Assessing Feeding Damage from Two Leaffooted Bugs, Leptoglossus clypealis Heidemann and Leptoglossus zonatus (Dallas) (Hemiptera: Coreidae), on Four Almond Varieties.

Leaffooted bugs (Leptoglossus spp; Hemiptera: Coreidae) are phytophagous insects native to the Western Hemisphere. In California, Leptoglossus clypealis and Leptoglossus zonatus are occasional pests on almonds. Early season feeding by L. clypealis and L. zonatus leads to almond drop, while late season feeding results in strikes on kernels, kernel necrosis, and shriveled kernels. A field cage study was conducted to assess feeding damage associated with L. clypealis and L. zonatus on four almond varieties, Nonpareil, Fritz, Monterey, and Carmel. The objectives were to determine whether leaffooted bugs caused significant almond drop, to pinpoint when the almond was vulnerable, and to determine the final damage at harvest. Branches with ~20 almonds were caged and used to compare almond drop and final damage in four treatments: (1) control branches, (2) mechanically punctured almonds, (3) adult Leptoglossus clypealis, and (4) adult Leptoglossus zonatus. Replicates were set up for eight weeks during two seasons. Early season feeding resulted in higher almond drop than late season, and L. zonatus resulted in greater drop than L. clypealis. The almond hull width of the four varieties in the study did not influence susceptibility to feeding damage. The final damage assessment at harvest found significant levels of kernel strikes, kernel necrosis, and shriveled almonds in bug feeding cages, with higher levels attributed to L. zonatus than L. clypealis. Further research is warranted to develop an Integrated Pest Management program with reduced risk controls for L. zonatus

Fatty acid bioaccessibility and structural breakdown from in vitro digestion of almond particles.

Previous studies have shown that the size of almond particles influences lipid bioaccessibility during digestion. However, the extent of structural breakdown of almond particles during gastric digestion and its impact on lipid bioaccessibility is unclear. In this study, in vitro digestion of almond particles was conducted using a dynamic model (Human Gastric Simulator) and a static model (shaking water bath). Structural breakdown of particles during the gastric phase occurred only in the Human Gastric Simulator, as evidenced by a reduction in particle size (15.89 ± 0.68 mm2 to 12.19 ± 1.29 mm2, p < 0.05). Fatty acid bioaccessibility at the end of the gastric phase was greater in the Human Gastric Simulator than in the shaking water bath (6.55 ± 0.85% vs. 4.54 ± 0.36%, p < 0.01). Results showed that the in vitro model of digestion which included peristaltic contractions (Human Gastric Simulator) led to breakdown of almond particles during gastric digestion which increased fatty acid bioaccessibility

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

Prospective randomized controlled pilot study on the effects of almond consumption on skin lipids and wrinkles.

ObjectiveAlmonds are a rich source of fatty acids and antioxidants, and their supplementation is known to significantly modulate serum lipids. The effects of almond on the skin's lipid barrier and the appearance of wrinkles have not yet been elucidated. The aim of this study was to investigate the effects of almond consumption on facial sebum production and wrinkles.MethodsThis was a prospective, investigator-blinded, randomized controlled trial in which subjects consumed 20% of their daily energy consumption in either almonds or a calorie-matched snack for 16 weeks. This study was completed at the UC Davis Dermatology clinic. Participants were a volunteer sample of generally healthy postmenopausal females with Fitzpatrick skin types 1 and 2. A facial photograph and image analysis system was used to obtain standardized photographs and information on wrinkle width and severity at 0, 8, and 16 weeks. Measurements of transepidermal water loss and sebum production were also completed at 0, 8, and 16 weeks.ResultsFifty healthy postmenopausal females were recruited, 31 participants were enrolled, and 28 completed the study. Under photographic analysis, the almond group had significantly decreased wrinkle severity and width compared with the control group at 16 weeks (p < .02). Changes in skin barrier function were nonsignificant, measured by the transepidermal water loss (p = .65) between the almond and control groups relative to baseline after 16 weeks. No adverse effects were reported.ConclusionOur study demonstrates that daily almond consumption may reduce wrinkle severity in postmenopausal females to potentially have natural antiaging benefits

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 (R 2) 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

DO U.S. MARKETING ORDERS HAVE MUCH MARKET POWER? AN EXAMINATION OF THE ALMOND BOARD OF CALIFORNIA

This paper tests the conventional wisdom that U.S. marketing orders act as profit-maximizing cartels. The paper analyzes the marketing order for U.S. almonds in both the domestic and export markets. Such a case study is relevant to all U.S. marketing orders because the size and scope of the U.S. almond industry on the world market, and the legal authority of the almond marketing order makes it a likely prospect for exhibiting true cartel behavior. The authors find that the market power exerted by the Almond Board of California's reserve setting is significantly less than would be expected from a profit-maximizing cartel.Marketing,

EXPORT DEMAND FOR U.S. ALMONDS: IMPACTS OF U.S. EXPORT PROMOTION PROGRAMS

The purpose of this study was to estimate the impact of the major factors affecting the export demand for U.S. almonds in Asia and the E.U. which together import about 93% of U.S. almond exports. The primary objective pertained to the impacts of federal promotion programs on the foreign demand for U.S. almonds. Based on previous literature, a single-equation framework was specified for estimation of the almond model. Based on promotion elasticities, impacts on almond export revenue from promotion were evaluated. The marginal return per dollar to decreasing promotion expenditures for almonds was $47.74 for Asia, reflecting prudent promotion expenditures for more efficient utilization of promotion funds as the Asian market for U.S. almonds approaches maturity. The E.U. appears to be a mature market for U.S. almond exports with no detectable responsiveness to promotion expenditures. Thus, simple reminder-type promotion activities for this market may be sufficient.Demand and Price Analysis, Marketing,

Multivariate classification of prunus dulcis varieties using leaves of nursery plants and near infrared spectroscopy

The emergence of new almond tree (Prunus dulcis) varieties with agricultural interest is forcing the nursery plant industry to establish quality systems to keep varietal purity in the production stage. The aim of this study is to assess the capability of near-infrared spectroscopy (NIRS) to classify different Prunus dulcis varieties as an alternative to more expensive methods. Fresh and dried-powdered leaves of six different varieties of almond trees of commercial interest (Avijor, Guara, Isabelona, Marta, Pentacebas and Soleta) were used.Peer ReviewedPostprint (published version