Monitoring fruit daily growth indicates the onset of mild drought stress in apple

Boini, Alexandra; Manfrini, Luigi; Bortolotti, G.; Grappadelli, Luca Corelli; Morandi, Brunella

doi:10.1016/j.scienta.2019.05.047

Cited by 34 publications

(16 citation statements)

References 46 publications

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“…CREA has already carried out experiences on regulating the fruiting load monitoring both flowers and fruits [20] if it should be particularly uneven in the detected canopy for precision chose of intervention orchards area. In other orchard studies [21], remote sensing solutions exist for monitoring fruit growing as a function of specific fruit load. There are several and available alternatives of remote and proximity systems to detect canopy uniformity and distribution [22]; however, the possibility of having them installed on the machine that ordinarily performs the work allows for the constant updating of the situation as a result of performing periodic passages inside the olive grove.…”

Section: Data Analisysmentioning

confidence: 99%

Canopy Index Evaluation for Precision Management in an Intensive Olive Orchard

et al. 2021

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The evaluation of the canopy in orchard cultivation is a key aspect for the main cultivation techniques, such as pruning, thinning, harvesting, production and improved fruit quality. The possibility of having a periodic screening of the state of development of the vegetation can be of practical support to growers. Research on the application of precision agriculture has provided tools for reading and interpreting crops, and the resulting information is potentially useful. Many of the systems under study provide after monitoring information processing systems that reduce the timeliness of intervention. Especially in intensive systems such as olive groves, knowing the precise intervention points is often essential. In the present work, a multi-parameter instrument was used for field monitoring on the agricultural tractor to analyse the canopy. The system allows measuring various indicators such as height and density of the canopy and the temperature and humidity of the ambient air and at the leaf level. The first evaluation of the data made it possible to identify areas with greater vegetative concentration and greater or lesser development. The system made it possible to identify with good approximation the homogeneous areas, based on the Canopy Index (CI) evaluation to be subjected to subsequent and specific management efforts, dividing them into low, ordinary, and high vegetative growth. The results highlight the possibility of directly combining operators able to intervene with the same passage, selecting based on differences in growth, typical varietal specificities, and areas of deficient development or that are affected by plant diseases, confirming the objective of defining the areas of the orchard that require different management and workload techniques.

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Section: Data Analisysmentioning

confidence: 99%

Canopy Index Evaluation for Precision Management in an Intensive Olive Orchard

et al. 2021

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“…Indeed, the sensitivity of table grape crops with regard to water deficit has been confirmed in the different works described above, in which irrigation was scheduled based on ET C ; however, Boini et al [38] mentioned that it would be highly desirable to schedule irrigation based on the actual plant water status, as it changes depending on several tree-related factors such as rootstock, source/sink ratio, phenological stage etc. [39,40].…”

Section: Discussionmentioning

confidence: 98%

“…Effectively, fruit water accumulation is highly sensitive to the level of water deficit during all the fruit developmental stages [3], as demonstrated via the strong correlations between mean Ψ s values during the season and harvest fruit size [44]. In apple trees, Boini et al [38] proposed fruit growth during the cell expansion stage, when fruit growth rates are constant [45], as a potential indicator of the plant water status, in order to implement this in decision support systems for irrigation scheduling. In this way, they found the onset of drought stress below the threshold of about 1.2 g fruit −1 day −1 , which corresponded to a midday stem water potential below −1 MPa.…”

Section: Discussionmentioning

confidence: 99%

Irrigation Protocols in Different Water Availability Scenarios for ‘Crimson Seedless’ Table Grapes under Mediterranean Semi-Arid Conditions

Temnani

Conesa

Ruiz

et al. 2020

Water

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For three consecutive years (2015–2017), two deficit irrigation (DI) strategies were used in a 12-year old vineyard (cv. ‘Crimson Seedless’) to implement a sustainable irrigation protocol according to the available water for the farmer. Four different irrigation treatments were assessed: (i) Control (CTL), irrigated to satisfy the maximum crop water requirements throughout the entire growing season; two DI treatments irrigated as CTL except during post-veraison, when the vines were irrigated at 50% CTL: (ii) Regulated Deficit Irrigation (RDI); and (iii) Partial Root Drying (PRD), alternating the wet and dry sides of the root zone, and (iv) irrigated according to the criteria followed by the farmer (FARM), and conditioned by the availability of water each season. The DI strategies resulted in a 50% increase in water use efficiency in the first two years and 81% during the third year. Weekly deficit irrigation protocols are proposed, which specify a maximum difference of 0.22 MPa of midday stem water potential with respect to well-watered vines for a range of irrigation water availabilities between 4000 and 7000 m3 ha−1. An applied water prediction model based on the Gaussian regression using day of the year and maximum temperature of the day is also proposed.

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“…Management units (MUs) have been used in site-specific horticultural management (Miranda et al, 2018;Aggelopoulou et al, 2013;Taylor and Whelan, 2011;Paoli et al, 2007). Recent advances in sensor technologies, such as soil sensors, canopy sensors and fruit/yield monitors, can now provide J o u r n a l o f H o r t i c u l t u r a l S c i e n c e many different layers to help derive these management units (Bresilla et al, 2019;Boini et al, 2019;Losciale et al, 2015). In many cases horticulture producers already have some of these data layers but do not have the tools to apply them spatially to crop management (Praat et al, 2001;Taylor et al, 2007a;Manfrini et al, 2009;Zude-Sasse et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Innovative approaches to orchard management: assessing the variability in yield and maturity in a ‘Gala’ apple orchard using a simple management unit modeling approach

Manfrini¹,

Grappadelli²,

Morandi³

et al. 2020

Europ.J.Hortic.Sci

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Stratification of spatial data into management classes is a common way of interpreting and managing spatial agricultural data. High-resolution environmental and crop production information was collected within a 2.2-ha apple orchard (Malus domestica cv. Gala) near Sydney, NSW, Australia. Classifying the block into management units using the environmental data did not help to interpret the observed apple spatial variation in apple production. A backwards modelling approach was subsequently undertaken, effectively analyzing whether the crop production explains environmental variation. The backwards management unit model identified a different spatial patterning that revealed both an environmental and managerial effect on fruit production in different sections of the orchards. The unknown management effect had masked any spatial environmental-crop interactions in the initial (forward) management unit analysis. The combined forward and backward modeling approach significantly assisted the interpretation of the spatial variation in production in the orchard and identified a previously unknown management effect. When using management units to assess spatial relationships, the inclusion of a backwards modeling approach may help industry and growers to better understand and interpret spatial relationships and facilitate orchard management.

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Monitoring fruit daily growth indicates the onset of mild drought stress in apple

Cited by 34 publications

References 46 publications

Canopy Index Evaluation for Precision Management in an Intensive Olive Orchard

Canopy Index Evaluation for Precision Management in an Intensive Olive Orchard

Irrigation Protocols in Different Water Availability Scenarios for ‘Crimson Seedless’ Table Grapes under Mediterranean Semi-Arid Conditions

Innovative approaches to orchard management: assessing the variability in yield and maturity in a ‘Gala’ apple orchard using a simple management unit modeling approach

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