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The importance of external data for agriculture

Over the last few years, the agricultural sector has been subjected to a fast and needed evolution. The intensive agricultural model of the 20th century, which searches high efficiency while consuming pesticides, does not respond anymore to current challenges (social, economic, ecological, sustainability health, quality…). We can take the example of the usage of pesticides. Once they are used, they remain in the soil and they are transmitted to the ground water and to the atmosphere, also polluting the whole ecosystem. Moreover, 83% of NO2 emissions in France, come from nitrogenous fertilisation of crops. These challenges, generate the need for adaptation and transformation of the agricultural sector to more sustainable and viable schemes. For instance, the development of sustainable agriculture has the objective of answering the needs of the population worldwide, preserving the health of the farmers as well as the consumers, resources, and the ecosystem [1].

In addition, climate change strongly perturbs the harvest: blooming occurs earlier every year, exposing the early fruits to late frosts, causing delay on the growth or even results in the total destruction of crops. These events help the development of certain diseases, pest proliferation, etc. In many cases, the harvest dates come earlier, which can cause crop deterioration and it can be difficult to predict by the farmers [2].

Air quality has a significant role in agricultural productions. The main pollutants are the particulate matter (PM2.5 and PM10), sulphur dioxide (SO2), nitrogen dioxide (NO2) and particularly ozone (O3) [3]. The NO2 is a big disruptor of biochemical mechanisms, and it destroys chlorophyll. As for O3, its absorption by the leaves’ stomata can cause the reduction of the photosynthesis, an increase in ageing at cellular level, making the plant more sensitive to maladies and finally it can reduce their growing capacity as well as its reproduction. Scientific studies have shown that the increase of ozone results in the loss of crop yield of wheat by 13% in Europe, and for vineyards this lost can reach 27% [4][5]. Hence, the effects of air quality are not only environmental, but also economic and social.


Thus, since the 2000s, farmers are increasingly open to practices of precision agriculture, with the aim of better manage their plots. In particular, precision agriculture allows them to improve their productivity: harvesting more while consuming less energy and inputs. This optimisation corresponds to an agroecological, economic and environmental point of view.



The transformation of agricultural practices leads to the usage of external data types, which are complementary to in situ measurements by the farmers:

● Data from Multispectral and hyper-spectral cameras: in situ, drones, satellites. For instance, satellites can provide information at extremely high temporal and spatial resolution, which is key to improve crop management. Processing the data obtained from these cameras allows as to obtain several vegetation indices, such as Normalised Difference Vegetation Index (NDVI). These indices help us monitor crops, to know the state of growth, and also it gives us an idea of their health status. Moreover, the high spatial resolution from satellites (around 10 m) or drones (< 1 m), provides the farmers with an entire daily view of their crops. This monitoring allows the farmers to intervene only when needed.

● Meteorological data, derived from metrological models based on satellite data or in situ measurements (fixed stations). These data, combined with vegetation indices, can help the earlier detection of a disease or water-stress, which in the end will help reducing the economic cost and improve farming efficiency. Moreover, these data can also help preparing the crops for extreme events.

● Air quality data: these data are also provided from satellites and fixed stations. Moreover, models can derive important information on the behaviour of pollutants locally and in the wider area of crops location.


Thanks to the whole set of data described above and to the indices to help the decision making, farmers can have a better organisation and management of their time. This is because they have a global view of their plot without having to manually analyse and explore each plant. They can also prevent diseases or detect them earlier, which allows them to act fast and where needed, reducing their loses. To sum up, farmers save time, they reduce their economic costs, and more importantly they can help reducing soil, ground water and atmospheric pollution.

It is worth mentioning that because of the volume, quality, and consistency that the above-mentioned external data have, they are projected to partake a significant role, and boost the new era of analytics that is based on big-data, ML algorithms, and AI. Prospecting to high quality and value information, predictive models, and high increase in farming efficiency [6] [7].


References:

[1] https://www.actu-environnement.com/ae/news/enjeux_agriculture_demain_7881.php4

[2] https://reseauactionclimat.org/quels-impacts-du-changement-climatique-sur-lagriculture/

[3] http://www.sdn11.fr/wp-content/uploads/2017/10/A-Carbonneau-AREVAt-VF.pdf

[4] Ascenso, A.; Gama, C.; Blanco-Ward, D.; Monteiro, A.; Silveira, C.; Viceto, C.; Rodrigues, V.; Rocha, A.; Borrego, C.; Lopes, M.; et al. Assessing Douro Vineyards Exposure to Tropospheric Ozone. Atmosphere 2021, 12, 200. https://doi.org/10.3390/atmos12020200

[5] Feng, Z.; Kobayashi, K.; Ainsworth, E.A. Impact of elevated ozone concentration on growth, physiology, and yield of wheat (Triticum aestivum L.): A meta-analysis. Glob. Chang. Biol. 2008, 14, 2696–2708

[6] Jung, J., Maeda, M., Chang, A., Bhandari, M., Ashapure, A., & Landivar-Bowles, J. The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems. Current Opinion in Biotechnology, 2021, 70, 15-22.

[7] S. A. Bhat and N. -F. Huang, "Big Data and AI Revolution in Precision Agriculture: Survey and Challenges," in IEEE Access, vol. 9, pp. 110209-110222, 2021, doi:10.1109/ACCESS.2021.3102227. https://ieeexplore.ieee.org/abstract/document/9505674


 

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