Ferdowsi University of MashhadIranian Journal of Field Crops Research2008-147216420181222Yield Monitoring for Wheat and Sugar beet in Khorasan Province: 1- Analysis of Methods for Estimating Potential YieldYield Monitoring for Wheat and Sugar beet in Khorasan Province: 1- Analysis of Methods for Estimating Potential Yield7237413817910.22067/gsc.v16i4.62556FAM Nasiri Mahalati0000-0003-0357-1733A KoochekiJournal Article20170212Introduction
Potential production condition is defined as situation where water and nutrients are supplied in ample with no damage from weeds, pests and diseases. Under this situation potential yield (Yp) which is defined by solar radiation, temperature, CO2 concentration and genotypic characteristics of the crop species could be achieved. Closing the gap between Yp and actual yield (Ya) harvested by farmers is considered as the main challenge of agronomic sciences all around the world. Crop simulation models provide a powerful tool for prediction of Yp at regional and national scales. However, the accuracy of these models is highly dependent on the quality of input data which are usually not fully available even in developed countries. Simplified models designed based on few simple equations may be considered as an alternative where accurate data sets are lacking. In this research, Yp of wheat and sugar beet were estimated using some simple methods and the results were compared with those of complex simulation models under the same climatic and management conditions in Khorasan province, north east of Iran.
Materials and Methods
Potential yields of wheat and sugar beet were estimated by three simple calculation methods including FAO method (FAO, 1981), modified FAO, FAO-M (Versteeg and van Keulen, 1986) and RUE-based method (Nonhebel, 1997) at three different regions (Torbat, Mashhad and Neishabor) in Khorasan province. In these methods total above ground dry matter (DM) is calculated using two equations and Yp is estimated as the product of DM and harvest index with minimum weather data requirements. In addition, Yp for both crops and at the same locations was predicted using LINTUL and SUCROS simulation models which were previously calibrated for yield estimation of wheat and sugar beet at regional level. The accuracy of predicted potential yields by five methods was compared statistically against the measured yields obtained from the field experiments and high yielding farms at the studied regions.
Results and Discussion
Mean observed yield of wheat over the three studied regions was 7.18 t ha-1 and the estimated potential yield in the same regions using two simulation models and three simplified models was ranged between 6.92 and 7.63 t ha-1. Prediction error for the simulation models was 1.39 and for simple methods less than 5% (4.64%). Relative root mean squared error (RMSEn) for the simple methods was higher than that of complex models. However, it was between 7.11 and 10.16 % of the mean measured wheat yield which could be statistically considered as reasonable. Calculated values of modeling efficiency (ME) were positive and higher than 0.60 except for FAO-M method (ME=0.48). Measured sugar beet yield averaged over regions was 82.5 t ha-1 and estimated potential yield by different methods was between 89 and 91 t ha-1. Simple calculation methods had lower accuracy in predicting sugar beet yield compared to simulation models but RMSEnof simple methods never exceeded 11.5% showing statistically acceptable prediction. Cross validation indicated significant correlation between the results of three simple methods and that of complex simulation models.
Conclusions
Results of this study showed that potential yields of different crops can be estimated accurately using simple calculation methods with minimum weather data requirements. Such methods may be used as an alternative for agroecological zoning and yield gap analysis at regional level where calibrated simulation models and complete daily weather data sets are not available.Introduction
Potential production condition is defined as situation where water and nutrients are supplied in ample with no damage from weeds, pests and diseases. Under this situation potential yield (Yp) which is defined by solar radiation, temperature, CO2 concentration and genotypic characteristics of the crop species could be achieved. Closing the gap between Yp and actual yield (Ya) harvested by farmers is considered as the main challenge of agronomic sciences all around the world. Crop simulation models provide a powerful tool for prediction of Yp at regional and national scales. However, the accuracy of these models is highly dependent on the quality of input data which are usually not fully available even in developed countries. Simplified models designed based on few simple equations may be considered as an alternative where accurate data sets are lacking. In this research, Yp of wheat and sugar beet were estimated using some simple methods and the results were compared with those of complex simulation models under the same climatic and management conditions in Khorasan province, north east of Iran.
Materials and Methods
Potential yields of wheat and sugar beet were estimated by three simple calculation methods including FAO method (FAO, 1981), modified FAO, FAO-M (Versteeg and van Keulen, 1986) and RUE-based method (Nonhebel, 1997) at three different regions (Torbat, Mashhad and Neishabor) in Khorasan province. In these methods total above ground dry matter (DM) is calculated using two equations and Yp is estimated as the product of DM and harvest index with minimum weather data requirements. In addition, Yp for both crops and at the same locations was predicted using LINTUL and SUCROS simulation models which were previously calibrated for yield estimation of wheat and sugar beet at regional level. The accuracy of predicted potential yields by five methods was compared statistically against the measured yields obtained from the field experiments and high yielding farms at the studied regions.
Results and Discussion
Mean observed yield of wheat over the three studied regions was 7.18 t ha-1 and the estimated potential yield in the same regions using two simulation models and three simplified models was ranged between 6.92 and 7.63 t ha-1. Prediction error for the simulation models was 1.39 and for simple methods less than 5% (4.64%). Relative root mean squared error (RMSEn) for the simple methods was higher than that of complex models. However, it was between 7.11 and 10.16 % of the mean measured wheat yield which could be statistically considered as reasonable. Calculated values of modeling efficiency (ME) were positive and higher than 0.60 except for FAO-M method (ME=0.48). Measured sugar beet yield averaged over regions was 82.5 t ha-1 and estimated potential yield by different methods was between 89 and 91 t ha-1. Simple calculation methods had lower accuracy in predicting sugar beet yield compared to simulation models but RMSEnof simple methods never exceeded 11.5% showing statistically acceptable prediction. Cross validation indicated significant correlation between the results of three simple methods and that of complex simulation models.
Conclusions
Results of this study showed that potential yields of different crops can be estimated accurately using simple calculation methods with minimum weather data requirements. Such methods may be used as an alternative for agroecological zoning and yield gap analysis at regional level where calibrated simulation models and complete daily weather data sets are not available.https://jcesc.um.ac.ir/article_38179_bff3c2bee2df020d968a484a1967564e.pdf