Evaluation of CROPWAT 8.0 model in predicting the yield of East Africa highland banana under different sets of irrigation scheduling
Simulation models based on plant physiology are used to predict growth and yield of crops. Such models are important because they can be used to pre-evaluate treatments, thus, improving the effectivity of agricultural research and reducing the cost of field experiments. For efficiency purpose, crop...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Olivier Ndayitegeye [verfasserIn] Japheth Ogalo Onyando [verfasserIn] Romulus Okoth Okwany [verfasserIn] Johnson Kisera Kwach [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: Fundamental and Applied Agriculture - Farm to Fork Foundation, 2018, 5(2020), 3, Seite 344-352 |
|---|---|
| Übergeordnetes Werk: |
volume:5 ; year:2020 ; number:3 ; pages:344-352 |
| Links: |
|---|
| DOI / URN: |
10.5455/faa.93386 |
|---|
| Katalog-ID: |
DOAJ050292188 |
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| 520 | |a Simulation models based on plant physiology are used to predict growth and yield of crops. Such models are important because they can be used to pre-evaluate treatments, thus, improving the effectivity of agricultural research and reducing the cost of field experiments. For efficiency purpose, crop models need to be calibrated and validated before using them. The objective of this study was to evaluate the performance of an existing crop model in predicting the yield of East Africa Highland Banana (EAHB) under deficit irrigation and different irrigation intervals. The model, CROPWAT 8.0, was calibrated, evaluated and applied for banana crop water requirements and estimation of EAHB yield. For calibration of CROPWAT 8.0, monthly climatic data (temperature, relative humidity, wind speed, sunshine hours and rainfall), crop and soil data are were used. Climatic data were provided by the New_LocClim software which is the local climate estimator of FAO, effective rain was set to zero because the experiment was conducted under a rain shelter. Three irrigation levels (IL) (80%, 90% and 100% of Evapotranspiration) were combined with three levels of irrigation intervals (D) (4, 6 and 8 days in a randomized complete block design (RCBD) with three replications. To evaluate the model for yield estimation, the observed yield was compared with the corresponding simulated values by CROPWAT 8.0 using mean squared deviation (MSD), Nash and Sutcliffe model efficiency (NSE), coefficient of determination (R2) and paired t-test. The predicted banana yield (39.1 ± 2.66 t ha-1) from the calibrated model was very close to the observed yield (38.4 ± 2.37 t ha-1 (p≥0.05, R2 = 0.82 and an NSE of 0.81. MSD analysis showed that the models prediction was more accurate at 8 or 6 days irrigation intervals than 4 days irrigation interval. The calibrated CROPWAT 8.0 model can be used efficiently to predict the yield of East Africa Highland Banana. [Fundam Appl Agric 2020; 5(3.000): 344-352] | ||
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10.5455/faa.93386 doi (DE-627)DOAJ050292188 (DE-599)DOAJb024cd26540d41eb98344c49a47d3a62 DE-627 ger DE-627 rakwb eng Olivier Ndayitegeye verfasserin aut Evaluation of CROPWAT 8.0 model in predicting the yield of East Africa highland banana under different sets of irrigation scheduling 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Simulation models based on plant physiology are used to predict growth and yield of crops. Such models are important because they can be used to pre-evaluate treatments, thus, improving the effectivity of agricultural research and reducing the cost of field experiments. For efficiency purpose, crop models need to be calibrated and validated before using them. The objective of this study was to evaluate the performance of an existing crop model in predicting the yield of East Africa Highland Banana (EAHB) under deficit irrigation and different irrigation intervals. The model, CROPWAT 8.0, was calibrated, evaluated and applied for banana crop water requirements and estimation of EAHB yield. For calibration of CROPWAT 8.0, monthly climatic data (temperature, relative humidity, wind speed, sunshine hours and rainfall), crop and soil data are were used. Climatic data were provided by the New_LocClim software which is the local climate estimator of FAO, effective rain was set to zero because the experiment was conducted under a rain shelter. Three irrigation levels (IL) (80%, 90% and 100% of Evapotranspiration) were combined with three levels of irrigation intervals (D) (4, 6 and 8 days in a randomized complete block design (RCBD) with three replications. To evaluate the model for yield estimation, the observed yield was compared with the corresponding simulated values by CROPWAT 8.0 using mean squared deviation (MSD), Nash and Sutcliffe model efficiency (NSE), coefficient of determination (R2) and paired t-test. The predicted banana yield (39.1 ± 2.66 t ha-1) from the calibrated model was very close to the observed yield (38.4 ± 2.37 t ha-1 (p≥0.05, R2 = 0.82 and an NSE of 0.81. MSD analysis showed that the models prediction was more accurate at 8 or 6 days irrigation intervals than 4 days irrigation interval. The calibrated CROPWAT 8.0 model can be used efficiently to predict the yield of East Africa Highland Banana. [Fundam Appl Agric 2020; 5(3.000): 344-352] cropwat 8.0 banana yield deficit irrigation irrigation interval Agriculture S Japheth Ogalo Onyando verfasserin aut Romulus Okoth Okwany verfasserin aut Johnson Kisera Kwach verfasserin aut In Fundamental and Applied Agriculture Farm to Fork Foundation, 2018 5(2020), 3, Seite 344-352 (DE-627)1024600866 24154474 nnns volume:5 year:2020 number:3 pages:344-352 https://doi.org/10.5455/faa.93386 kostenfrei https://doaj.org/article/b024cd26540d41eb98344c49a47d3a62 kostenfrei http://www.ejmanager.com/fulltextpdf.php?mno=93386 kostenfrei https://doaj.org/toc/2518-2021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 5 2020 3 344-352 |
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10.5455/faa.93386 doi (DE-627)DOAJ050292188 (DE-599)DOAJb024cd26540d41eb98344c49a47d3a62 DE-627 ger DE-627 rakwb eng Olivier Ndayitegeye verfasserin aut Evaluation of CROPWAT 8.0 model in predicting the yield of East Africa highland banana under different sets of irrigation scheduling 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Simulation models based on plant physiology are used to predict growth and yield of crops. Such models are important because they can be used to pre-evaluate treatments, thus, improving the effectivity of agricultural research and reducing the cost of field experiments. For efficiency purpose, crop models need to be calibrated and validated before using them. The objective of this study was to evaluate the performance of an existing crop model in predicting the yield of East Africa Highland Banana (EAHB) under deficit irrigation and different irrigation intervals. The model, CROPWAT 8.0, was calibrated, evaluated and applied for banana crop water requirements and estimation of EAHB yield. For calibration of CROPWAT 8.0, monthly climatic data (temperature, relative humidity, wind speed, sunshine hours and rainfall), crop and soil data are were used. Climatic data were provided by the New_LocClim software which is the local climate estimator of FAO, effective rain was set to zero because the experiment was conducted under a rain shelter. Three irrigation levels (IL) (80%, 90% and 100% of Evapotranspiration) were combined with three levels of irrigation intervals (D) (4, 6 and 8 days in a randomized complete block design (RCBD) with three replications. To evaluate the model for yield estimation, the observed yield was compared with the corresponding simulated values by CROPWAT 8.0 using mean squared deviation (MSD), Nash and Sutcliffe model efficiency (NSE), coefficient of determination (R2) and paired t-test. The predicted banana yield (39.1 ± 2.66 t ha-1) from the calibrated model was very close to the observed yield (38.4 ± 2.37 t ha-1 (p≥0.05, R2 = 0.82 and an NSE of 0.81. MSD analysis showed that the models prediction was more accurate at 8 or 6 days irrigation intervals than 4 days irrigation interval. The calibrated CROPWAT 8.0 model can be used efficiently to predict the yield of East Africa Highland Banana. [Fundam Appl Agric 2020; 5(3.000): 344-352] cropwat 8.0 banana yield deficit irrigation irrigation interval Agriculture S Japheth Ogalo Onyando verfasserin aut Romulus Okoth Okwany verfasserin aut Johnson Kisera Kwach verfasserin aut In Fundamental and Applied Agriculture Farm to Fork Foundation, 2018 5(2020), 3, Seite 344-352 (DE-627)1024600866 24154474 nnns volume:5 year:2020 number:3 pages:344-352 https://doi.org/10.5455/faa.93386 kostenfrei https://doaj.org/article/b024cd26540d41eb98344c49a47d3a62 kostenfrei http://www.ejmanager.com/fulltextpdf.php?mno=93386 kostenfrei https://doaj.org/toc/2518-2021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 5 2020 3 344-352 |
| allfields_unstemmed |
10.5455/faa.93386 doi (DE-627)DOAJ050292188 (DE-599)DOAJb024cd26540d41eb98344c49a47d3a62 DE-627 ger DE-627 rakwb eng Olivier Ndayitegeye verfasserin aut Evaluation of CROPWAT 8.0 model in predicting the yield of East Africa highland banana under different sets of irrigation scheduling 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Simulation models based on plant physiology are used to predict growth and yield of crops. Such models are important because they can be used to pre-evaluate treatments, thus, improving the effectivity of agricultural research and reducing the cost of field experiments. For efficiency purpose, crop models need to be calibrated and validated before using them. The objective of this study was to evaluate the performance of an existing crop model in predicting the yield of East Africa Highland Banana (EAHB) under deficit irrigation and different irrigation intervals. The model, CROPWAT 8.0, was calibrated, evaluated and applied for banana crop water requirements and estimation of EAHB yield. For calibration of CROPWAT 8.0, monthly climatic data (temperature, relative humidity, wind speed, sunshine hours and rainfall), crop and soil data are were used. Climatic data were provided by the New_LocClim software which is the local climate estimator of FAO, effective rain was set to zero because the experiment was conducted under a rain shelter. Three irrigation levels (IL) (80%, 90% and 100% of Evapotranspiration) were combined with three levels of irrigation intervals (D) (4, 6 and 8 days in a randomized complete block design (RCBD) with three replications. To evaluate the model for yield estimation, the observed yield was compared with the corresponding simulated values by CROPWAT 8.0 using mean squared deviation (MSD), Nash and Sutcliffe model efficiency (NSE), coefficient of determination (R2) and paired t-test. The predicted banana yield (39.1 ± 2.66 t ha-1) from the calibrated model was very close to the observed yield (38.4 ± 2.37 t ha-1 (p≥0.05, R2 = 0.82 and an NSE of 0.81. MSD analysis showed that the models prediction was more accurate at 8 or 6 days irrigation intervals than 4 days irrigation interval. The calibrated CROPWAT 8.0 model can be used efficiently to predict the yield of East Africa Highland Banana. [Fundam Appl Agric 2020; 5(3.000): 344-352] cropwat 8.0 banana yield deficit irrigation irrigation interval Agriculture S Japheth Ogalo Onyando verfasserin aut Romulus Okoth Okwany verfasserin aut Johnson Kisera Kwach verfasserin aut In Fundamental and Applied Agriculture Farm to Fork Foundation, 2018 5(2020), 3, Seite 344-352 (DE-627)1024600866 24154474 nnns volume:5 year:2020 number:3 pages:344-352 https://doi.org/10.5455/faa.93386 kostenfrei https://doaj.org/article/b024cd26540d41eb98344c49a47d3a62 kostenfrei http://www.ejmanager.com/fulltextpdf.php?mno=93386 kostenfrei https://doaj.org/toc/2518-2021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 5 2020 3 344-352 |
| allfieldsGer |
10.5455/faa.93386 doi (DE-627)DOAJ050292188 (DE-599)DOAJb024cd26540d41eb98344c49a47d3a62 DE-627 ger DE-627 rakwb eng Olivier Ndayitegeye verfasserin aut Evaluation of CROPWAT 8.0 model in predicting the yield of East Africa highland banana under different sets of irrigation scheduling 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Simulation models based on plant physiology are used to predict growth and yield of crops. Such models are important because they can be used to pre-evaluate treatments, thus, improving the effectivity of agricultural research and reducing the cost of field experiments. For efficiency purpose, crop models need to be calibrated and validated before using them. The objective of this study was to evaluate the performance of an existing crop model in predicting the yield of East Africa Highland Banana (EAHB) under deficit irrigation and different irrigation intervals. The model, CROPWAT 8.0, was calibrated, evaluated and applied for banana crop water requirements and estimation of EAHB yield. For calibration of CROPWAT 8.0, monthly climatic data (temperature, relative humidity, wind speed, sunshine hours and rainfall), crop and soil data are were used. Climatic data were provided by the New_LocClim software which is the local climate estimator of FAO, effective rain was set to zero because the experiment was conducted under a rain shelter. Three irrigation levels (IL) (80%, 90% and 100% of Evapotranspiration) were combined with three levels of irrigation intervals (D) (4, 6 and 8 days in a randomized complete block design (RCBD) with three replications. To evaluate the model for yield estimation, the observed yield was compared with the corresponding simulated values by CROPWAT 8.0 using mean squared deviation (MSD), Nash and Sutcliffe model efficiency (NSE), coefficient of determination (R2) and paired t-test. The predicted banana yield (39.1 ± 2.66 t ha-1) from the calibrated model was very close to the observed yield (38.4 ± 2.37 t ha-1 (p≥0.05, R2 = 0.82 and an NSE of 0.81. MSD analysis showed that the models prediction was more accurate at 8 or 6 days irrigation intervals than 4 days irrigation interval. The calibrated CROPWAT 8.0 model can be used efficiently to predict the yield of East Africa Highland Banana. [Fundam Appl Agric 2020; 5(3.000): 344-352] cropwat 8.0 banana yield deficit irrigation irrigation interval Agriculture S Japheth Ogalo Onyando verfasserin aut Romulus Okoth Okwany verfasserin aut Johnson Kisera Kwach verfasserin aut In Fundamental and Applied Agriculture Farm to Fork Foundation, 2018 5(2020), 3, Seite 344-352 (DE-627)1024600866 24154474 nnns volume:5 year:2020 number:3 pages:344-352 https://doi.org/10.5455/faa.93386 kostenfrei https://doaj.org/article/b024cd26540d41eb98344c49a47d3a62 kostenfrei http://www.ejmanager.com/fulltextpdf.php?mno=93386 kostenfrei https://doaj.org/toc/2518-2021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 5 2020 3 344-352 |
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10.5455/faa.93386 doi (DE-627)DOAJ050292188 (DE-599)DOAJb024cd26540d41eb98344c49a47d3a62 DE-627 ger DE-627 rakwb eng Olivier Ndayitegeye verfasserin aut Evaluation of CROPWAT 8.0 model in predicting the yield of East Africa highland banana under different sets of irrigation scheduling 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Simulation models based on plant physiology are used to predict growth and yield of crops. Such models are important because they can be used to pre-evaluate treatments, thus, improving the effectivity of agricultural research and reducing the cost of field experiments. For efficiency purpose, crop models need to be calibrated and validated before using them. The objective of this study was to evaluate the performance of an existing crop model in predicting the yield of East Africa Highland Banana (EAHB) under deficit irrigation and different irrigation intervals. The model, CROPWAT 8.0, was calibrated, evaluated and applied for banana crop water requirements and estimation of EAHB yield. For calibration of CROPWAT 8.0, monthly climatic data (temperature, relative humidity, wind speed, sunshine hours and rainfall), crop and soil data are were used. Climatic data were provided by the New_LocClim software which is the local climate estimator of FAO, effective rain was set to zero because the experiment was conducted under a rain shelter. Three irrigation levels (IL) (80%, 90% and 100% of Evapotranspiration) were combined with three levels of irrigation intervals (D) (4, 6 and 8 days in a randomized complete block design (RCBD) with three replications. To evaluate the model for yield estimation, the observed yield was compared with the corresponding simulated values by CROPWAT 8.0 using mean squared deviation (MSD), Nash and Sutcliffe model efficiency (NSE), coefficient of determination (R2) and paired t-test. The predicted banana yield (39.1 ± 2.66 t ha-1) from the calibrated model was very close to the observed yield (38.4 ± 2.37 t ha-1 (p≥0.05, R2 = 0.82 and an NSE of 0.81. MSD analysis showed that the models prediction was more accurate at 8 or 6 days irrigation intervals than 4 days irrigation interval. The calibrated CROPWAT 8.0 model can be used efficiently to predict the yield of East Africa Highland Banana. [Fundam Appl Agric 2020; 5(3.000): 344-352] cropwat 8.0 banana yield deficit irrigation irrigation interval Agriculture S Japheth Ogalo Onyando verfasserin aut Romulus Okoth Okwany verfasserin aut Johnson Kisera Kwach verfasserin aut In Fundamental and Applied Agriculture Farm to Fork Foundation, 2018 5(2020), 3, Seite 344-352 (DE-627)1024600866 24154474 nnns volume:5 year:2020 number:3 pages:344-352 https://doi.org/10.5455/faa.93386 kostenfrei https://doaj.org/article/b024cd26540d41eb98344c49a47d3a62 kostenfrei http://www.ejmanager.com/fulltextpdf.php?mno=93386 kostenfrei https://doaj.org/toc/2518-2021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4367 GBV_ILN_4700 AR 5 2020 3 344-352 |
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Evaluation of CROPWAT 8.0 model in predicting the yield of East Africa highland banana under different sets of irrigation scheduling |
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Simulation models based on plant physiology are used to predict growth and yield of crops. Such models are important because they can be used to pre-evaluate treatments, thus, improving the effectivity of agricultural research and reducing the cost of field experiments. For efficiency purpose, crop models need to be calibrated and validated before using them. The objective of this study was to evaluate the performance of an existing crop model in predicting the yield of East Africa Highland Banana (EAHB) under deficit irrigation and different irrigation intervals. The model, CROPWAT 8.0, was calibrated, evaluated and applied for banana crop water requirements and estimation of EAHB yield. For calibration of CROPWAT 8.0, monthly climatic data (temperature, relative humidity, wind speed, sunshine hours and rainfall), crop and soil data are were used. Climatic data were provided by the New_LocClim software which is the local climate estimator of FAO, effective rain was set to zero because the experiment was conducted under a rain shelter. Three irrigation levels (IL) (80%, 90% and 100% of Evapotranspiration) were combined with three levels of irrigation intervals (D) (4, 6 and 8 days in a randomized complete block design (RCBD) with three replications. To evaluate the model for yield estimation, the observed yield was compared with the corresponding simulated values by CROPWAT 8.0 using mean squared deviation (MSD), Nash and Sutcliffe model efficiency (NSE), coefficient of determination (R2) and paired t-test. The predicted banana yield (39.1 ± 2.66 t ha-1) from the calibrated model was very close to the observed yield (38.4 ± 2.37 t ha-1 (p≥0.05, R2 = 0.82 and an NSE of 0.81. MSD analysis showed that the models prediction was more accurate at 8 or 6 days irrigation intervals than 4 days irrigation interval. The calibrated CROPWAT 8.0 model can be used efficiently to predict the yield of East Africa Highland Banana. [Fundam Appl Agric 2020; 5(3.000): 344-352] |
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Simulation models based on plant physiology are used to predict growth and yield of crops. Such models are important because they can be used to pre-evaluate treatments, thus, improving the effectivity of agricultural research and reducing the cost of field experiments. For efficiency purpose, crop models need to be calibrated and validated before using them. The objective of this study was to evaluate the performance of an existing crop model in predicting the yield of East Africa Highland Banana (EAHB) under deficit irrigation and different irrigation intervals. The model, CROPWAT 8.0, was calibrated, evaluated and applied for banana crop water requirements and estimation of EAHB yield. For calibration of CROPWAT 8.0, monthly climatic data (temperature, relative humidity, wind speed, sunshine hours and rainfall), crop and soil data are were used. Climatic data were provided by the New_LocClim software which is the local climate estimator of FAO, effective rain was set to zero because the experiment was conducted under a rain shelter. Three irrigation levels (IL) (80%, 90% and 100% of Evapotranspiration) were combined with three levels of irrigation intervals (D) (4, 6 and 8 days in a randomized complete block design (RCBD) with three replications. To evaluate the model for yield estimation, the observed yield was compared with the corresponding simulated values by CROPWAT 8.0 using mean squared deviation (MSD), Nash and Sutcliffe model efficiency (NSE), coefficient of determination (R2) and paired t-test. The predicted banana yield (39.1 ± 2.66 t ha-1) from the calibrated model was very close to the observed yield (38.4 ± 2.37 t ha-1 (p≥0.05, R2 = 0.82 and an NSE of 0.81. MSD analysis showed that the models prediction was more accurate at 8 or 6 days irrigation intervals than 4 days irrigation interval. The calibrated CROPWAT 8.0 model can be used efficiently to predict the yield of East Africa Highland Banana. [Fundam Appl Agric 2020; 5(3.000): 344-352] |
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Simulation models based on plant physiology are used to predict growth and yield of crops. Such models are important because they can be used to pre-evaluate treatments, thus, improving the effectivity of agricultural research and reducing the cost of field experiments. For efficiency purpose, crop models need to be calibrated and validated before using them. The objective of this study was to evaluate the performance of an existing crop model in predicting the yield of East Africa Highland Banana (EAHB) under deficit irrigation and different irrigation intervals. The model, CROPWAT 8.0, was calibrated, evaluated and applied for banana crop water requirements and estimation of EAHB yield. For calibration of CROPWAT 8.0, monthly climatic data (temperature, relative humidity, wind speed, sunshine hours and rainfall), crop and soil data are were used. Climatic data were provided by the New_LocClim software which is the local climate estimator of FAO, effective rain was set to zero because the experiment was conducted under a rain shelter. Three irrigation levels (IL) (80%, 90% and 100% of Evapotranspiration) were combined with three levels of irrigation intervals (D) (4, 6 and 8 days in a randomized complete block design (RCBD) with three replications. To evaluate the model for yield estimation, the observed yield was compared with the corresponding simulated values by CROPWAT 8.0 using mean squared deviation (MSD), Nash and Sutcliffe model efficiency (NSE), coefficient of determination (R2) and paired t-test. The predicted banana yield (39.1 ± 2.66 t ha-1) from the calibrated model was very close to the observed yield (38.4 ± 2.37 t ha-1 (p≥0.05, R2 = 0.82 and an NSE of 0.81. MSD analysis showed that the models prediction was more accurate at 8 or 6 days irrigation intervals than 4 days irrigation interval. The calibrated CROPWAT 8.0 model can be used efficiently to predict the yield of East Africa Highland Banana. [Fundam Appl Agric 2020; 5(3.000): 344-352] |
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Such models are important because they can be used to pre-evaluate treatments, thus, improving the effectivity of agricultural research and reducing the cost of field experiments. For efficiency purpose, crop models need to be calibrated and validated before using them. The objective of this study was to evaluate the performance of an existing crop model in predicting the yield of East Africa Highland Banana (EAHB) under deficit irrigation and different irrigation intervals. The model, CROPWAT 8.0, was calibrated, evaluated and applied for banana crop water requirements and estimation of EAHB yield. For calibration of CROPWAT 8.0, monthly climatic data (temperature, relative humidity, wind speed, sunshine hours and rainfall), crop and soil data are were used. Climatic data were provided by the New_LocClim software which is the local climate estimator of FAO, effective rain was set to zero because the experiment was conducted under a rain shelter. Three irrigation levels (IL) (80%, 90% and 100% of Evapotranspiration) were combined with three levels of irrigation intervals (D) (4, 6 and 8 days in a randomized complete block design (RCBD) with three replications. To evaluate the model for yield estimation, the observed yield was compared with the corresponding simulated values by CROPWAT 8.0 using mean squared deviation (MSD), Nash and Sutcliffe model efficiency (NSE), coefficient of determination (R2) and paired t-test. The predicted banana yield (39.1 ± 2.66 t ha-1) from the calibrated model was very close to the observed yield (38.4 ± 2.37 t ha-1 (p≥0.05, R2 = 0.82 and an NSE of 0.81. MSD analysis showed that the models prediction was more accurate at 8 or 6 days irrigation intervals than 4 days irrigation interval. The calibrated CROPWAT 8.0 model can be used efficiently to predict the yield of East Africa Highland Banana. 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