89. Why are yield sensors seldom used by farmers – a French case study

Lachia, Nina; Pichon, L.; Marcq, P.; Taylor, Jeremy E.; Tisseyre, Bruno

doi:10.3920/978-90-8686-916-9_89

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…Schimmelpfennig [7] reported that yield monitors were used on roughly 70% of the United States corn and soybean acres in 2010-2012. European studies indicate lower levels of adoption [8], and there is evidence that most yield monitors are used to visualize the realtime yield during harvest and not as data providers for yield maps [7,8]. Lachia et al [8] reported that the use of yield maps is too complex for the majority of farmers.…”

Section: Introductionmentioning

confidence: 99%

Site-Specific Seed Yield Prediction of Red Fescue (Festuca rubra L.) Based on Drone Imaging and Local Regression Models

2023

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Yield maps give farmers information about growth conditions and can be a tool for site-specific crop management. Combine harvesters may provide farmers with detailed yield maps if there is a constant flow of a certain amount of biomass through the yield sensor. This is unachievable for grass seeds because the weight of the intake is generally too small to record the variation. Therefore, there is a need to find another way to make grass seed yield maps. We studied seed yield variation in two red fescue (Festuca rubra) fields with variation in management and soil fertility, respectively. We estimated five vegetation indices (VI) based on RGB images taken from a drone to describe yield variation, and trained prediction models based on relatively few harvested plots. Only results from the VI showing the strongest correlation between the index and the yield are presented (Normalized Excess Green Index (ExG) and Normalized Green/Red Difference Index (NGRDI)). The study indicates that it is possible to predict the yield variation in a grass field based on relatively few harvested plots, provided the plots represent contrasting yield levels. The prediction errors in yield (RMSE) ranged from 171 kg ha−1 to 231 kg ha−1, with no clear influence of the size of the training data set. Using random selection of plots instead of selecting plots representing contrasting yield levels resulted in slightly better predictions when evaluated on an average of ten random selections. However, using random selection of plots came with a risk of poor predictions due to the occasional lack of correlation between yield and VI. The exact timing of unmanned aerial vehicles (UAVs) image capture showed to be unimportant in the weeks before harvest.

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Section: Introductionmentioning

confidence: 99%

Site-Specific Seed Yield Prediction of Red Fescue (Festuca rubra L.) Based on Drone Imaging and Local Regression Models

2023

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“…Tensiometers and capacitance sensors that provide information on soil water status are already available [9] but are rarely used in farming systems, particularly at the farm scale, and even more rarely in the Global South. The different barriers to the adoption of precision farming technologies are linked to the user's socio-technical environment, but mostly to (i) economic rationales or (ii) the complexity of using these technologies, including the calibration procedures [10,11].…”

Section: Introductionmentioning

confidence: 99%