Orchard Characteristics Research Articles

Once Common, Long in Decline: Dynamics of Traditional Orchards in a Central European Landscape

Traditional orchards are distinctive features of cultural landscapes in Central Europe. Despite their high level of ecological importance, they are in decline, and comprehensive spatial data over broad extents, which could enable a trend analysis, are lacking. We analysed traditional orchard maps from 1952 to 1967 and a map from 2010, generated via aerial image interpretation, for the state of Hesse (ca. 21,115 km2), which has the second largest share of traditional orchards in Germany. We aimed to (1) quantify long-term orchard dynamics, (2) compare orchard characteristics in terms of topographical, ecological, and socioeconomic factors, and (3) identify key drivers of orchard loss. We found that the number and area of orchards have clearly decreased across Hesse, with varying local and regional patterns. Further, historically old orchards tended to have a larger area, higher shape complexity, and were located closer to settlements, highways, and neighbouring orchards. In contrast, newly established orchards were often found at higher elevations and on steeper slopes. Finally, the three historical orchard hotspots also experienced the most notable losses driven by different factors, namely the expansion of Artificial Surfaces, Residential Buildings, and Agricultural Land. We highlight the importance of such multitemporal spatial data for a wide range of ecological applications, and we encourage the use of novel technologies to support geospatial analyses in the future.

High-resolution satellite imagery to assess orchard characteristics impacting water use

Most Orchards throughout the Mediterranean basin rely heavily on irrigation, a dependency increasing due to climate changes. Assessing the water requirement (WR) is crucial and depends on different factors, including orchard age, tree density per field, inter-row management. This study proposes new methods to evaluate these characteristics with remote sensing (RS). Various remote sensors providing high and very high spatial resolution images are investigated and their accuracy is assessed. The final objective is to assess WR using variables derived from remote sensing compared to data provided by water managers and from the FAO method. A typical Mediterranean watershed was selected in South-Eastern France, with orchards having various agricultural practices. Original methods were developed with Sentinel 2 (S2) data (2016–2023), 1 Pleiades image (2022) and the extraction of Google-satellite-hybrid images (GSH, 2017), and assessed using a large ground observation dataset (information on water use collected on 366 fields). Five orchards were monitored by capacitive sensors to assess the water balance. Irrigation durations ranged from 3–300 hours/year, with decision influenced by tree density and plot age. To identify young orchards, a thresholding approach on S2 derived NDVI effectively identified young orchards achieving a 98% accuracy rate. Grassed and non-grassed orchards were mapped using two methods, with a random forest classification using three spectral bands with 72% accuracy and a supervised approach yielding 81% accuracy for GSH and 57% for Pleiades. The performance depends on the acquisition date of images. A pattern detection algorithm applied to GSH and Pleaides determined tree density, showing a high correlation (r²=0.9) with observed data. These RS derived variables allowed to compute orchard water requirements at the watershed scale, ranging from 70 to 550 mm annually depending on management practices. The proposed methods can be extrapolated to other territories and are implemented using open access softwares.

Comparative analysis of machine learning classification algorithms for predicting olive anthracnose disease

<p>Olive anthracnose (OA) is the most damaging fungal disease of the olive tree worldwide. In the context of integrated pest management, the development of predictive models could be used for early diagnosis and control. In the current study, a dataset consisting of 58 cases, coming from 5 locations and 12 olive cultivars, was used to study the relationship between ΟΑ incidence (OAI) and 35 heterogeneous variables. These variables include orchard characteristics, olive fruit parameters, foliar and soil nutrients, soil parameters and soil texture classes. The Random Forest-Recursive Feature Elimination with Cross Validation (RF-RFECV) feature selection method identified Location, water content, P, Ca, Mg, exchangeable Mg, trace Zn, trace Cu as possible new indicators associated with OAI. The objective of this study was to investigate whether these variables have a predictive value for OAI. Six different machine learning classification algorithms, namely decision tree (DT), gradient boosting (GB), logistic regression (LR), random forest (RF), k-nearest neighbors (KNN) and support vector machine (SVM), were developed for predicting conditions leading to OAI > 0% and 10%. Grid search hyperparameter optimization was employed to optimize model parameters. The final models were evaluated in terms of several standard metrics, such as accuracy, sensitivity, specificity and ROC AUC score. Findings suggested that GB performance was superior compared to the other models for the prediction of the occurrence of OA disease (OAI > 0%) with an accuracy of 86.7%, a sensitivity of 100%, a specificity of 75% and a ROC-AUC score of 93%, while for the prediction of the spread of the disease (OAI > 10%), DT stood out with an accuracy of 86.7%, a sensitivity of 81.8%, a specificity of 100% and a ROC-AUC score of 91%.</p>

Effects of environmental drivers and irrigation on yields of macadamia orchards along an altitudinal gradient in South Africa

Macadamia is a high value tree nut crop experiencing a global rise in demand. South Africa is the worldwide largest producer and macadamia orchards are rapidly expanding in the country. However, yields are highly variable across years and have been declining in recent years. Therefore, to sustainably increase the productivity and resilience of macadamia orchards to climate change, the impact of environmental factors on the trees’ vegetative and reproductive cycles needs to be better understood. To this end, the extent to which orchard characteristics, climatic and soil factors, as well as irrigation amounts drive macadamia yields in Levubu (Limpopo Province, South Africa) along an altitudinal gradient (600–950 m a.s.l.), was quantified by using mixed-effects models. Climatic variables were selected separately for the different macadamia phenological stages, in which they potentially affected yields. Furthermore, the effects of different yield determining factors were compared between irrigated and rainfed orchards.A pronounced interannual variability of macadamia yields was found (from 1.2 to 4.0 tons dry nut-in-shell ha−1), although a triennial bearing pattern was observed. Higher yields were found at elevations >700 m a.s.l. and in micro-sprinkler irrigated orchards. Orchard characteristics and environmental variables jointly explained 49% of yield variability. Cultivar, presence of irrigation, tree age and planting density were found to affect yield, while no significant effect was found for soil variables (i.e. soil texture, bulk density, pH and C:N ratio). High temperatures and low global radiation during the nut development stages, alongside poor rainfall amounts in the dry season, were the climatic factors more severely affecting yields. In particular, low irradiance was the main yield limiting factor in irrigated orchards, while extremely high temperatures and poor rainy seasons in rainfed orchards. Increased irrigation amounts, although beneficial, were not fully compensating the impact of climatic factors on productivity. Our findings suggest that irrigation alone cannot counteract adverse climatic effects on macadamia yields. To sustainably increase macadamia productivity and resilience to climate change, abiotic stress impacts will have to be reduced through a combination of genetic improvement and better orchard management practices.

Evaluating the effects of different nutrient management strategies on apple (malus pumila) in intensive orchards of Kazakhstan. Results from a 4-year study

This article presents the findings of a four-year study in intensive apple orchards in southern Kazakhstan, evaluating different nutrient management strategies on apple trees (Malus pumila). The research aimed to assess the impact of nutrition plans on tree growth, productivity, and fruit quality, considering the region's unique soil and climate. The experiment, established in 2014, covered 1.5 hectares and consisted of Jeromini apple trees grafted onto M9 rootstock. Three treatment groups were established: a control group with no additional nutrition, a group with nutrition based on a generally accepted program, and a group with nutrition tailored to the apple tree's needs with fertigation. Phenological and biometric observations were conducted regularly from 2019 to 2022 to monitor tree performance. Soil and leaf samples were analyzed for nutrient content, and soil properties like pH and salinity were assessed. Statistical analysis was used to identify significant differences among the treatment groups. The research was conducted in intensive apple orchards located in the Turkestan region, known for its grey-brown soil. The climate in the study area was characterized as continental, with hot and dry summers and limited precipitation. Soil samples were analyzed for agrophysical and agrochemical properties, including nutrient levels and trace elements. The results showed that the control group had a decreasing trend in yield, while both the generally accepted program and the tailored nutrition program showed increasing trends in yield, with the latter showing a decrease in 2022. Over the fouryear period, the fruits from the control group exhibited reduced commercial qualities. Our program demonstrated similar sweetness and superior shape and yield compared to the conventional method. However, the storage quality of apples in our program was lower. Nevertheless, considering its positive impact on soil fertility and cost-effectiveness, our program appears to be an optimal choice. The findings of this study provide valuable insights into the effects of different nutrient management strategies on the growth, health, and productivity of apple trees in a garden setting. This information can benefit orchard growers and researchers seeking to optimize nutrient management practices for apple trees. The study also highlights the importance of considering various factors, such as orchard characteristics and environmental conditions, when designing nutrient management strategies for apple orchards.

No wild bees? Don't worry! Non-bee flower visitors are still hard at work: The edge effect, landscape, and local characteristics determine taxonomic and functional diversity in apple orchards

Native pollinators are key to enhancing fruit production in apple orchards and maintaining yield stability through taxonomic and functional diversity. Some studies suggest that the distance to the nearest native forest patch, the edge effect, or apple orchard management, modulate the richness and abundance of pollinators. However, such studies have focused on bees, thereby generating knowledge gaps about non-bee pollinators. This study looks at the taxonomic and functional diversity in central Chile, as well as the abundance of major pollinator groups (native beetles, flies, wild bees, and lepidopterans), and how these are influenced by landscape and apple orchard characteristics. For two seasons, native and managed pollinators were recorded in nine apple orchards with different management approaches (organic vs conventional) and apple varieties (Gala vs Fuji). The taxonomic and functional diversity of major pollinator groups were correlated with distance to the nearest native forest patch. We measured the edge effect on pollinators toward the interior of apple orchards and analyzed the results with one-way ANOVA. Lastly, we associated the diversity and abundance of pollinators with landscape and apple orchards characteristics using GLMM. Excepted managed bees and contrary to the global trend in apple studies, native flies and beetles were the most dominant pollinators, the latter being more abundant at the orchard edge. The distance to the native forest patch was correlated negatively to the functional richness and positively to body temperature, while that same variable influenced the abundance of total native pollinators, beetles, flies, and wild bees. The gala variety was found to attract greater pollinator richness and more wild bees, whereas organic orchards positively influence native flies and conventional orchards attract lepidopterans. Moreover, the number of honey bees influences the abundance of wild bees and lepidopterans. Land intensification practices could decrease the taxonomic and functional diversity of pollinators. Management alternatives like increased landscape heterogeneity or native habitat restoration, could to maintain apple fruit production in space and time.

Expansion of olive orchards and their impact on the cultivation and landscape through a case study in the countryside of Cordoba (Spain)

The sustainability of farming systems has been enhanced by legislation on different scales, but at the same time these policies also promote more productive systems through farming intensification (e.g., use of irrigation or high tree densities). This is the case of olive orchard expansion on cereal cropland in recent decades. This study analyses the impact of this expansion on orchard characteristics and landscape elements in a case study in the ’campiña‘ of Cordoba in Southern Spain based on the evolution of their surface and typologies during the period from 2005 to 2018. Our results show that olive orchards doubled their surface after the 13-year period, from 7997.8 to 16,447.6 ha. On average the new orchards tended to have higher plant density and a more frequent use of irrigation in the study period. Despite this trend towards intensification, the current situation shows a majority of rainfed (76.4%) and medium tree densities, 120–200 trees/ha, (42.7%) of the area. Nevertheless, newly intensified orchards are arising in the region, resulting in a mosaic of orchards of different characteristics (slope, tree density, soil type) and agricultural managements (irrigation, ground cover vegetation).In addition, this characterization was complemented with an inventory of the existing semi-natural elements associated with these orchards to identify the current state of the regional agricultural landscape. A total number of 507 isolated trees and different linear and polygonal landscape elements (343.9 km and 714.0 ha, respectively), mainly segmented, were inventoried. From these polygonal landscape elements, a significant fraction (e.g., slopes, gullies, water banks and non-productive strips/faces) remains unvegetated (57%). Therefore, these elements must be considered in multiscale agricultural policies as potential restoration areas to enhance ecosystem service provisioning.

Identifying adaptation strategies to climate change for Mediterranean olive orchards using impact response surfaces

The identification of agricultural adaptation strategies to offset climate change impact requires a detailed knowledge of the response of crop systems under future climatic conditions. To carry out such an analysis for Mediterranean olive orchards, an improved version of the AdaptaOlive simulation model combined with perturbed climate (PC) and sensitivity analysis using impact response surfaces (IRSs) have been applied. As a result, phenology, crop yield and irrigation requirements were projected for olive orchards located at five locations representative of Southern Spain, with contrasting current and future weather conditions. Thus, under near future conditions using Representative Concentration Pathway (RCP) 4.5, large differences in olive yield response were found depending on the location, with an average yield increase of 19% for locations with cold winters, and reductions of 34% for locations with mild winters. Olive yield response could show abrupt changes even for limited changes in weather variables when critical thresholds are exceeded. IRSs and PC enabled the identification of such site-specific thresholds in terms of changes in temperature and rainfall from the baseline conditions. Thus, temperature increases of 3 °C generated null crop damage linked to flowering failure caused by lack of chilling in Jaen or Granada, whereas in Seville the damage was severe in 74% of the years. The analysis of the impacts of water stress and temperature increase on olive yield revealed the critical processes that need to be addressed by promoting adaptation measures. Said processes were not the same for entire analyzed area; in Seville —which has mild winters— cultivars with an earlier flowering date should be recommended, but in Granada —which has cold winters and low rainfall— the introduction of deficit irrigation strategies would be the appropriate recommendation. These results confirm the need to conduct weather sensitivity analyses, as provided by PC and IRSs, in order to develop site-specific adaptation strategies for the Mediterranean olive orchards. This procedure was required due to the non-smooth response to weather components, and the high spatial variability of weather conditions and olive orchard characteristics in Southern Spain.

Advancing Agricultural Production With Machine Learning Analytics: Yield Determinants for California's Almond Orchards.

Agricultural productivity is subject to various stressors, including abiotic and biotic threats, many of which are exacerbated by a changing climate, thereby affecting long-term sustainability. The productivity of tree crops such as almond orchards, is particularly complex. To understand and mitigate these threats requires a collection of multi-layer large data sets, and advanced analytics is also critical to integrate these highly heterogeneous datasets to generate insights about the key constraints on the yields at tree and field scales. Here we used a machine learning approach to investigate the determinants of almond yield variation in California’s almond orchards, based on a unique 10-year dataset of field measurements of light interception and almond yield along with meteorological data. We found that overall the maximum almond yield was highly dependent on light interception, e.g., with each one percent increase in light interception resulting in an increase of 57.9 lbs/acre in the potential yield. Light interception was highest for mature sites with higher long term mean spring incoming solar radiation (SRAD), and lowest for younger orchards when March maximum temperature was lower than 19°C. However, at any given level of light interception, actual yield often falls significantly below full yield potential, driven mostly by tree age, temperature profiles in June and winter, summer mean daily maximum vapor pressure deficit (VPDmax), and SRAD. Utilizing a full random forest model, 82% (±1%) of yield variation could be explained when using a sixfold cross validation, with a RMSE of 480 ± 9 lbs/acre. When excluding light interception from the predictors, overall orchard characteristics (such as age, location, and tree density) and inclusive meteorological variables could still explain 78% of yield variation. The model analysis also showed that warmer winter conditions often limited mature orchards from reaching maximum yield potential and summer VPDmax beyond 40 hPa significantly limited the yield. Our findings through the machine learning approach improved our understanding of the complex interaction between climate, canopy light interception, and almond nut production, and demonstrated a relatively robust predictability of almond yield. This will ultimately benefit data-driven climate adaptation and orchard nutrient management approaches.

California Almond Yield Prediction at the Orchard Level With a Machine Learning Approach.

California’s almond growers face challenges with nitrogen management as new legislatively mandated nitrogen management strategies for almond have been implemented. These regulations require that growers apply nitrogen to meet, but not exceed, the annual N demand for crop and tree growth and nut production. To accurately predict seasonal nitrogen demand, therefore, growers need to estimate block-level almond yield early in the growing season so that timely N management decisions can be made. However, methods to predict almond yield are not currently available. To fill this gap, we have developed statistical models using the Stochastic Gradient Boosting, a machine learning approach, for early season yield projection and mid-season yield update over individual orchard blocks. We collected yield records of 185 orchards, dating back to 2005, from the major almond growers in the Central Valley of California. A large set of variables were extracted as predictors, including weather and orchard characteristics from remote sensing imagery. Our results showed that the predicted orchard-level yield agreed well with the independent yield records. For both the early season (March) and mid-season (June) predictions, a coefficient of determination (R2) of 0.71, and a ratio of performance to interquartile distance (RPIQ) of 2.6 were found on average. We also identified several key determinants of yield based on the modeling results. Almond yield increased dramatically with the orchard age until about 7 years old in general, and the higher long-term mean maximum temperature during April–June enhanced the yield in the southern orchards, while a larger amount of precipitation in March reduced the yield, especially in northern orchards. Remote sensing metrics such as annual maximum vegetation indices were also dominant variables for predicting the yield potential. While these results are promising, further refinement is needed; the availability of larger data sets and incorporation of additional variables and methodologies will be required for the model to be used as a fertilization decision support tool for growers. Our study has demonstrated the potential of automatic almond yield prediction to assist growers to manage N adaptively, comply with mandated requirements, and ensure industry sustainability.

Prediction of kiwifruit orchard characteristics from satellite images

The dry matter content of New Zealand kiwifruit is currently measured using destructive testing of a 90-fruit sample. Dry matter content varies from 14 to 20% with a standard deviation of 1.05% in the test measurement. This work investigates the use of multispectral data from satellite images of kiwifruit orchards to predict the dry matter of both green and gold kiwifruit. A novel method is developed that reduces the four-dimensional satellite data to three-dimensional unit color vectors and these show a strong linear relationship with measured dry matter. Regression on the data from a set of 20 ‘training’ orchards yielded predictions of dry matter with a standard deviation of 0.76%. When the resulting model was applied to nine test orchards the standard deviation of predicted dry matter was 0.73%. The prediction of dry matter was accurate even when applied to images taken when the fruit was too immature for the standard dry matter measurement test. Therefore, satellite image data may provide a more accurate and non-destructive alternative to the standard 90-fruit test method. It can also provide a way to visualize the variation in dry matter content of the fruit in an orchard; showing regions that would benefit from remedial action and defining areas where fruit harvesting is optimal. Several vegetation indices, derived from satellite image data, are reported in the literature. The strength of the linear correlation between dry matter and twelve common vegetation indices was tested but was found to be much weaker than the correlation developed in this research.

Bird use of organic apple orchards: Frugivory, pest control and implications for production.

As the largest terrestrial biomes, crop and pasturelands can have very large positive or negative impacts on biodiversity and human well-being. Understanding how animals use and impact agroecosystems is important for making informed decisions that achieve conservation and production outcomes. Yet, few studies examine the tradeoffs associated with wildlife in agricultural systems. We examined bird use of organic apple orchards as well as how birds influence fruit production positively through control of an economically important insect pest (codling moth (Cydia pomonella)) and negatively through fruit damage. We conducted transect surveys, observed bird frugivory and assessed bird and insect damage with an exclosure experiment in small organic farms in western Colorado. We found that organic apple orchards in this region provide habitat for a large number of both human-adapted and human-sensitive species and that the species in orchards were relatively similar to adjacent hedgerow habitats. Habitat use did not vary as a function of orchard characteristics, and apple damage by both birds and C. pomonella was consistent within and across apple blocks that varied in size. A small subset of bird species was observed foraging on apples yet the effect of birds as agents of fruit damage appeared rather minor and birds did not reduce C. pomonella damage. Our results demonstrate that organic apple orchards have the potential to provide habitat for diverse bird communities, including species typically sensitive to human activities, with little apparent effect on production.

Management strategies in apple orchards influence earwig community

Our aim was to assess whether different apple orchard management strategies (low-input, organic, Integrated Pest Management (IPM)) would have an effect on earwigs, which are important natural enemies of apple pests. These commercial orchards were as well compared to abandoned orchards. The density of Forficula auricularia and Forficula pubescens was studied for three years in 74 orchards around Avignon. The pesticide usage, some orchard characteristics and two small-scale landscape parameters were characterized. Pesticide use was significantly different between low-input, organic and IPM orchards with particularly significant differences in the number of insecticide applications (2.2, 4.9 and 9.2 respectively). Pesticide use had a much stronger impact on earwig community than other characteristics. F. auricularia density was significantly lower in IPM orchards (0.47 individuals per tree) compared to organic, low-input and abandoned orchards (3.1, 4.5 and 1.6 individuals per tree, respectively). F. pubescens was almost absent from IPM orchards and its abundance was higher in abandoned or low-input orchards compared to organic orchards (1.5 and 2.8 vs 0.8 individuals per tree). The percentage of F. pubescens in the earwig community decreased from abandoned (52%) to low-input (40%), organic (15%) and IPM orchards (0.5%). These results were confirmed by LD50 assays showing that for the two pesticides causing mortality close to normal application rates (chlorpyrifos-ethyl and acetamiprid), F. pubescens was significantly more sensitive than F. auricularia. Since earwigs are also easy to capture and identify, they may be useful to estimate the effects of management strategies and their modification in pome fruit orchards.

Modelling of Codling Moth Damage as a Function of Adult Monitoring, Crop Protection and Other Orchard Characteristics

The codling moth, the major insect pest in apple and pear orchards, is responsible for most insecticide treatments in European and North American orchards. Despite the intensive insecticide pressure, the codling moth remains a concern for fruit growers and can have a locally dramatic impact on production. To reduce pesticide use, there is a need to better determine the factors that affect the level of damage in orchards. The number of damaged fruits at the end of the codling moth’s first flight in pomefruit commercial orchards quantifies the severity of the attack. However, this variable sums the unobserved damages that occurred throughout this period, depending on a damage risk process that is likely driven by time-dependent and observable covariates. Moreover, as in most ecological/epidemiological studies, the data sets are incomplete and heterogeneous. The statistical challenge here is to build a sensible stochastic model to handle grouped and various data sets that are both right- and left-censored. In this paper, we model the temporal damage risk by combining survival methods with generalised linear mixed models to account for a time varying regression analysis. The results indicate that adult trapping provides useful information on the local level of fruit damages. Unexpectedly, the number of insecticide treatments did not appear to be a significant covariate for fruit damage modelling. The significance of the random component of the model indicates that a substantial portion of the variation in fruit damage remains, demanding further investigation concerning other relevant agronomic and environmental covariates.

Effects of Orchard Characteristics and Operator Performance on Harvesting Rate of a Mechanical Sweet Cherry Harvester

A model was developed to simulate sweet cherry harvesting with a mirrored-pair mechanical harvest system that removes fruit by transferring vibrational energy to tree limbs through an impactor. Six orchard characteristic variables (ST – tree spacing; SR – row spacing; NT – number of trees per row; NR – number of rows; NTB – number of branches per tree; and WTF – total weight of fruit per tree) and three harvester/operator characteristic variables (vH – forward speed of harvester, tIP – time to position impactor on actuation point, and tPS – shaking time per actuation point) were the main inputs to the model. Total harvest time (tTH) and harvesting rate were the two output variables of the model. Harvesting rate was evaluated with three different measures: time rate of area coverage (RAC), time rate of tree coverage (RTC), and time rate of fruit removal by weight (RFR). The model was validated with field data, showing very close predictions with modeling efficiencies of 99%, 86%, 82% and 84% respectively for tTH, RAC, RTC, and RFR. Local sensitivity analysis was conducted varying the input variables in five different levels in order to observe their effect on the output variables. A global sensitivity analysis was also performed to identify input variables with significant effects on the output variables. Data from a complete factorial experiment with three levels of input variables in 19,683 combinations was used to perform the global sensitivity analysis. . It was revealed that ST and SR only affected RAC by defining the unit area occupied by a single tree. NT and NR only affected tTH by determining the number of trees to be harvested, but had no effect on harvesting rate. NTB greatly affected harvesting time and all measures of harvesting rate, and was identified as the most important variable. WTF only affected RFR by determining how much fruit is removed in a single shaking event. Of the harvester/operator variables, tIP affected all the outputs the most whereas vH affected none. Except for RAC, which was least affected by SR, tPS had the least effect amid all the significant input variables. These results provide explanation for achieving different harvesting rates in different orchard settings, and can be used to optimize orchard characteristics and adjust operator behaviors for improved performance in mechanical sweet cherry harvesting.

Mango-based orchards in Senegal: diversity of design and management patterns

Introduction. Mango-based orchards in Senegal occur in a large diversity of cropping systems, but few typologies of these systems exist and none are associated with their compre- hensive and quantitative analysis. In this study we defined and characterized the typology of these systems based on a quantitative assessment of their planting design, management, vegetative state, hedgerow structure and infestation by a major pest of mango, the Bactrocera invadens fly. Materials and methods. Multivariate analysis and clustering methods were applied to data from 64 mango-based orchards and their surrounding hedgerows sampled in the Dakar and Thies re- gions, in Senegal. Results and discussion. Four types of cropping systems were identified ac- cording to orchard design and management patterns: (1) 'No-input mango diversified orchards', (2) 'Low-input mango orchards', (3) 'Medium-input citrus-predominant orchards' and (4) 'Me- dium-input large mango- or citrus-predominant orchards'. Orchard characteristics varied among these patterns. For instance, vegetation was dense and homogeneous in system 1, and the mor- tality rate of trees was high in system 2 but low in system 3. Orchards of systems 3 and 4 were mostly associated with hedgerows with, respectively, boundary-marking and defensive species. Lastly, the number of B. invadens flies was high in orchards of system 4, whereas it was low in those of system 2. Conclusion. The diversity of mango-based cropping systems in Senegal is now well described and quantified. This characterization is a preliminary step that is essential for further studies aiming to improve these systems. Senegal / Mangifera indica / fruit trees / orchards / typology / design / crop management / Bactrocera invadens / hedges / multivariate analysis Vergers a base de manguiers au Senegal : diversite des modeles de conception et de gestion.

COMPARISON OF PLATFORM VERSUS LADDERS FOR HARVEST IN NORTHERN CALIFORNIA PEAR ORCHARD

A self-propelled platform (Argiles AF-8) was tested in four mature ‘Bartlett’ pear orchards in Lake County, California in 2006 and 2007 to address productivity, post-harvest quality, ergonomics and worker satisfaction. Orchard characteristics included densities of 640-834 trees/ha, heights of 5.7-4.3 m, and trunk-to-canopy edge distances of 1.7-0.7 m. This contrasts with example orchards from Portugal and Spain with 1600 and 2300 trees/ha, 3 m and 2.5 m heights, and 0.9 and 0.5 m trunkto-canopy edge distances, respectively. Results from a 12-day machine trial using a somewhat atypical hourly-paid crew (majority female, average age 43 years and 15 years orchard experience) were compared to the norm of a typical piece-rate crew harvesting from 5.3 m ladders (100% male, average age 27 years and 4 years orchard experience). Platform productivity, expressed in 500 kg bins/worker/day, averaged 1.8 for selective (partial tree) harvest and 3.4 for complete fruit removal (“stripping”) compared to the ladder crew’s 3.5 and 5.5, respectively. Productivity was highest where the canopy was narrowest (0.7 m). Platform-harvested fruit had 57% fewer stem punctures. 2007 platform modifications included night-time lights, overhead shading, increased vertical reach, and re-configured conveyors to target a productivity goal of 6 bins/worker/day. Productivity for the hourly-paid mixedgender crew over 5 days and 2 nights was similar to 2006, but increased 75% (3.4 to 5.9) for a 100% male crew paid piece rate, comparable to an average ladder crew. Post-harvest quality was similar to 2006, despite faster picking rate. Worker ergonomics and satisfaction favored the platform both years. Hourly-paid platform crew productivity was comparable to that in Portugal and Spain but California’s short harvest window and 35-70% higher per hectare yields necessitate higher machine productivity. Major barriers to widespread adoption of platforms for harvest in California pear orchards are 1) capital and maintenance costs, 2) orchard renovation expense, 3) fruit sorting challenges and 4) lack of imminent drastic labor shortage. Overhead costs might be mitigated with lower insurance rates, machine use in pruning, fire blight cutting, pheromone tying, night-time harvesting fruit thinning. Further harvest mechanization research is being initiated.

Weed community structure of mandarin orchards under conventional and integrated management in northern Spain

Abstract Mandarin orchards under conventional and integrated production (IP) practices were surveyed in the area around the delta of the Ebro River (Spain). The floristic and functional structures were evaluated to compare weed communities and to determine the relationship between community structure, mandarin orchard characteristics and type of management. The alpha diversity observed was 12.3 (S.D. 5.84) and 18.0 (S.D. 5.50) for conventional and IP orchards, respectively, and gamma diversity was 77 for conventional and 96 for IP agricultural practices. Differences were found in overall inter-row vegetation cover (11.3% conventional, 90.4% IP). Analyses of variance and mean comparison tests revealed no differences in any of the functional traits considered between the two types of management. All the multivariate methods of analysis employed revealed that the weed community composition was related to the inter-row management system. Mowing orchard inter-rows led to a successional trajectory from typically agrestal weed communities to ruderal vegetation, giving rise to high richness and also high cover.

Innovations favouring environmental sustainability in avocado orchards: an analysis of the Spanish Mediterranean coastlands

Avocado cultivation started to take hold on the Southern Spanish Mediterranean coast in the early 1970s. Today, avocado is the most widespread tropical crop in the area, occupying some 8,350 ha; average annual production is around 70,000 Mg. In recent years, several technological innovations have been adopted by avocado growers, some of which favour the environmental sustainability of the crop. Among the practices adopted are several non-tillage or conservation tillage techniques (which use no —or very little— herbicide), flower pruning, the use of weed clearing machines, mulching, and organic and integrated farming systems. This paper reports the results of a survey of avocado growers from the southeastern coast of Spain, and analyses their adoption of environmentally friendly technologies. To identify the grower and orchard characteristics that encourage such adoption, an aggregate innovation index was created and a multinomial ordered probit model constructed. The findings might help in the design of strategies for increasing the adoption of environmentally safe technologies.

Factors Affecting the Spread of Plum pox virus Strain M in Peach Orchards Subjected to Roguing in France

ABSTRACT We evaluated the impact of roguing on the spread and persistence of the aggressive Plum pox virus strain M (PPV-M) in 19 peach orchard blocks in Southern France. During a 7- to 10-year period, orchards were visually inspected for PPV symptoms, and symptomatic trees were removed every year. Disease incidence was low in all orchards at disease discovery and was <1% in 16 of the 19 orchard blocks. The spread of Sharka disease was limited in all 19 blocks, with an annual disease incidence between 2 and 6%. However, new symptomatic trees were continuously detected, even after 7 to 10 years of uninterrupted control measures. An extended Cox model was developed to evaluate to what extent tree location, orchard characteristics, environment, and disease status within the vicinity influenced the risk of infection through time. Eleven variables with potential effect on tree survival (i.e., maintenance of a tree in a disease- free status through time) were selected from survey data and databases created using a geographical information system. Area of the orchard, density of planting, distance of a tree from the edge of the orchard block sharing a boundary with another diseased orchard, and distance to the nearest previously detected symptomatic tree had a significant effect on the risk for a tree to become infected through time. The combined results of this study suggest that new PPV-M infections within orchards subjected to roguing resulted from exogenous sources of inoculum, disease development of latent infected trees, as well as infected trees overlooked within the orchards during visual surveys. A revision of the survey and the roguing procedures used for more effective removal of potential sources of inoculum within the orchards and in the vicinity of the orchards would improve disease control suppression of PPV.