Measuring the Impact of Agronomic Practices on Production Quality
It is currently difficult to measure the impact that agronomic practices have on the nutritional quality of foods. Field measurement tools are beginning to emerge and will help support decision-making in agroecology.
Current state
Numerous studies have pointed to a slow degradation of the nutritional quality of fruits and vegetables during the second half of the 20th century.
A study was conducted in the United States to examine the evolution of the contents of 13 nutrients (proteins, lipids, carbohydrates, iron, thiamine, riboflavin, niacin, and ascorbic acid…) in fruits and vegetables between 1950 and 1993 using the American nutritional composition database from the Department of Agriculture USDA[2]. The verdict is that a significant decline was identified in the 43 foods studied for : proteins (-6%), calcium (-16%), phosphorus (-9%), and iron (-15%), riboflavin vitamin B2 (-38%) for median values while there was no significant change for other nutrients.
Why measure the impact of agronomic practices on production quality?
We lack references on the link between soil health and the nutritional quality of foods[3].
Yet, it is essential to determine the causal mechanisms between management systems and soil health as well as between nutrition and the nutritional quality of crops, all within a framework of improving human health.
To answer all these questions, future research studies will need to include :
- Defined and consistently applied management practices to promote soil health.
- Methodologically relevant and consistent measurements of soil health and general condition (physical condition, mineral and nutrient concentration, etc.).
- Measurements of crop yield and nutrient elements of crops (concentrations of mineral nutrients, plant secondary compounds, proteins, etc.).
- Minerals and proteins composing the plant, relevant for human health.
- Methodologically consistent indicators on human health related to nutrition.
Why don’t we do this currently?
Today, no routine measurement of the nutritional qualities of the foods we produce is performed. Consequently, we neither know the nutritional qualities of the products nor the relationship they have with agronomic practices.
The cost of measurements is also a blocking factor. The majority of analyses are carried out by laboratory tests. They are therefore expensive and occasional. Consequently, in many cases these measurements are not performed and do not allow a global and daily view of the state of soils and crops.
Solutions exist
Senseen has developed a scanner, the Nutriscope, which is a miniature device combining a UV-Visible-Near Infrared spectrometer (UV-VIS-NIR) with artificial intelligence (AI) to help decision-making in agroecology : it is a simple and low-cost measurement tool (€900).
This scanner allows daily measurements of the nutritional qualities of foods among other things. Through the predictions it provides, it is possible to understand and improve production techniques with the aim of improving the system and thus placing production in the complete "farm to fork" chain as well as in a logic of one health for humans, animals, and nature.
Currently, predictive measurements are possible for the leaves of vines, wheat, corn, rapeseed and tomato. They constitute a decision support tool aimed at improving the state of crops and soils.
How the Nutriscope works
The scanner emits near-infrared light which vibrates the bonds between atoms. The absorbance curve of the light measured in return gives "a vision" of the matter. This information allows prediction by Artificial Intelligence (AI) of the requested values : Redox Potential (Eh), pH and conductivity (EC). These measurements allow management of agroecological systems.
The Senseen scanner works with a mobile application and sends scanned data to the cloud.
Cette technique s'applique aux cultures suivantes
La technique permet de favoriser la présence des auxiliaires et bioagresseurs suivants
La technique limite la présence des auxiliaires et bioagresseurs suivants
La technique est complémentaire des techniques suivantes
Cette technique utilise le matériel suivants
Cette technique fait référence aux outils d'aide à la décision suivants
- ↑ https://www.senseen.io/_files/ugd/5ba8a7_751320a75e244594a94e2a1aa5cd8dc1.pdf
- ↑ Donald R. Davis et al. Changes in USDA food composition data for 43 garden crops, 1950 to 1999, 2004
- ↑ Bourne D. et al, Exploring the Relationship Between Soil Health and Food Nutritional Quality: A Summary of Research Literature, Soil Health Institute, 2022.
