Data from: Comparison of yield characteristics of independence, a self-compatible almond variety, as affected by presence of honey bee pollinators
Data files
Aug 08, 2025 version files 294.57 KB
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2020_Honey_bee_visits_increase_yields_in_a_self-fertile_almond_variety_KernelQualityData.csv
135.06 KB
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2020_Honey_bee_visits_increase_yields_in_a_self-fertile_almond_variety_NutsetData.csv
5.11 KB
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2020_Honey_bee_visits_increase_yields_in_a_self-fertile_almond_variety_WholeTreeYieldsData.csv
543 B
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2021_Honey_bee_visits_increase_yields_in_a_self-fertile_almond_variety_FruitdropData.csv
16.52 KB
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2021_Honey_bee_visits_increase_yields_in_a_self-fertile_almond_variety_HoneyBeeVisitationData.csv
9.25 KB
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2021_Honey_bee_visits_increase_yields_in_a_self-fertile_almond_variety_KernelQualityData.csv
108.90 KB
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2021_Honey_bee_visits_increase_yields_in_a_self-fertile_almond_variety_NutsetData.csv
14.45 KB
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README.md
4.74 KB
Abstract
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.
Dataset DOI: 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.
