Since 2011, California has been under drought conditions. These conditions have not only affected water availability for farmers, but also production. California’s second most valuable crop, almonds, has been affected by drought conditions. This study used three models (Model 1-3) to describe almond yield variability from year to year and almond yield variability within a year in Kern County, CA. The study evaluated 185 almond farms that were classified in three locations (east side, west side and north west side). The years of the study were 2011 (wet year) and 2013-2015 (drought condition years). Model 1 determined a functional regression between almond yield and annual evapotranspiration during the 4 years of the study. The R2was 7.9%, meaning low association between both variables and high unexplained variability (92.1%). Model 2 evaluated year to year variation. A regression function between almond yield and annual evapotranspiration after adjusting for location, precipitation, chilling hours and year was made. The R2of this model 62.6%, and all the variables used had a p2was higher than Model 1; however, there was high unexplained variability (47.4%). Model 3 evaluated within-year variation. A regression function between almond yield and annual evapotranspiration after adjusting for tree age and location (east, west and northwest side) was made for each year (2011 and 2013 -2015). Coefficient of variation of evapotranspiration and soil available water storage were analyzed as additional variables in Model 3; however, they were not introduced in Model 3 due to the low increase in R2 in each year (2 of Model 3 for each year were, 60.4%, 49.7%, 53.8% and 53.2% for the years 2011, 2013-2015, respectively. Model 3 also had high unexplained almond yield variability in each year (39.6%-50.3%). This high unexplained variability leads to introduce additional variables to the functional regression model for further studies. Identifying these additional variables and having a functional regression model with high R2 would lead to understand howlow evapotranspiration could potentially lead to a positive response on yield in drought conditions; thus, making farmers improve water use efficiency and hence, lowering production cost. However, the high unexplained variability clearly indicates that evapotranspiration is only one of many factors that influence yield. If improved yield is an important outcome, future studies must examine large- scale almond-producing farms with multiple agricultural system variables.
","abstract_html":"<p>Since 2011, California has been under drought conditions. These conditions have not only affected water availability for farmers, but also production. California’s second most valuable crop, almonds, has been affected by drought conditions. This study used three models (Model 1-3) to describe almond yield variability from year to year and almond yield variability within a year in Kern County, CA. The study evaluated 185 almond farms that were classified in three locations (east side, west side and north west side). The years of the study were 2011 (wet year) and 2013-2015 (drought condition years). Model 1 determined a functional regression between almond yield and annual evapotranspiration during the 4 years of the study. The R<sup>2</sup>was 7.9%, meaning low association between both variables and high unexplained variability (92.1%). Model 2 evaluated year to year variation. A regression function between almond yield and annual evapotranspiration after adjusting for location, precipitation, chilling hours and year was made. The R<sup>2</sup>of this model 62.6%, and all the variables used had a p2was higher than Model 1; however, there was high unexplained variability (47.4%). Model 3 evaluated within-year variation. A regression function between almond yield and annual evapotranspiration after adjusting for tree age and location (east, west and northwest side) was made for each year (2011 and 2013 -2015). Coefficient of variation of evapotranspiration and soil available water storage were analyzed as additional variables in Model 3; however, they were not introduced in Model 3 due to the low increase in R<sup>2 </sup>in each year (2 of Model 3 for each year were, 60.4%, 49.7%, 53.8% and 53.2% for the years 2011, 2013-2015, respectively. Model 3 also had high unexplained almond yield variability in each year (39.6%-50.3%). This high unexplained variability leads to introduce additional variables to the functional regression model for further studies. Identifying these additional variables and having a functional regression model with high R<sup>2 </sup>would lead to understand howlow evapotranspiration could potentially lead to a positive response on yield in drought conditions; thus, making farmers improve water use efficiency and hence, lowering production cost. However, the high unexplained variability clearly indicates that evapotranspiration is only one of many factors that influence yield. If improved yield is an important outcome, future studies must examine large- scale almond-producing farms with multiple agricultural system variables.</p>","abstract_has_math":false,"creators":["Serrano, Dafne Isaac"],"institution":null,"degree_name":"MS in Agriculture - Irrigation","degree_level":null,"degree_discipline":"Bioresource and Agricultural Engineering","degree_department":null,"school":null,"contributors":["Daniel Howes"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-10-01T07:00:00Z","date_published":"2018-10-01T07:00:00Z","updated_at":"2026-07-24T01:32:08Z","subjects":["Evapotranspiration","tree age","drought condition year","wet year","coefficient of variation of evapotranspiration","chilling hours and almonds.","Agronomy and Crop Sciences","Fruit Science","Horticulture","Plant Biology","Plant Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2018.125"],"render_values":[{"text":"10.15368/theses.2018.125","href":"https://doi.org/10.15368/theses.2018.125","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/1955","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Daniel Howes"]},{"key":"dc:creator","label":"Author","values":["Serrano, Dafne Isaac"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-10-06T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Bioresource and Agricultural Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Agriculture - Irrigation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Evapotranspiration","tree age","drought condition year","wet year","coefficient of variation of evapotranspiration","chilling hours and almonds.","Agronomy and Crop Sciences","Fruit Science","Horticulture","Plant Biology","Plant Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/1955","10.15368/theses.2018.125"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Since 2011, California has been under drought conditions. These conditions have not only affected water availability for farmers, but also production. California’s second most valuable crop, almonds, has been affected by drought conditions. This study used three models (Model 1-3) to describe almond yield variability from year to year and almond yield variability within a year in Kern County, CA. The study evaluated 185 almond farms that were classified in three locations (east side, west side and north west side). The years of the study were 2011 (wet year) and 2013-2015 (drought condition years). Model 1 determined a functional regression between almond yield and annual evapotranspiration during the 4 years of the study. The R2was 7.9%, meaning low association between both variables and high unexplained variability (92.1%). Model 2 evaluated year to year variation. A regression function between almond yield and annual evapotranspiration after adjusting for location, precipitation, chilling hours and year was made. The R2of this model 62.6%, and all the variables used had a p2was higher than Model 1; however, there was high unexplained variability (47.4%). Model 3 evaluated within-year variation. A regression function between almond yield and annual evapotranspiration after adjusting for tree age and location (east, west and northwest side) was made for each year (2011 and 2013 -2015). Coefficient of variation of evapotranspiration and soil available water storage were analyzed as additional variables in Model 3; however, they were not introduced in Model 3 due to the low increase in R2 in each year (2 of Model 3 for each year were, 60.4%, 49.7%, 53.8% and 53.2% for the years 2011, 2013-2015, respectively. Model 3 also had high unexplained almond yield variability in each year (39.6%-50.3%). This high unexplained variability leads to introduce additional variables to the functional regression model for further studies. Identifying these additional variables and having a functional regression model with high R2 would lead to understand howlow evapotranspiration could potentially lead to a positive response on yield in drought conditions; thus, making farmers improve water use efficiency and hence, lowering production cost. However, the high unexplained variability clearly indicates that evapotranspiration is only one of many factors that influence yield. If improved yield is an important outcome, future studies must examine large- scale almond-producing farms with multiple agricultural system variables.
"]},{"key":"dc:title","label":"Title","values":["Effect of Evapotranspiration Rate on Almond Yield in California"]}]}],"canonical_facts":{"dc:contributor":["Daniel Howes"],"dc:creator":["Serrano, Dafne Isaac"],"dc:date.available":["2018-10-06T07:00:00Z"],"dc:description.abstract":["Since 2011, California has been under drought conditions. These conditions have not only affected water availability for farmers, but also production. California’s second most valuable crop, almonds, has been affected by drought conditions. This study used three models (Model 1-3) to describe almond yield variability from year to year and almond yield variability within a year in Kern County, CA. The study evaluated 185 almond farms that were classified in three locations (east side, west side and north west side). The years of the study were 2011 (wet year) and 2013-2015 (drought condition years). Model 1 determined a functional regression between almond yield and annual evapotranspiration during the 4 years of the study. The R2was 7.9%, meaning low association between both variables and high unexplained variability (92.1%). Model 2 evaluated year to year variation. A regression function between almond yield and annual evapotranspiration after adjusting for location, precipitation, chilling hours and year was made. The R2of this model 62.6%, and all the variables used had a p2was higher than Model 1; however, there was high unexplained variability (47.4%). Model 3 evaluated within-year variation. A regression function between almond yield and annual evapotranspiration after adjusting for tree age and location (east, west and northwest side) was made for each year (2011 and 2013 -2015). Coefficient of variation of evapotranspiration and soil available water storage were analyzed as additional variables in Model 3; however, they were not introduced in Model 3 due to the low increase in R2 in each year (2 of Model 3 for each year were, 60.4%, 49.7%, 53.8% and 53.2% for the years 2011, 2013-2015, respectively. Model 3 also had high unexplained almond yield variability in each year (39.6%-50.3%). This high unexplained variability leads to introduce additional variables to the functional regression model for further studies. Identifying these additional variables and having a functional regression model with high R2 would lead to understand howlow evapotranspiration could potentially lead to a positive response on yield in drought conditions; thus, making farmers improve water use efficiency and hence, lowering production cost. However, the high unexplained variability clearly indicates that evapotranspiration is only one of many factors that influence yield. If improved yield is an important outcome, future studies must examine large- scale almond-producing farms with multiple agricultural system variables.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/1955","10.15368/theses.2018.125"],"dc:subject":["Evapotranspiration","tree age","drought condition year","wet year","coefficient of variation of evapotranspiration","chilling hours and almonds.","Agronomy and Crop Sciences","Fruit Science","Horticulture","Plant Biology","Plant Sciences"],"dc:title":["Effect of Evapotranspiration Rate on Almond Yield in California"],"thesis:degree_discipline":["Bioresource and Agricultural Engineering"],"thesis:degree_name":["MS in Agriculture - Irrigation"]},"updated_at":"2026-07-24T01:32:08Z"}