AI Summary & Key Takeaways
AI InsightComparison of 3 models (multiple linear regression, principal component regression, and multivariate adaptive regression splines) showed that multivariate adaptive regression splines provided the best performance in modeling soil CO2 emissions, explaining overall variation of 64%, 56%, 76%, and 53% in CO2-AVG for the first, second, third, and all three 3 periods, respectively.
📌 3 Key Takeaways
Abstract
Broad interest in reducing greenhouse gas emissions requires a better understanding of controls on carbon dioxide (CO2) release under different agricultural management practices. The objective of this study was to investigate and model seasonal variation of soil CO2 emissions from an apple orchard field (Malus domestica L. 'Starkrimson'). Soil CO2 emissions from an apple orchard managed with common practices were measured weekly over a 3-year period (May 2008 to May 2011) from both under the crowns of trees (CO2-UC) and between rows (CO2-BR) using a soda lime technique and were modeled using available environmental data. The study area is located in the Harran Plain of southeastern Turkey and has a semiarid climate. The weekly soil CO2 emissions ranged from 87.8 to 1428 kg week-1 ha-1, from 74.6 to 835 kg week-1 ha-1, and from 88.6 to 1087 kg week-1 ha-1 for CO2-UC, CO2-BR, and the average of both (CO2-AVG), respectively, and showed a pronounced seasonal pattern with the lowest emissions in winter (January and February) and the highest emissions during the growing season (April to December). Relative to 2008 emissions, 2009 CO2 emissions increased by approximately 75%, and 2010 emissions increased by approximately 88%. Comparison of 3 models (multiple linear regression, principal component regression, and multivariate adaptive regression splines) showed that multivariate adaptive regression splines provided the best performance in modeling soil CO2 emissions, explaining overall variation of 64%, 56%, 76%, and 53% in CO2-AVG for the first, second, third, and all three 3 periods, respectively. In conclusion, overall findings showed that soil CO2 emissions could be modeled by available environmental data such as air and soil temperature.
Özet
Broad interest in reducing greenhouse gas emissions requires a better understanding of controls on carbon dioxide (CO2) release under different agricultural management practices. The objective of this study was to investigate and model seasonal variation of soil CO2 emissions from an apple orchard field (Malus domestica L. 'Starkrimson'). Soil CO2 emissions from an apple orchard managed with common practices were measured weekly over a 3-year period (May 2008 to May 2011) from both under the crowns of trees (CO2-UC) and between rows (CO2-BR) using a soda lime technique and were modeled using available environmental data. The study area is located in the Harran Plain of southeastern Turkey and has a semiarid climate. The weekly soil CO2 emissions ranged from 87.8 to 1428 kg week-1 ha-1, from 74.6 to 835 kg week-1 ha-1, and from 88.6 to 1087 kg week-1 ha-1 for CO2-UC, CO2-BR, and the average of both (CO2-AVG), respectively, and showed a pronounced seasonal pattern with the lowest emissions in winter (January and February) and the highest emissions during the growing season (April to December). Relative to 2008 emissions, 2009 CO2 emissions increased by approximately 75%, and 2010 emissions increased by approximately 88%. Comparison of 3 models (multiple linear regression, principal component regression, and multivariate adaptive regression splines) showed that multivariate adaptive regression splines provided the best performance in modeling soil CO2 emissions, explaining overall variation of 64%, 56%, 76%, and 53% in CO2-AVG for the first, second, third, and all three 3 periods, respectively. In conclusion, overall findings showed that soil CO2 emissions could be modeled by available environmental data such as air and soil temperature.