of DA adoption was led by Brazil and Argentina followed by Uruguay and at a slower rate, Chile. GPS guidance systems, mapping tools, mobile apps and remote sensing were the most adopted DA technologies in SA. The most reported limitations to adoption were technology cost, lack of training, limited number of companies providing services, and unclear benefits from DA. Across the case studies, there was no clear definition of DA. To mitigate some of these limitations, our findings suggest the need for a DA educational curriculum that can fulfill the demand for job skills such as data processing, analysis and interpretation. Regional efforts are needed to standardise these metrics. This will allow stakeholders to design targeted initiatives to promote DA towards sustainability of food production in the region. The status of digital agriculture (DA) in South America (SA) is unknown. This article quantified adoption of existing DA technologies, identified limitations for DA adoption, implementing a literature review and case studies. Sustainable metrics were summarised to benchmark DA impact. GPS guidance systems, mapping tools, mobile apps and remote sensing were the most adopted DA technologies in SA. The most reported limitations to adoption were technology cost, lack of training, limited number of companies providing services, and unclear benefits from DA. The proposed metrics to benchmark DA benefits will allow stakeholders to design targeted initiatives to promote DA towards sustainability of food production in the region. [ABSTRACT FROM AUTHOR]

Differences between seven authentic samples of Citrus sinensis var. Valencia peel (albedo and flavedo) and juices from Spain and Uruguay, in addition to a concentrate obtained from Brazil, were investigated by untargeted metabolic profiling. Sixty-six metabolites were detected by nano-liquid chromatography coupled to a high-resolution electrospray-ionization quadrupole time-of-flight mass spectrometer (nLC-ESI-qTOF-MS) belonging to phenolic acids, coumarins, flavonoid glycosides, limonoids, terpenes, and fatty acids. Eleven metabolites were detected for the first time in Citrus sinensis and identified as citroside A, sinapic acid pentoside, apigenin-C-hexosyl-O-pentoside, chrysoeriol-C-hexoside, di-hexosyl-diosmetin, perilloside A, gingerol, ionone epoxide hydroxy-sphingenine, xanthomicrol, and coumaryl alcohol-O-hexoside. Some flavonoids were completely absent from the juice, while present most prominently in the Citrus peel, conveying more industrial and economic prospects to the latter. Multivariate data analyses clarified that the differences among orange parts overweighed the geographical source. PCA analysis of ESI-(−)-mode data revealed for hydroxylinoleic acid abundance in flavedo peel from Uruguay the most distant cluster from all others. The PCA analysis of ESI-(+)-mode data provided a clear segregation of the different Citrus sinensis parts primarily due to the large diversity of flavonoids and coumarins among the studied samples. [ABSTRACT FROM AUTHOR]

Highlights • Model estimated yield was not more accurate than a simple regression estimation. • Assimilating remote sensing data into a crop model correctly estimated yield spatial variability. • Remote sensing-model data assimilation could be useful to recreate yield maps from archive data. • Adjusted model parameters during assimilation could provide useful derived metrics of the site. Abstract Early prediction of crop yields has been a challenge frequently resolved through the combination of remote sensing data and crop models. The aim of this study was to evaluate two different methods based on remote sensing data for predicting winter wheat (Triticum aestivum L.) yield at field scale. We compared the accuracy of: (i) a simple regression method between different vegetation indices at anthesis and grain yield, and (ii) a crop model method based on optimization of two parameters (specific leaf nitrogen and initial aboveground-biomass) using time series of vegetation indices. Vegetation indices were derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images acquired for two growing seasons (2013, 2014) across 22 fields in south western Uruguay with an average size of 128 ha. At all sites, leaf area index (LAI) was measured during a field campaign, and grain yield was measured with yield monitors on harvesters. The simple regression method (SRM) achieved higher accuracy than the model-based method (CMM) for the estimation of yield at field scale (RMSE = 966 kg ha−1 and RMSE = 1532 kg ha−1, respectively). When deviations between observed and estimated yields were evaluated at pixel (30 × 30 m) level, the model-based method was better at detecting existing spatial variability in grain yield and at identifying areas of different yield potential. Even though both methods have limited utility to estimate yield at field scale with very high accuracy due to large RMSE, the methodologies are suitable to predict harvest volumes at large agricultural areas or at country level, and to construct synthetic yield maps reflecting within field variability. Higher temporal resolution of images would improve accuracy in estimating yield and spatial variability at field scale. [ABSTRACT FROM AUTHOR]

CONTEXT Global climate change is resulting in more frequent and more damaging extreme events affecting the performance of production systems. It is imperative to develop good season-specific crop management recommendations to help farmers to improve their adaptive capacity to a changing climate one season at a time. We aimed to evaluate the skill of the International Research Institute for Climate and Society (IRI) seasonal precipitation forecasts and the interaction between the forecasted seasonal precipitation scenarios and management practices for rainfed soybean cropping systems using a crop simulation model. We used a crop simulation model (CROPGRO-Soybean) coupled with weather data to assess the potential use of the IRI seasonal precipitation forecasts as a tool to optimize season-specific management strategies for rainfed soybean in Uruguay. We used a total of 620–668 IRI seasonal precipitation forecasts released from 2003 to 2016 for each of the five weather stations located in the main soybean producing area. The analysis was performed for two soybean cropping systems (i.e. , sown as a single crop or as double-cropped soybean), for which we considered combinations of sowing dates and maturity groups (11 sowing dates × 3 maturity groups combinations for each soybean cropping system). The IRI seasonal precipitation forecasts were able to successfully forecast below-normal precipitation scenarios in 77% of the total predictions developed for this scenario considering all weather stations during the study period (2003–2016), while it was less accurate in forecasting above-normal precipitation scenarios (60% of success). We found that earlier sowing dates were a better strategy for years when an above-normal precipitation forecast was released for the December–January-February period (4.7 Mg ha−1 average seed yield). In contrast, delayed sowing dates were more appropriate for below-normal precipitation forecasts (3.7 Mg ha−1 average seed yield). Applying season-specific management practices farmers could potentially increase their soybean yields by up to 0.6 and 1.6 Mg ha−1, in years with below- or above-normal forecasted precipitations, respectively. The benefit of season-specific management will depend on the interaction among all management practices, the effective capacity of farmers to implement it, and the risk profile the farmer adopts and it is exposed to. Here we built a novel approach to assess the impact of considering seasonal precipitation forecasts for optimizing crop production. This assessment provided insights on how farmers can use seasonal precipitation forecasts to optimize rainfed soybean yield for a specific cropping season. [Display omitted] • IRI seasonal forecasts successfully forecasted precipitation scenarios in the region. • Forecast success was higher for below (77%) than for above normal (60%) precipitation. • Dominant farmer management practices are aligned with a risk-averse strategy. • Season-specific management could increase soybean yield between 0.6 and 1.6 Mg ha−1. [ABSTRACT FROM AUTHOR]