Climate and rainfed wheat yield
Planning for precision agriculture requires a better understanding of the plant’s response to climate. The economy of Qorveh, in Iran, is severely affected by wheat yield fluctuations. In this study, multivariate statistical methods were used to identify important climatic factors affecting rainfed...
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description | Planning for precision agriculture requires a better understanding of the plant’s response to climate. The economy of Qorveh, in Iran, is severely affected by wheat yield fluctuations. In this study, multivariate statistical methods were used to identify important climatic factors affecting rainfed wheat yield and to simulate yield variations based on these impact factors. A new method was introduced to initiate seed germination. After determining the germination time, the wheat growth period was divided into seven stages based on the growing degree day (GDD). Forty-four climatic variables and indices related to the first six stages were used to perform factor analysis and to develop a model for predicting pre-harvest yield. The results showed that 91.5% of the total variance of 44 variables can be explained by 9 factors. Eighty-five percent of yield variations can be explained and modeled (
R
= 0.92) using five of these factors. This indicates that rainfed wheat yield is highly correlated with climate conditions, and this relationship is well simulated by statistical methods. According to the results, the significant trend of climatic variables was identified as the main reason for the yield growth trend in Qorveh. The yield showed a direct relationship with precipitation and relative humidity and an inverse relationship with air temperature and sunshine. The impact intensity of variables on yield included precipitation, relative humidity, sunshine, and air temperature, respectively. The results also showed that the yield was more affected by climatic variables of spring and May than other seasons and months, respectively. |
doi_str_mv | 10.1007/s00704-020-03478-9 |
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R
= 0.92) using five of these factors. This indicates that rainfed wheat yield is highly correlated with climate conditions, and this relationship is well simulated by statistical methods. According to the results, the significant trend of climatic variables was identified as the main reason for the yield growth trend in Qorveh. The yield showed a direct relationship with precipitation and relative humidity and an inverse relationship with air temperature and sunshine. The impact intensity of variables on yield included precipitation, relative humidity, sunshine, and air temperature, respectively. The results also showed that the yield was more affected by climatic variables of spring and May than other seasons and months, respectively.</description><identifier>ISSN: 0177-798X</identifier><identifier>EISSN: 1434-4483</identifier><identifier>DOI: 10.1007/s00704-020-03478-9</identifier><language>eng</language><publisher>Vienna: Springer Vienna</publisher><subject>Agriculture ; Air temperature ; Analysis ; Aquatic Pollution ; Atmospheric Protection/Air Quality Control/Air Pollution ; Atmospheric Sciences ; Climate ; Climate change ; Climate science ; Climatic conditions ; Climatic indexes ; Climatology ; Crop yield ; Crop yields ; Earth and Environmental Science ; Earth Sciences ; Factor analysis ; Germination ; heat sums ; Humidity ; Iran ; Original Paper ; Precipitation ; precision agriculture ; Precision farming ; Relative humidity ; Seed germination ; solar radiation ; spring ; Statistical methods ; Statistics ; Sunlight ; variance ; Waste Water Technology ; Water Management ; Water Pollution Control ; Wheat ; Wheat yield</subject><ispartof>Theoretical and applied climatology, 2021-04, Vol.144 (1-2), p.13-24</ispartof><rights>Springer-Verlag GmbH Austria, part of Springer Nature 2021</rights><rights>COPYRIGHT 2021 Springer</rights><rights>Springer-Verlag GmbH Austria, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c425t-863f414a4b01c45aee50de0cca083b4f5f4b6c750d3959e2bd12d5cf02fccd8e3</citedby><cites>FETCH-LOGICAL-c425t-863f414a4b01c45aee50de0cca083b4f5f4b6c750d3959e2bd12d5cf02fccd8e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00704-020-03478-9$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00704-020-03478-9$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,778,782,27907,27908,41471,42540,51302</link.rule.ids></links><search><creatorcontrib>Faghih, Homayoun</creatorcontrib><creatorcontrib>Behmanesh, Javad</creatorcontrib><creatorcontrib>Rezaie, Hossein</creatorcontrib><creatorcontrib>Khalili, Keivan</creatorcontrib><title>Climate and rainfed wheat yield</title><title>Theoretical and applied climatology</title><addtitle>Theor Appl Climatol</addtitle><description>Planning for precision agriculture requires a better understanding of the plant’s response to climate. The economy of Qorveh, in Iran, is severely affected by wheat yield fluctuations. In this study, multivariate statistical methods were used to identify important climatic factors affecting rainfed wheat yield and to simulate yield variations based on these impact factors. A new method was introduced to initiate seed germination. After determining the germination time, the wheat growth period was divided into seven stages based on the growing degree day (GDD). Forty-four climatic variables and indices related to the first six stages were used to perform factor analysis and to develop a model for predicting pre-harvest yield. The results showed that 91.5% of the total variance of 44 variables can be explained by 9 factors. Eighty-five percent of yield variations can be explained and modeled (
R
= 0.92) using five of these factors. This indicates that rainfed wheat yield is highly correlated with climate conditions, and this relationship is well simulated by statistical methods. According to the results, the significant trend of climatic variables was identified as the main reason for the yield growth trend in Qorveh. The yield showed a direct relationship with precipitation and relative humidity and an inverse relationship with air temperature and sunshine. The impact intensity of variables on yield included precipitation, relative humidity, sunshine, and air temperature, respectively. The results also showed that the yield was more affected by climatic variables of spring and May than other seasons and months, respectively.</description><subject>Agriculture</subject><subject>Air temperature</subject><subject>Analysis</subject><subject>Aquatic Pollution</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Atmospheric Sciences</subject><subject>Climate</subject><subject>Climate change</subject><subject>Climate science</subject><subject>Climatic conditions</subject><subject>Climatic indexes</subject><subject>Climatology</subject><subject>Crop yield</subject><subject>Crop yields</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Factor analysis</subject><subject>Germination</subject><subject>heat sums</subject><subject>Humidity</subject><subject>Iran</subject><subject>Original Paper</subject><subject>Precipitation</subject><subject>precision agriculture</subject><subject>Precision 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a better understanding of the plant’s response to climate. The economy of Qorveh, in Iran, is severely affected by wheat yield fluctuations. In this study, multivariate statistical methods were used to identify important climatic factors affecting rainfed wheat yield and to simulate yield variations based on these impact factors. A new method was introduced to initiate seed germination. After determining the germination time, the wheat growth period was divided into seven stages based on the growing degree day (GDD). Forty-four climatic variables and indices related to the first six stages were used to perform factor analysis and to develop a model for predicting pre-harvest yield. The results showed that 91.5% of the total variance of 44 variables can be explained by 9 factors. Eighty-five percent of yield variations can be explained and modeled (
R
= 0.92) using five of these factors. This indicates that rainfed wheat yield is highly correlated with climate conditions, and this relationship is well simulated by statistical methods. According to the results, the significant trend of climatic variables was identified as the main reason for the yield growth trend in Qorveh. The yield showed a direct relationship with precipitation and relative humidity and an inverse relationship with air temperature and sunshine. The impact intensity of variables on yield included precipitation, relative humidity, sunshine, and air temperature, respectively. The results also showed that the yield was more affected by climatic variables of spring and May than other seasons and months, respectively.</abstract><cop>Vienna</cop><pub>Springer Vienna</pub><doi>10.1007/s00704-020-03478-9</doi><tpages>12</tpages></addata></record> |
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subjects | Agriculture Air temperature Analysis Aquatic Pollution Atmospheric Protection/Air Quality Control/Air Pollution Atmospheric Sciences Climate Climate change Climate science Climatic conditions Climatic indexes Climatology Crop yield Crop yields Earth and Environmental Science Earth Sciences Factor analysis Germination heat sums Humidity Iran Original Paper Precipitation precision agriculture Precision farming Relative humidity Seed germination solar radiation spring Statistical methods Statistics Sunlight variance Waste Water Technology Water Management Water Pollution Control Wheat Wheat yield |
title | Climate and rainfed wheat yield |
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