Comparing efficiency of different sampling schemes to estimate yield and quality parameters in fruit orchards

Arnò, Jaume; Martínez‐Casasnovas, José A.; Uribeetxebarria, Asier; Escolà, Alexandre; Rosell-Polo, Joan R.

doi:10.1017/s2040470017000978

Cited by 9 publications

(9 citation statements)

References 9 publications

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“…One important reason could be the small sized orchards usually have there. Nevertheless, and as pointed out by Käthner and Zude-Sasse (2015) and Arnó et al (2017), even in small orchards there may be differences in soil properties affecting tree growth and fruit quality.…”

Section: Introductionmentioning

confidence: 97%

Spatial variability in orchards after land transformation: Consequences for precision agriculture practices

Uribeetxebarria

Daniele

Escolà

et al. 2018

Science of The Total Environment

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The change from traditional to a more mechanized and technical agriculture has involved, in many cases, land transformations. This has supposed alteration of landforms and soils, with significant consequences. The effects of induced soil variability and the subsequent implications in site-specific crop management have not been sufficiently studied. The present work investigated the application of a resistivity soil sensor (Veris 3100), to map the apparent electrical conductivity (ECa), and detailed multispectral airborne images to analyse soil and crop spatial variability to assist in site-specific orchard management. The study was carried out in a peach orchard (Prunus persica (L.) Stokes), in an area transformed in the 1980 decade to change from rainfed arable crops to irrigated orchards. A total of 40 soil samples at two depths (0-30cm and 30-60cm) were analysed and compared to ECa and the normalised difference vegetation index (NDVI). Two types of statistical analysis were performed between ECa or NDVI classes with soil properties: a linear correlation analysis and multivariate analysis of variance (MANOVA). The results showed that the land transformation altered the spatial distribution and continuity of soil properties. Although a relationship between ECa and peach tree vigour could be expected, it was not found, even in the case of trees planted in soils with salts content above the tolerance threshold. Two types of management zones were proposed: a) zones delineated according to ECa classes to leach salts in the high ECa zones, and b) zones delineated according to NDVI classes to regulate tree vigour and yield. These strategies respond to the alteration of the original soil functions due to the land transformation carried out in previous years.

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Section: Introductionmentioning

confidence: 97%

Spatial variability in orchards after land transformation: Consequences for precision agriculture practices

Uribeetxebarria

Daniele

Escolà

et al. 2018

Science of The Total Environment

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“…Field yield estimation is often carried out by sampling. Observations of yield components are then made on a limited number of vines (Carrillo et al, 2016;Arnó et al, 2017;Wolpert and Vilas, 1992) and the mean value is generally used to provide an estimation of the field yield. To have a relevant estimation of the final yield, sampling is often carried out a few days before harvest, at a critical period in terms of workload.…”

Section: Introductionmentioning

confidence: 99%

Is the optimal strategy to decide on sampling route always the same from field to field using the same sampling method to estimate yield?

et al. 2021

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Aim: This short communication aims at providing insights to verify whether common yield sampling protocols (i.e., one round trip within the fields over two representative rows) are optimal whatever the considered fields. In addition, it aims to show how factors like the spatial organisation of the within-field yield variability, the length of the rows, the erratic variance, etc. may affect the optimal sampling route and the error of the yield estimation.Methods and Material: A new algorithm based on constraint programming and stochastic approaches was used to provide optimal sampling routes for vineyards. This algorithm guarantees the representativeness of the measurement sites and a minimization of the walking distance. Practical constraints (trellised structure, starting point, etc.) are considered by the algorithm to optimise the walking distance and the resulting sampling route. The algorithm has been applied to 60 simulated vineyards with known yield variability. Characteristics like yield spatial structure, row length and proportion of erratic variance were controlled during the simulation process and were used to study how they affect the optimal sampling route derived from the algorithm.Results: The row length as well as the spatial organization of the within-field yield variability are the main factors that determine the optimality of a sampling route. Spatial organisation of the yield happens to have a strong incidence; fields with small yield patterns (Range of the semi-variogram = 25 m) showed a yield estimation error of less than 2 % with an optimal sampling route of three minutes with 7 sampling sites, whereas it takes more than 5 minutes (with 9 sampling sites) to achieve the same estimation error for fields with larger spatial patterns (range > 50 m). Results also highlight the relevance of original sampling routes which intend to sample only the beginnings of rows or mixed approaches based on a round trip in two inter-rows and complementary samples on the beginnings of one or more rows.Conclusions: This study shows that an optimal sampling route strongly depends on the field characteristics. The optimal sampling route should therefore be tailored to each field. This approach is a first step which shows how this methodology could be used to identify other factors of influence. It could also apply to real fields to optimise other logistic operations in viticulture.Significance and Impact of the Study: This short communication demonstrates the necessity to tailor sampling strategy to characteristics of each field to provide both an optimised sampling route (minimum walking distance with minimum samples) and the best possible estimate. It also proposes an original approach based on field simulations and an optimal sampling route generation algorithm. This approach makes it possible to produce new insights (and also to validate empirical practices) that can help the wine industry to better manage the logistics at harvest. This paper also gives considerations when it comes to the choice of a sampling route for a given field.

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“…Recent works (Carrillo et al 2016;Uribeetxebarria et al 2019;Arnó et al 2017) have shown the interest of integrating auxiliary data to improve sampling strategies and yield estimation for perennial crops. Among possible auxiliary information, vegetation index derived from multispectral airborne images is of great interest since they permit to characterise the spatial variability of several fields; in one acquisition, with a high spatial resolution (< 1 m) and at an optimal date.…”

Section: Introductionmentioning

confidence: 99%

Combining target sampling with within field route-optimization to optimise on field yield estimation in viticulture

2020

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Comparing efficiency of different sampling schemes to estimate yield and quality parameters in fruit orchards

Cited by 9 publications

References 9 publications

Spatial variability in orchards after land transformation: Consequences for precision agriculture practices

Spatial variability in orchards after land transformation: Consequences for precision agriculture practices

Is the optimal strategy to decide on sampling route always the same from field to field using the same sampling method to estimate yield?

Combining target sampling with within field route-optimization to optimise on field yield estimation in viticulture

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