Use of remote sensing‑derived fPAR data in a grapevine simulation model for estimating vine biomass accumulation and yield variability at sub‑field level

Grapevine simulation models are mostly used to estimate plant development, growth and yield at plot scale. However, the spatial variability of pedologic and micro-climatic conditions can influence vine growth, leading to a sub-field heterogeneity in plant vigor and final yield that may be better estimated through the assimilation of high spatial resolution data in crop models. In this study, the spatial variability of grapevine intercepted radiation at fruit-set was used as input for a grapevine simulation model to estimate the variability in biomass accumulation and yield in two Tuscan vineyards (Sites A and B). In Site A, the model, forced with intercepted radiation data as derived from the leaf area index (LAI), measured at canopy level in three main vigor areas of the vineyard, provided a satisfactory simulation of the final pruning weight (r2 = 0.61; RMSE = 19.86 dry matter g m−2). In Site B, Normalized Difference Vegetation Index (NDVI) from Sentinel-2A images was firstly re-scaled to account for canopy fraction cover over the study areas and then used as a proxy for grapevine intercepted radiation for each single pixel. These data were used to drive the grapevine simulation model accounting for spatial variability of plant vigor to reproduce yield variability at pixel scale (r2 = 0.47; RMSE = 75.52 dry matter g m−2). This study represents the first step towards the realization of a decision tool supporting winegrowers in the selection of the most appropriate agronomic practices for reducing the vine vigor and yield variability at sub-field level

SPECTRAL AGROMETEOROLOGICAL MODELING ADAPTED BY MEANS OF SIMPLIFIED TRIANGLE METHOD FOR SOYBEAN IN PARANÁ STATE – BRAZIL

Agriculture is an economic activity with high dependence on weather and climate. Special geotechnology and agrometeorological modeling can be used to optimize productivity in regional and national systems, while minimizing costs. The aim was to test the agrometeorological model for estimating crop soybean yield proposed by Doorenbos and Kassam (1979), using only spectral data as input variable in the model obtained by a simplified triangle method applied in Paraná state, for crop years 2002/03 to 2011/12. A high accuracy of the data was found, the model values for the parameter d1 ("d1" modified Willmott) were between 0.8 and 0.95, whereas the root mean squared error showed that there was low variation between 30.81 to 116.88 (kg ha-1) and the p-value was used as the indicator significance of the model at the level of 5%, indicating that there was no statistically significant difference between the estimated and observed data, this means that the average of the data estimated by the model were statistically equal the average of the observed data. Thus, we can say that images of remote sensing can be used as tools in the absence of surface information, in agrometeorological modeling to estimate crop soybean yield

Remote sensing and apparent electrical conductivity to characterize soil water content

2016 Fall.Includes bibliographical references.Improvement in water use efficiency of crops is a key component in addressing the increasing global water demand. The time and depth of the soil water monitoring are essential when defining the amount of water to be applied to irrigated crops. Precision irrigation (PI) is a relatively new concept in agriculture, and it provides a vast potential for enhancing water use efficiency while maintaining or increasing grain yield. As part of site-specific farming, PI needs to be explored, tested, and evaluated which continues to be a research issue. Neutron probes (NPs) have consistently been used for studies as a robust and accurate method to estimate soil water content (SWC). Remote sensing derived vegetation indices have been successfully used to estimate variability of Leaf Area Index and biomass, which are related with root water uptake. Crop yield has not been evaluated on a basis of SWC as explained by NPs in time and at different depths. One among many challenges in implementing PI is the reliable characterization of the soil water content (SWC) across spatially variable fields. For this purpose, commercial retailers are employing apparent soil electrical conductivity (ECa) to create irrigation prescription maps. However, the accuracy of this method has not been properly studied at the field scale. The objectives of this study were (1) to determine the optimal time and depth of SWC and its relationship to maize grain yield (2) to determine if satellite-derived vegetation indices coupled with SWC could further improve the relationship between maize grain yield and SWC (3) to assess the potential of ECa measurement to characterize spatial distribution of SWC at field scale, and (4) to determine whether soil properties coupled with ECa could further improve the characterization of the SWC. For objectives 1 and 2, the study was conducted on maize (Zea Mays L.) irrigated in two fields in northern Colorado. Soil water data was collected at five soil depths (30, 60, 90, 120 and 150 cm), 21 and 12 times at Site I and II, respectively. Three vegetation indices were calculated on seven dates (Emergence to R3). Maize grain yield was harvested at the physiological maturity at each NPs location. Automated model selection of SWC readings to assess maize yield consistently selected three dates spread around reproductive growth stages for most depths (p value < 0.05). For objectives 3 and 4, the study was conducted on two fields located in northeastern Colorado. In-field SWC was measured using neutron probes at 41 and 31 locations for Site I and II respectively. Soil ECa measurements were acquired using Geonics EM38-MK2 unit. In addition, cation exchange capacity, clay, organic matter and salt content were coupled with soil ECa to estimate SWC. Data analysis was performed using the statistical software R. Statistical correlations and multiple linear regressions were obtained from the properties that were statistically significant (p value < 0.05). Results of the study showed that the SWC readings at the 90 cm depth had the highest correlations with maize yield, followed closely by the 120 cm. When coupled with remote sensing data, models improved by adding vegetation indices representing the crop health status right before the reproductive growth stage (V9). Thus, SWC monitoring at reproductive stages combined with vegetation indices could be a tool for improving maize irrigation management. Likewise, the SWC was found to be statistically different across ECa derived zones, indicating that ECa was able to accurately characterize average differences in SWC across management zones. Organic matter and salt content significantly improved the SWC assessment when combined with the ECa. The development of prescription maps for variable rate irrigation should be tailor made depending on the specific field characteristics influencing SWC

Agricultural Insurance Schemes II

Index insurances, diversely from traditional agricultural insurances, do not refer to the actual farm losses but to the losses evaluated from an index. The study evaluates the feasibility of index insurances for EU and makes a cross-validation based on the yield loss risk calculated from FADN data. Premiums have been estimated for a Regional Yield Insurance (RYI) for FADN regions and a number of arable crops. Some meteorological, agro-meteorological and NDVI indicators were also analysed according to the model of the area yield-tailored insurance. From the statistical analysis the indicators do not explain yields optimally. Due to the strong heterogeneity within the EU regions, a meteorological yield-tailored index could have a better explanation capacity at a more disaggregated level. FADN data are used to compute and map the risk of yield reduction for major field crops and of income reduction by farm type. The cross validation of area yield insurance consisted on the calculation of the risk with FADN data with and without insurance. Results show that the risk reduction capacity of yield area index for the case analysed is not very high, but in some regions the risk can be reduced up to a 68%. The risk reduction capacity of other indexes is expected to be lower than the yield area index. Finally, the study shows that index products efficiency is relatively low at farm level due to the European heterogeneity of climates and geography and to the large geographical scale that had to be used in the study. So, index products could be more efficient for reinsurance that works at aggregated level.JRC.G.3-Monitoring agricultural resource

Site-Specific Nutrient Management

The concept of nitrogen gap (NG), i.e., its recognition and amelioration, forms the core of this book entitled Site-Specific Nutrient Management (SSNM). Determination of the presence of an NG between fields on a farm and/or within a particular field, together with its size, requires a set of highly reliable diagnostic tools. The necessary set of diagnostic tools, based classically on pedological and agrochemical methods, should be currently supported by remote-sensing methods. A combination of these two groups of methods is the only way to recognize the factors responsible for yield gap (YG) appearance and to offer a choice of measures for its effective amelioration. The NG concept is discussed in the two first papers (Grzebisz and Łukowiak, Agronomy 2021, 11, 419; Łukowiak et al., Agronomy 2020, 10, 1959). Crop productivity depends on a synchronization of plant demand for nitrogen and its supply from soil resources during the growing season. The action of nitrate nitrogen (N–NO3), resulting in direct plant crop response, can be treated by farmers as a crucial growth factor. The expected outcome also depends on the status of soil fertility factors, including pools of available nutrients and the activity of microorganisms. Three papers are devoted to these basic aspects of soil fertility management (Sulewska et al., Agronomy 2020, 10, 1958; Grzebisz et al., Agronomy 2020, 10, 1701; Hlisnikovsky et al., Agronomy 2021, 11, 1333). The resistance of a currently cultivated crop to seasonal weather variability depends to a great extent on the soil fertility level. This aspect is thoroughly discussed for three distinct soil types and climates with respect to their impact on yield (Hlisnikovsky et al., Agronomy 2020, 10, 1160—Czech Republic; Wang et al., Agronomy 2020, 10, 1237—China; Łukowiak and Grzebisz et al., Agronomy 2020, 10, 1364—Poland). In the fourth section of this book, the division a particular field into homogenous production zones is discussed as a basis for effective nitrogen management within the field. This topic is presented for different regions and crops (China, Poland, and the USA) (Cammarano et al., Agronomy 2020, 10, 1767; Panek et al., Agronomy 2020, 10, 1842; Larson et al., Agronomy 2020, 10, 1858)

Multiscale Assimilation of Sentinel and Landsat Data for Soil Moisture and Leaf Area Index Predictions Using an Ensemble-Kalman-Filter-Based Assimilation Approach in a Heterogeneous Ecosystem

Data assimilation techniques allow researchers to optimally merge remote sensing observations in ecohydrological models, guiding them for improving land surface fluxes predictions. Presently, freely available remote sensing products, such as those of Sentinel 1 radar, Landsat 8 sensors, and Sentinel 2 sensors, allow the monitoring of land surface variables (e.g., radar backscatter for soil moisture and the normalized difference vegetation index (NDVI) and for leaf area index (LAI)) at unprecedentedly high spatial and time resolutions, appropriate for heterogeneous ecosystems, typical of semiarid ecosystems characterized by contrasting vegetation components (grass and trees) competing for water use. A multiscale assimilation approach that assimilates radar backscatter and grass and tree NDVI in a coupled vegetation dynamic-land surface model is proposed. It is based on the ensemble Kalman filter (EnKF), and it is not limited to assimilating remote sensing data for model predictions, but it uses assimilated data for dynamically updating key model parameters (the ENKFdc approach), including saturated hydraulic conductivity and grass and tree maintenance respiration coefficients, which are highly sensitive parameters of soil-water balance and biomass budget models, respectively. The proposed EnKFdc assimilation approach facilitated good predictions of soil moisture, grass, and tree LAI in a heterogeneous ecosystem in Sardinia for a 3-year period with contrasting hydrometeorological (dry vs. wet) conditions. Contrary to the EnKF-based approach, the proposed EnKFdc approach performed well for the full range of hydrometeorological conditions and parameters, even assuming extremely biased model conditions with very high or low parameter values compared with the calibrated ("true") values. The EnKFdc approach is crucial for soil moisture and LAI predictions in winter and spring, key seasons for water resources management in Mediterranean water-limited ecosystems. The use of ENKFdc also enabled us to predict evapotranspiration and carbon flux well, with errors of less than 4% and 15%, respectively; such results were obtained even with extremely biased initial model conditions

Climate Change Impacts on Agriculture in Europe

COST Action 734 was launched thanks to the coordinated activity of 29 EU countries. The main objective of the Action was the evaluation of impacts from climate change and variability on agriculture for various European areas. Secondary objectives were: collection and review of existing agroclimatic indices and simulation models, to assess hazard impacts on European agricultural areas; to apply climate scenarios for the next few decades; the definition of harmonised criteria to evaluate the impacts of climate change and variability on agriculture; the definition of warning systems guidelines. Based on the result, possible actions (specific recommendations, suggestions, warning systems) were elaborated and proposed to the end-users, depending on their needs

Symposium franco-chinois de télédétection quantitative en agronomie et environnement. Bilan et perspectives de collaboration. Rapport de mission (26 au 30 mars 2000)

Ce rapport présente les principaux résultats d'un Symposium en Télédétection entre des équipes de chercheurs de l'INRA, du CIRAD, de l'Université de Lille et leurs homologues chinois de l'Institute of Remote Sensing Applications (IRSA) of Chinese Academy of Sciences (CAS), et du National Satellite Meteorological Center (NSMC). Les perspectives d'un programme de collaboration sont présentées avec deux axes majeurs correspondant à deux niveaux d'approche, régional et local en agriculture de précision. (Résumé d'auteur

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production