Spatial Variability of Kiwifruit Quality in Orchards and Its Implications for Sampling and Mapping

Taylor, James A.; Praat, John-Paul; Bollen, A. F.

doi:10.21273/hortsci.42.2.246

Cited by 22 publications

(9 citation statements)

References 9 publications

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“…Yield is classically used to describe the production in terms of quantity of any crop in multiple service assessment (Dale and Polasky 2007; Kragt and Robertson 2014;Schipanski et al 2014;Syswerda and Robertson 2014). The five quality criteria cited below can be assessed at the plot scale using mean values at harvest, given that the spatial variability of fruit quality is large (Génard and Bruchou 1992;Taylor et al 2007). These criteria cover the concerns of various stakeholders, from fruit growers to consumers.…”

Section: Water and Nitrogen Effectsmentioning

confidence: 99%

Ecosystem services in orchards. A review

Demestihas

Plénet

Génard

et al. 2017

Agron. Sustain. Dev.

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Arboriculture must maintain acceptable fruit production levels while preserving natural resources. This duality can be analyzed with the concept of ecosystem service. We reviewed the literature on orchards to explain how ecological functions modified by agricultural practices provide six ecosystem services-fruit production, climate regulation, soil nitrogen availability, water regulation, pest and disease control, and pollination-and which indicators could describe them. The major points are, first, that orchards have a high potential of multiple services. They can sequester from 2.4 to 12.5 t C/ha/year. Their perennial character and multi-strata habitat, as well as the opportunity of creating diversified hedgerows and cover crops in alleys, may contribute to a high level of biodiversity and related services. Second, every service depends on many functions. Fruit yield, which could reach up to 140 t/ha in apple orchards, is increased by light interception, carbon allocation, and nitrogen and water uptake. Third, agricultural practices in orchards have a strong impact on ecosystem functions and, consequently, on ecosystem services. Overfertilization enhances nitrogen leaching, which reduces soil nitrogen availability for the plant and deteriorates the quality of drained water. Groundcover increases humification and reduces denitrification and runoff, thus enhancing soil nitrogen availability and water regulation. It also enhances biotic interactions responsible for pest control and pollination. Pruning may increase fruit quality trough a better carbon allocation but decreases pest control by fostering the dynamics of aphids. To study multiple ecosystem services in orchards, we suggest using models capable of simulating service profiles and their variation according to management scenarios. We then refer to the available literature to show that conflicts between provisioning and regulating services can be mitigated by agricultural practices. Improved knowledge of soil processes and carbon balance as well as new models that address multiple services are necessary to foster research on ecosystem service relationships in orchards.

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Section: Water and Nitrogen Effectsmentioning

confidence: 99%

Ecosystem services in orchards. A review

Demestihas

Plénet

Génard

et al. 2017

Agron. Sustain. Dev.

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“…Some researchers use metrics together to better assess spatial variability and optimize decision-making: Taylor et al (2007) using the NE and the MCD; Souza et al (2008) using the NE and the integral scale J2; Oldoni and Bassoi (2016) using SPD and SDI; Amaral and Della Justina (2019) and Guedes et al (2020) using the NE and SDI. These findings show the need for further studies to propose and evaluate the performance of the indexes, mainly for the SDM that is being proposed.…”

Section: Resultsmentioning

confidence: 99%

Geostatistical-based index for spatial variability in soil properties

Neto¹,

Seidel²,

Oliveira³

2020

Revista Brasileira De Ciência Do Solo

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The assessment of spatial variability of environmental variables such as soil properties is important for site-specific management. A geostatistical index that allows quantifying and characterizing the structure of spatial variability is fundamental in this context. Thus, this study aimed to develop a new spatial dependency index, called the Spatial Dependence Measure (SDM) for the spherical, exponential, Gaussian, cubic, pentaspherical, and wave semivariogram models; and comparing it with some of the indexes available in the literature. The SDM is also dimensionless, in the same way as the Spatial Dependence Index (SDI), also considering more parameters of the semivariogram, when compared to the Spatial Dependence Degree (SPD) and Relative Nugget Effect (NE) indexes. In a simulation data study, it is observed that the SDI and SDM indexes showed an advantage over the SPD (or NE). To exemplify the application of the SDM in the proposal for the classification of soil properties, we used estimates of geostatistical parameters presented in the two studies. The results indicate that the SDM can be a measure that, analyzed together with the SDI, can help to improve the description of the spatial variability structure. Thus, this study expands the number of geostatisitcal-based measures and increases the power of decision on the description of the degree of spatial variability of agricultural and soil attributes.

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“…In our system we obtained satisfactory estimates of the population mean through a random selection of vines within defined biomass zones for which a simple, field‐oriented procedure has been proposed (Bramley n.d., Ledesma and Carpena , Parker et al ). Other systems have been proposed, but not assessed by us, include ‘structured‐random’ (Wulfsohn et al , Vega et al ), ‘grid’ (Taylor et al , Baluja et al ), ‘staggered linear‐transect’ (Rigamonti et al ), ‘transects, normal to landscape trends’ (Austin and Heyligers , Burbidge ) and ‘grid within spatial variograms’ (Fortin et al , McBratney and Pringle ). The simpler the procedure for selecting units, vines, within a plot, vineyard, the lower the cost and the higher the confidence, regardless of skill level.…”

Section: Discussionmentioning

confidence: 99%

Hierarchical modelling partitions variation in vineyard fruit maturity for optimal sampling

Wisdom

Stuckey

Considine

2020

Australian Journal of Grape and Wine Research

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Background and Aims Sampling is problematic in perennial tree and vine crops because organ numbers are vast, budgets are low and maturation of fruit is asynchronous within and between individuals. Typically, the precision and the repeatability of sampling are low and methodologies used in the field vary widely within the industry. We define a combined statistical and cost model for fruit maturity sampling that could be applied widely and would enable precision and cost to be pre‐defined. Methods and Results A two‐stage statistical model, incorporating a ‘cost’ element was designed. The model was evaluated using an intensive set of data acquired from Cabernet Sauvignon vines. This was decomposed into between‐ and within‐vine sources of variance for sugar measured as TSS, anthocyanin and phenolic substances. Conclusions Within a management unit, the between‐vine variation may equal or exceed the within‐vine variation. The two‐stage protocol, for unit cost, gave a more precise estimate of TSS than other protocols. On‐site costs were the main element in apportioning vines per plot and bunches per vine. The variance for anthocyanin and phenolic substances was greater than that of TSS and thus precision was lower for these at a given sample size. Significance of the Study A simple, two‐stage sampling protocol resolves uncertainties inherent in existing protocols. It may be applied widely to grapevine and other tree crops.

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Spatial Variability of Kiwifruit Quality in Orchards and Its Implications for Sampling and Mapping

Cited by 22 publications

References 9 publications

Ecosystem services in orchards. A review

Ecosystem services in orchards. A review

Geostatistical-based index for spatial variability in soil properties

Hierarchical modelling partitions variation in vineyard fruit maturity for optimal sampling

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