DOI: 10.5061/dryad.t4b8gtjf1
title: Data from: Comparison of yield characteristics of independence, a self-compatible almond variety, as affected by presence of honey bee pollinators

description: Until recently, all the almond varieties grown globally, have been
 self-incompatible, a fact that drives the interdependent relationship
 between almond growers and the beekeepers that provide honey bee colonies
 for pollination. Recently, almond growers have begun a transition to
 self-fertile varieties, such as Independence, which do not require
 genetically distinct pollen from another ‘pollinizer’ (or pollenizer)
 variety to produce kernels. This transition is often combined with the
 assumption that due to the self-compatible nature of the trees, honey bee
 colony rentals for pollination can in turn be eliminated by the grower,
 thereby reducing costs for these orchards. However, the impact of
 eliminating rented colonies on almond quality and yield are not fully
 understood. Here, we explore the extent to which yields for Independence
 variety vary with and without honey bee pollination activity and across a
 range of hive stocking densities in almond orchards in the San Joaquin
 Valley of California. Practical implication: We found that trees that
 recieved honey bee pollination show significantly improved nut set and
 whole tree yields, ultimately resulting in economically meaningful gains
 to growers. Our economic model suggests specific honey bee hive stocking
 density recommendations considering that the surrounding landscape which
 will likely change as the acreages of self-fertile varieties continue to
 increase.
description: # Data from: Comparison of yield characteristics of independence, a
 self-compatible almond variety, as affected by presence of honey bee
 pollinators Dataset DOI:
 [10.5061/dryad.t4b8gtjf1](10.5061/dryad.t4b8gtjf1) ## Description of the
 data and file structure ### Files and variables #### File:
 2021_Honey_bee_visits_increase_yields_in_a_self-fertile_almond_variety_HoneyBeeVisitationData.csv **Description:** Honey bee visitation data from 2021 portion of the study.  ##### Variables * Date: Date * Avg_temp: Temperature (F) * Ranch: Location ID * HPA: Hives per acre * Zone: Location ID (within Ranch) * Treatment: Tree level treatment (Open/Shade Control) * Flowers: Number of flowers observed for visitation survey * Visits: Number of visits observed during 5-minute visitation survey * V_Frq: Visitation Frequency (Visits/Flower/Time) #### File: 2021_Honey_bee_visits_increase_yields_in_a_self-fertile_almond_variety_KernelQualityData.csv **Description:** Kernel Quality data for the 2021 portion of the study.  ##### Variables * Date_harvested: Date 1 * Date_weighed: Date 2 * Ranch: Location ID * Zone: Location ID * Treatment: Tree-level treatment (Exclusion/Open/Shade Control) * Almond_num: Sample number * Weight_g: Sample weight (grams) #### File: 2021_Honey_bee_visits_increase_yields_in_a_self-fertile_almond_variety_FruitdropData.csv **Description:** Fruit drop (difference between initial and final nut set) for the 2021 portion of the study. ##### Variables * Date: Date * Ranch: Location ID * Zone: Location ID * HD: Hives per acre * Tree: Tree ID * Branch: Branch ID * Treatment: Tree-level treatment (Exclusion/Open/Shade Control) * pct_IFS_total: Initial nut set (%) * FFS_Pct_TTL: Final nut set (%) * diff_pct: Difference between the initial and final nut set  Note: NA (not available) value(s) in this dataset relate to lost data (e.g., broken branch).  #### File: 2021_Honey_bee_visits_increase_yields_in_a_self-fertile_almond_variety_NutsetData.csv **Description:** Nut set (Kernels/Flowers) for the 2021 portion of the study. ##### Variables * Date: Date * Ranch: Location ID * HD: Hives per Acre * Tree: Tree ID * Zone: Location ID (within Ranch) * Branch: Branch ID * Treatment: Tree-level treatment (Exclusion/Open/Shade Control) * pct_IFS_total: Initial nut set (%) * FFS_Pct_TTL: Final nut set (%) #### File: 2020_Honey_bee_visits_increase_yields_in_a_self-fertile_almond_variety_WholeTreeYieldsData.csv **Description:** Whole tree yields from the 2020 portion of the study ##### Variables * Row: Location ID * Tree: Tree ID * Treatment: Tree-level treatment (Exclusion/Open) * Wet_weight_g: Fresh Harvested Weight (grams) * Dry_Weight_g: Weight after 2-weeks (grams) #### File: 2020_Honey_bee_visits_increase_yields_in_a_self-fertile_almond_variety_NutsetData.csv **Description:** Nut set (Kernels/Flowers) from the 2020 portion of the study ##### Variables * Trees: Tree ID * branch: Branch ID * Treatment: Tree-level treatment (Exclusion/Open) * Nut_set_pre_June_drop: Initial nut set (%) * Nut_set_post_June_drop: Final nut set (%) #### File: 2020_Honey_bee_visits_increase_yields_in_a_self-fertile_almond_variety_KernelQualityData.csv **Description:** Kernel quality data from the 2020 portion of the study ##### Variables * Row: Location ID * Treatment: Tree-level treatment (Exclusion/Open) * Shell_: Column break * S.width_mm: Shell width (mm) * S.length_mm: Shell length (mm) * S.thickness_mm: Shell thickness (mm) * S.vol_mm3: Shell width x length x thicknesss (mm3) * S.mass_g: Shell weight (g) * Kernel_: Column break * K.width_mm: Kernel width (mm) * K.length_mm: Kernel length (mm) * K.thickness_mm: Kernel thickness (mm) * K.vol_mm3: Kernel width x length x thickness (mm3) * K.mass_g: Kernel weight (grams) * K.shape: Kernel shape score * K.color: Kernel color score * K.smoothness: Kernel smoothness score * K.num_twins: count, number of double kernels * Defects_: Column break * shrivel: count * chipped: count * double: count ## Code/software Data for study objectives 1 and 2 were analyzed using the R statistical software (version 4.2.2; R Core Team, 2022) using general linear models with treatment and hive density (2021 only) as explanatory variables, and tree number as a random variable (packages: ‘lme4’ (Bates et al., 2015), ‘MASS’ (Venables, 2002). We used the ‘pscl’ (Zeileis et al., 2008) package to fit a zero-inflated model for ‘doubles’, which were zero-inflated. In addition, we utilized non-parametric and post-hoc tests (package: ‘dunn.test’; Dinno, 2017) to make specific treatment-level comparisons since these data did not consistently meet the assumptions of normality between time periods.