Delineation of Management Zones in Hedgerow Almond Orchards Based on Vegetation Indices from UAV Images Validated by LiDAR-Derived Canopy Parameters

One of the challenges in orchard management, in particular of hedgerow tree plantations, is the delineation of management zones on the bases of high-precision data. Along this line, the present study analyses the applicability of vegetation indices derived from UAV images to estimate the key structural and geometric canopy parameters of an almond orchard. In addition, the classes created on the basis of the vegetation indices were assessed to delineate potential management zones. The structural and geometric orchard parameters (width, height, cross-sectional area and porosity) were characterized by means of a LiDAR sensor, and the vegetation indices were derived from a UAV-acquired multispectral image. Both datasets summarized every 0.5 m along the almond tree rows and were used to interpolate continuous representations of the variables by means of geostatistical analysis. Linear and canonical correlation analyses were carried out to select the best performing vegetation index to estimate the structural and geometric orchard parameters in each cross-section of the tree rows. The results showed that NDVI averaged in each cross-section and normalized by its projected area achieved the highest correlations and served to define potential management zones. These findings expand the possibilities of using multispectral images in orchard management, particularly in hedgerow plantations.

Quantitative Survey of ‘Montmorency’ Tart Cherry Orchard Design in Utah

‘Montmorency’ tart cherry trees (Prunus cerasus L.) are grown commercially in the United States in low-density systems. Commercial tart cherry orchard design has not changed significantly over the past 50 years, but there is some variation from farm to farm in management strategies, including tree spacing, training, and pruning, and the resulting orchard production and turnover. Canopy dimensions and dynamics are important considerations for evaluating and improving orchard management strategies but are not well documented for tart cherry systems. Current orchard design and canopy management strategies were surveyed along a gradient of orchard age across five commercial farming operations in Utah. Trunk cross-sectional area and various canopy dimensions, including spread and volume, were quantified to capture tree size and canopy architecture. The survey indicated a surprising lack of deviation in orchard design in the region over the last several decades with higher variation among blocks within a farm than across farms. As a result, the survey revealed trends in tree growth and canopy structure across the range in orchard ages despite differences in management approaches of the surveyed farms. These trends were useful in illustrating canopy development and space fill. Tree age between 11 and 15 years after planting was determined to represent a transition between establishment and mature growth, where canopies filled available row space and began experiencing senescing canopy structure. Based on the distribution of ages captured in the survey, a significant number of orchards in Utah are at an age range of 11–15 years, perhaps contributing to superior yields per land area reported for the region. The confluence of space-fill and canopy development described in this study highlights a critical period for tart cherry orchard management at the transition of canopy establishment and maturity. These baseline dynamics will provide benchmarks for evaluating strategies for refining and improving orchard management systems for tart cherry in the Intermountain West region.

Estimation of Apple Tree Leaf Chlorophyll Content Based on Machine Learning Methods

Leaf chlorophyll content (LCC) is one of the most important factors affecting photosynthetic capacity and nitrogen status, both of which influence crop harvest. However, the development of rapid and nondestructive methods for leaf chlorophyll estimation is a topic of much interest. Hence, this study explored the use of the machine learning approach to enhance the estimation of leaf chlorophyll from spectral reflectance data. The objective of this study was to evaluate four different approaches for estimating the LCC of apple tree leaves at five growth stages (the 1st, 2nd, 3rd, 4th and 5th growth stages): (1) univariate linear regression (ULR); (2) multivariate linear regression (MLR); (3) support vector regression (SVR); and (4) random forest (RF) regression. Samples were collected from the leaves on the eastern, western, southern and northern sides of apple trees five times (1st, 2nd, 3rd, 4th and 5th growth stages) over three consecutive years (2016–2018), and experiments were conducted in 10–20-year-old apple tree orchards. Correlation analysis results showed that LCC and ST, LCC and vegetation indices (VIs), and LCC and three edge parameters (TEP) had high correlations with the first-order differential spectrum (FODS) (0.86), leaf chlorophyll index (LCI) (0.87), and (SDr − SDb)/ (SDr + SDb) (0.88) at the 3rd, 3rd, and 4th growth stages, respectively. The prediction models of different growth stages were relatively good. The MLR and SVR models in the LCC assessment of different growth stages only reached the highest R2 values of 0.79 and 0.82, and the lowest RMSEs were 2.27 and 2.02, respectively. However, the RF model evaluation was significantly better than above models. The R2 value was greater than 0.94 and RMSE was less than 1.37 at different growth stages. The prediction accuracy of the 1st growth stage (R2 = 0.96, RMSE = 0.95) was best with the RF model. This result could provide a theoretical basis for orchard management. In the future, more models based on machine learning techniques should be developed using the growth information and physiological parameters of orchards that provide technical support for intelligent orchard management.

An assessment of mandarin (Citrus reticulata Blanco.) orchard management practices in Dailekh, Nepal

A study was carried out in Dailekh, Nepal with an objective to assess the mandarin orchard management practices and their level of adoption by mandarin growers of Dullu municipality. All together 60 respondents were selected for the household survey in major citrus-producing wards of Dullu by following the simple random sampling technique. Primary data were collected by administering the pretested questionnaire and; descriptive and inferential statistics were computed using Ms-Excel and SPSS. The result showed that the majority of the respondents were from Brahmin ethnicity (43.3%), middle-age group i.e. 35-49 years (53.3%), and male (70.0%). Furthermore, most of them belonged to medium size family of 5-8 members (60.0%) and were earning most of their income through agriculture (80.0%). The result of the study revealed the variation in the scale of adoption of different management practices. Most of the households had adopted pruning and applied farmyard manure (FYM) while the least of them had adopted mulching and applied chemical fertilizer. Chi-square test revealed that the factors such as age (p=0.042), education level (p=0.007), family size (p=0.029), and training exposure (p=0.001) were significantly associated with the level of adoption of these practices. Incidence of insect-pests and diseases (I=0.88), frequent occurrence of climatic hazards such as hailstorms (I=0.71), and limited irrigation facility (I=0.70) were the major problems of mandarin growing households. It is recommended that improving access to irrigation facilities, use of certified planting material, mulching application and use of appropriate dose of manure and fertilizer must be prioritized in the study area.

Factors influencing adoption of major orchard management practices in mandarin orange of Gorkha: A case from mid-hills of Nepal

The mid-hills of Nepal have immense potential for mandarin orange production. However, its productivity is declining over the years despite an increasing trend in the cultivated area. Since the proper adoption of orchard management practices has been associated with increased productivity, it is, therefore, an imminent requirement to sustain the mandarin orange production for long in the country. Therefore, a research survey was carried out to assess the adoption of major orchard management practices and identify factors affecting its adoption so that the insights from this finding would provide potential policy implications for the rejuvenation of the declining orchards. Altogether, 93 households from Gandaki and Sahid Lakhan Rural municipalities, as well as Gorkha Municipality from Gorkha district were chosen for the study using a simple random sampling technique. Data were collected with a semi-structured interview which was analyzed using SPSS and Stata software. A seemingly unrelated regression (SUR) logit model was used for triangulating the effect of different variables on the adoption of major mandarin orange orchard management practices. The findings revealed that nearly two-third of the households reported citrus decline in their orchards. Further, the adoption of major orchard management practices was mostly found driven by regular access to extension agents, and the income generated from agriculture. Nevertheless, the adoption was plagued by the prevalence of citrus decline in the orchard. In light of these findings, participatory training programs related to the rejuvenation of declining orchards, regular advisory and extension services, and input subsidies to the smallholding farmers are suggested so that the adoption of major orchard management practices gets increased among the mandarin orange farmers.

Advances in Hazelnut (Corylus avellana L.) Rootstocks Worldwide

Studies on hazelnut (Corylus avellana L.) rootstocks have been limited to date. However, the use of vigorous, non-suckering rootstocks for this species could increase the cost-effectiveness of orchards by reducing the annual need to prune suckers, thus facilitating mechanical harvesting, and reducing orchard management costs and environmental impact. Seedlings of the non-suckering Turkish tree hazel (C. colurna L.) have been used traditionally in Serbia. In the 1970s, the United States Department of Agriculture in Corvallis, Oregon (USA) released the first two non-suckering clonal rootstocks—‘Dundee’ and ‘Newberg’—from open-pollinated seeds of C. colurna. Moreover, selection of C. avellana cvs. with few suckers is continuing. Trials carried out in different countries with own-rooted and grafted plants have shown good performance of grafted hazelnuts. Currently, some nurseries in several countries are propagating hazelnut rootstocks and grafting trees for planting commercial orchards. Interest in these cultivar/rootstock combinations is increasing, and more new orchards of grafted trees are expected to be planted in the coming years.

Orchard Management and Landscape Context Mediate the Floral Microbiome of Pear

Crop-associated microbiota are key factors affecting host health and productivity. Most crops are grown within heterogeneous landscapes, and interactions between management practices and landscape context often affect plant and animal biodiversity in agroecosystems. However, whether these same factors typically affect crop-associated microbiota is less clear. Here, we assessed whether orchard management strategies and landscape context affected bacterial and fungal communities in pear (Pyrus communis) flowers. We found that bacteria and fungi responded differently to management schemes. Organically-certified orchards had higher fungal diversity in flowers than conventional or bio-based integrated pest management (IPM) orchards, but organic orchards had the lowest bacterial diversity. Orchard management scheme also best predicted the distribution of several important bacterial and fungal genera that either cause or suppress disease, with organic and bio-based IPM best explaining the distributions of bacterial and fungal genera, respectively. Moreover, patterns of bacterial and fungal diversity were affected by interactions between management, landscape context, and climate. When examining the similarity of bacterial and fungal communities across sites, both abundance- and taxa-related turnover were mediated primarily by orchard management scheme and landscape context, and specifically the amount of land in cultivation. Our study reveals local- and landscape-level drivers of floral microbiome structure in a major fruit crop, providing insights that can inform microbiome management to promote host health and high-yielding quality fruit. IMPORTANCE. In tree fruits, proper crop management during bloom is essential for producing disease-free fruit. Tree fruits are often grown in heterogeneous landscapes; however, few studies have assessed whether landscape context and crop management affect the floral microbiome, which plays a critical role in shaping plant health and disease tolerance. Such work is key for identification of tactics and/or contexts where beneficial microbes proliferate, and pathogenic microbes are limited. Here, we characterize the floral microbiome of pear crops in Washington State, USA, where major production occurs in inter-mountain valleys and basins with variable elevation and microclimates. Our results show that both local- (crop management) and landscape-level (habitat types and climate) factors affect floral microbiota, but in disparate ways for each kingdom. More broadly, these findings can potentially inform microbiome management in orchards for promotion of host health and high-quality yields.

Orchard management with small unmanned aerial vehicles: a survey of sensing and analysis approaches

Advances in sensor miniaturization are increasing the global popularity of unmanned aerial vehicle (UAV)-based remote sensing applications in many domains of agriculture. Fruit orchards (the source of the fruit industry chain) require site-specific or even individual-tree-specific management throughout the growing season—from flowering, fruitlet development, ripening, and harvest—to tree dormancy. The recent increase in research on deploying UAV in orchard management has yielded new insights but challenges relating to determining the optimal approach (e.g., image-processing methods) are hampering widespread adoption, largely because there is no standard workflow for the application of UAVs in orchard management. This paper provides a comprehensive literature review focused on UAV-based orchard management: the survey includes achievements to date and shortcomings to be addressed. Sensing system architecture focusing on UAVs and sensors is summarized. Then up-to-date applications supported by UAVs in orchard management are described, focusing on the diversity of data-processing techniques, including monitoring efficiency and accuracy. With the goal of identifying the gaps and examining the opportunities for UAV-based orchard management, this study also discusses the performance of emerging technologies and compare similar research providing technical and comprehensive support for the further exploitation of UAVs and a revolution in orchard management.