Postharvest losses of fresh produce constitute the biggest portion of the total food losses occurring in food chains globally. The main driver behind the postharvest losses of fresh fruits and vegetables is temperature abuse occurring mainly during transportation and storage. This is a particular problem for small-acreage producers, who frequently have limited access to postharvest handling resources like optimum refrigeration conditions. Passive modified atmosphere packaging (MAP) is a relatively inexpensive intervention that does not require specialized equipment and has demonstrated some potential for maintaining the quality and extending the shelf life of fresh produce stored in nonoptimum temperatures. Our objective was to determine the effect of passive MAP on the quality and storage life of spinach (Spinacia oleracea cv. Corvair) when stored in nonoptimum temperatures. Mature spinach leaves (≈320 g) were packaged in passive MAP bags, developed using the BreatheWay technology, and non-MAP produce bags and subsequently stored at 13 or 21 °C. Spinach physical and nutritional quality was evaluated throughout its storage life in terms of overall quality, water loss, leaf tenderness, surface color, chlorophyll content, electrolyte leakage, chlorophyll fluorescence, antioxidant capacity, total phenolic content, and vitamin C content. Spinach that was stored in MAP bags reached headspace equilibrium at ≈6% Ο2 and 11% CΟ2 at 13 °C and ≈4% Ο2 and 8% CΟ2 at 21 °C after 2 days of storage for both temperatures. The spinach stored in passive MAP at 13 or 21 °C demonstrated significantly higher overall quality during storage and 2 and 1 day longer storage life, respectively, when compared with the control. The spinach in passive MAP demonstrated a slower rate of yellowing and water loss during storage. The limiting factor for the spinach stored in MAP was decay due to condensation at 13 °C and yellowing at 21 °C. There were no statistical differences in the examined nutritional quality parameters between the spinach stored in MAP and produce bags. This study shows that passive MAP can be a valuable tool for reducing the food losses occurring in small-acreage fruit and vegetable operations that have limited access to cooling and refrigerated storage.

The implementation of high tunnels has shown to increase marketability and/or yield of tomato (Solanum lycopersicum) and lettuce (Lactuca sativa) crops compared with open-field systems. These structures provide the opportunity to alter light intensity and spectral quality by using specific polyethylene (poly) films and/or shadecloth, which may affect microclimate and subsequent crop productivity. However, little is known about how specific high tunnel coverings affect these parameters. The overall goal of this study was to evaluate the impact of various high tunnel coverings on the microclimate and crop productivity of tomato and lettuce. The coverings included standard, ultraviolet (UV)-stabilized poly film (standard); diffuse poly (diffuse); full-spectrum clear poly (clear); UV-A/B blocking poly (block); standard + 55% shadecloth (shade); and removal of standard poly 2 weeks before initial harvest to simulate a movable tunnel (movable). Microclimate parameters that were observed included canopy and soil temperatures, canopy growing degree-days (GDD), and photosynthetic active radiation (PAR), and crop productivity included yield and net photosynthetic rate. Hybrid red ‘BHN 589’ tomatoes were grown during the summer, and red ‘New Red Fire’ and green ‘Two Star’ leaf lettuce were grown in both spring and fall in 2017 and 2018. Increased temperature, GDD, and PAR were observed during the spring and summer compared with the fall. The soil temperatures during the summer increased more under the clear covering compared with the others. For tomato, the shade produced lower total fruit yield and net photosynthetic rate (Pn) compared with the other treatments, which were similar (P < 0.001 and <0.001, respectively). The greatest yield was 7.39 kg/plant, which was produced under the clear covering. For red leaf lettuce grown in the spring, the plants under the clear, standard, and diffuse coverings had significantly greater yield than the movable and shade coverings (P < 0.001). The coverings had less effect on the yield during the fall lettuce trials, which may have been attributed to the decrease in PAR and environmental temperatures. The findings of this study suggest that high tunnel coverings affect both microclimate and yield of lettuce and tomato.

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For the nonparametric regression models with covariates contaminated with the normal measurement errors, this paper proposes an extrapolation algorithm to estimate the regression functions. By applying the conditional expectation directly to the kernel‐weighted least squares of the deviations between the local linear approximation and the observed responses, the proposed algorithm successfully bypasses the simulation step in the classical simulation extrapolation, thus significantly reducing the computational time. It is noted that the proposed method also provides an exact form of the extrapolation function, although the extrapolation estimate generally cannot be obtained by simply setting the extrapolation variable to negative one in the fitted extrapolation function, if the bandwidth is less than the SD of the measurement error. Large sample properties of the proposed estimation procedure are discussed, as well as simulation studies and a real data example being conducted to illustrate its applications.

For the general parametric regression models with covariates contaminated with normal measurement errors, this paper proposes an accelerated version of the classical simulation extrapolation algorithm to estimate the unknown parameters in the regression function. By applying the conditional expectation directly to the target function, the proposed algorithm successfully removes the simulation step, by generating an estimation equation either for immediate use or for extrapolating, thus significantly reducing the computational time. Large sample properties of the resulting estimator, including the consistency and the asymptotic normality, are thoroughly discussed. Potential wide applications of the proposed estimation procedure are illustrated by examples, simulation studies, as well as a real data analysis.

For the nonparametric regression models with covariates contaminated with normal measurement errors, this paper proposes an extrapolation algorithm to estimate the nonparametric regression functions. By applying the conditional expectation directly to the kernel-weighted least squares of the deviations between the local linear approximation and the observed responses, the proposed algorithm successfully bypasses the simulation step needed in the classical simulation extrapolation method, thus significantly reducing the computational time. It is noted that the proposed method also provides an exact form of the extrapolation function, but the extrapolation estimate generally cannot be obtained by simply setting the extrapolation variable to negative one in the fitted extrapolation function if the bandwidth is less than the standard deviation of the measurement error. Large sample properties of the proposed estimation procedure are discussed, as well as simulation studies and a real data example being conducted to illustrate its applications.

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