A comparison of global agricultural monitoring systems and current gaps


3. Comparison of the main global and regional scale agricultural monitoring systems

Data and model inputs Data and model inputs Data and model inputs GIEWS Seasonal Monitor
Meteorological data source used Precipitation Precipitation RS RS
Temperature Temperature Temperature RS
Evapotranspiration Evapotranspiration Evapotranspiration
Solar radiation Solar radiation Solar radiation

Feb 15 2022


What is monitoring system in agriculture?

Crop monitoring systems Applied in crop monitoring, smart sensing technology collects metrics about the state of the crops (temperature, humidity, health indicators) and enables farmers take timely measures should anything go wrong.

Why are products used in agriculture monitored?

By monitoring, collecting, analyzing, and responding to data, products can get to consumers much more efficiently and cost-effectively.

What is smart agriculture monitoring system?

Smart agriculture monitoring system or simply smart farming is an emerging technology concept where data from several agricultural fields ranging from small to large scale and its surrounding are collected using smart electronic sensors.

What is smart agriculture system?

Smart farming is a management concept focused on providing the agricultural industry with the infrastructure to leverage advanced technology – including big data, the cloud and the internet of things (IoT) – for tracking, monitoring, automating and analyzing operations.

Why is crop area mapping important?

Crop area mapping is important for tracking agricultural production and supporting food security. Spaceborne approaches using synthetic aperture radar (SAR) now allow for mapping crop area at moderate spatial and temporal resolutions. Multi-frequency SAR data is highly useful for crop monitoring because backscatter response from vegetation canopies is wavelength dependent. This study evaluates the utility of C-band Sentinel-1B (Sentinel-1) and L-band ALOS-2 (PALSAR) data, collected during the 2019 growing season, for generating accurate active crop extent (crop vs. non-crop) classifications over an agricultural region in western Canada. Evaluations were performed against the Agriculture and Agri-Food Canada satellite-based Annual Cropland Inventory (ACI), an open data product that maps land cover across the extent of Canada’s agricultural land. Classifications were performed using the temporal coefficient of variation (CV) approach, where an optimal crop/non-crop delineating CV threshold (CVthr) is selected according to Youden’s J-statistic. Results show that crop area mapping agreed better with the ACI when using Sentinel-1 data (83.5%) compared to PALSAR (73.2%). Analysis of performance by crop reveals that PALSAR’s poorer performance can be attributed to soybean, urban, grassland, and pasture ACI classes. This study also compared CV values to in situ wet biomass data for canola and soybeans, showing that crops with lower biomass (soybean) had correspondingly lower CV values.

What is global and regional scale?

Global and regional scale agricultural monitoring systems aim to provide up-to-date information regarding food production to different actors and decision makers in support of global and national food security. To help reduce price volatility of the kind experienced between 2007 and 2011, a global system of agricultural monitoring systems is needed to ensure the coordinated flow of information in a timely manner for early warning purposes. A number of systems now exist that fill this role. This paper provides an overview of the eight main global and regional scale agricultural monitoring systems currently in operation and compares them based on the input data and models used, the outputs produced and other characteristics such as the role of the analyst, their interaction with other systems and the geographical scale at which they operate. Despite improvements in access to high resolution satellite imagery over the last decade and the use of numerous remote-sensing based products by the different systems, there are still fundamental gaps. Based on a questionnaire, discussions with the system experts and the literature, we present the main gaps in the data and in the methods. Finally, we propose some recommendations for addressing these gaps through ongoing improvements in remote sensing, harnessing new and innovative data streams and the continued sharing of more and more data.

What is remote monitoring technology?

Remote monitoring technology is a mandatory component of the crop management concept. The available solutions allow determining the presence of plant stress but not identifying its causes. A particular danger is presented by stresses of a technological nature, and chemical poisoning of plants due to the aftereffect of herbicides, compaction of the subsoil, and the like. Plant stresses of a technological nature lead to a decrease in plant immunity and, accordingly, special measures are needed to restore their productivity. Laboratory methods for analyzing stress, in particular, chemical poisoning of plants, are technologically complex and expensive, which prevents their widespread use. Remote sensing technologies are capable of identifying areas with manifestations of technological stresses since such stresses have characteristic features. As our studies have shown, a promising method for identifying plant areas with signs of technological stress is the method of leaf diagnostics. For such areas, it is necessary to carry out monitoring with the highest image resolution, it is assumed in the UAV flight program. Taking into account the above, the aim of the work was to develop an algorithm and software for its implementation of UAV flight planning for the identification of plant stresses of a technological nature. The software was developed in the cross-platform programming language Python, and it allowed processing maps of the distribution of vegetation indices (for experimental studies, maps were used that were created using the Slantrange spectral sensor system). The use of the algorithm, implemented in the cross-platform programming language Python, made it possible to identify the paths of movement of technological equipment, the contours of areas with close values of the vegetation index, and the main features of areas with plant stress of a technological nature. The accuracy of identifying areas with technological stresses has been confirmed by ground surveys in production fields.

What is synthetic aperture radar?

Abstract Synthetic Aperture Radar (SAR) data are well‐suited for change detection over agricultural fields, owing to high spatiotemporal resolution and sensitivity to soil and vegetation. The goal of this work is to evaluate the science algorithm for the NASA ISRO SAR (NISAR) Cropland Area product using data collected by NASA’s airborne Uninhabited Aerial Vehicle SAR (UAVSAR) platform and the simulated NISAR data derived from it. This study uses mode 129, which is to be used for global‐scale mapping. The mode consists of an upper (129A) and lower band (129B), respectively having bandwidths of 20 and 5 MHz. This work uses 129A data because it has a four times finer range resolution compared to 129B. The NISAR algorithm uses the coefficient of variation (CV) to perform crop/noncrop classification at 100 m. We evaluate classifications using three accuracy metrics (overall accuracy, J‐statistic, Cohen’s Kappa) and spatial resolutions (10, 30, and 100 m) for crop/noncrop delineating CV thresholds (CVthr) ranging from 0 to 1 in 0.01 increments. All but the 10 m 129A product exceeded NISAR’s mission accuracy requirement of 80%. The UAVSAR 10 m data performed best, achieving maximum overall accuracy, J‐statistic, and Kappa values of 85%, 0.62, and 0.60. The same metrics for the 129A product respectively are: 77%, 0.40, 0.36 at 10 m; 81%, 0.55, 0.49 at 30 m; 80%, 0.58, 0.50 at 100 m. We found that using a literature recommended CVthr value of 0.5 yielded suboptimal accuracy (65%) at this site and that optimal CVthr values monotonically decreased with decreasing spatial resolution.

What is performance farming?

The performance farming is a new bet for the EU in the context of the present’s major climate and economic challenges. This paper aims at defining a model of agricultural competitiveness for the EU and its application for the evaluation of regional agricultural performance, in relation to the global competitiveness index, using the theory of catastrophes. The objectives of the analysis are: to evaluate the current growth theories in agriculture, to conceptualize a new model of agricultural performance improvement (RAP), to test the model and to obtain the relevant working tools after its application. The used methods are: the study of the general models of growth in agriculture; the dynamic analysis of the Eurostat data on agricultural performance and Member States’ data published in the National Accounts System; the conceptualization of the RAP (Regional Agricultural Performance) growth model; the statistical testing of the model, its connectivity with global competitiveness indexes and climate change; the hypotheses’ building in order to eliminate the climate transformations influences according to the catastrophe theorem’s results; and providing a viable and sustainable tool for the national strategy for agriculture’s forecasting changes to the Member States. The novelty element brought by the present proposed model is that of quantification in a broader and special way of the impact of environmental changes on the performing agricultural output in terms of National Accounting System.

What is forecasting crop yield?

Forecasting crop yields, or providing an expectation of ex-ante harvest amounts, is highly relevant to the whole agricultural production chain. Farmers can adapt their management, traders or insurers their pricing schemes, suppliers their stocks, logistic companies their routes, national authorities their food balance sheets to guide import or export and, finally, international aid organizations can mobilize reliefs. Evidence has grown in the literature that such forecasts with a meaningful lead time are possible on various geographic scales and for a broad range of crops. Here, we present a systematic review of the methods applied in end-of-season yield forecasting and three frequently used data sources: weather data, satellite data and crop masks. Our literature database comprises 362 studies (2004–2019) which were evaluated regarding methods, crops, regions, data sources, lead time and performance. Moreover, we present 24 sources of real-time and predictive weather data, 21 sources of remote sensing data and 16 crop masks. Yield forecasting in our literature sample has been performed for 44 crops in 71 countries, also including many non-staple crops, but with an apparent bias in regions and crops. Forecasting performance depends on various factors, including crop, region, method, lead time to harvest and input diversity. Our systematic review supports a broader application of locally successful approaches at larger scales by providing a comprehensive, accessible compendium of necessary information for yield forecasting. We discuss improvement potentials with respect to methodological approaches and available data sources. We additionally suggest standardization procedures for future forecasting studies and encourage studying additional crops and geographic regions. Implications of forecasts for different target groups on different scales and the adaptation towards climate change are also discussed.

Why is forecasting crop yields important?

Forecasting crop yields is important for food security, in particular to predict where crop production is likely to drop. Climate records and remotely-sensed data have become instrumental sources of data for crop yield forecasting systems. Similarly, machine learning methods are increasingly used to process big Earth observation data. However, access to data necessary to train such algorithms is often limited in food-insecure countries. Here, we evaluate the performance of machine learning algorithms and small data to forecast yield on a monthly basis between the start and the end of the growing season. To do so, we developed a robust and automated machine-learning pipeline which selects the best features and model for prediction. Taking Algeria as case study, we predicted national yields for barley, soft wheat and durum wheat with an accuracy of 0.16-0.2 t/ha (13-14 % of mean yield) within the season. The best machine-learning models always outperformed simple benchmark models. This was confirmed in low-yielding years, which is particularly relevant for early warning. Nonetheless, the differences in accuracy between machine learning and benchmark models were not always of practical significance. Besides, the benchmark models outperformed up to 60% of the machine learning models that were tested, which stresses the importance of proper model calibration and selection. For crop yield forecasting, like for many application domains, machine learning has delivered significant improvement in predictive power. Nonetheless, superiority over simple benchmarks is often fully achieved after extensive calibration, especially when dealing with small data.


Global and regional scale agricultural monitoring systems aim to provide up-to-date information regarding food production to different actors and decision makers in support of global and national food security.


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