Comparison of Comparative Yield and Stubble Height for Estimating Herbage Standing Crop in Annual Rangelands

Abstract

We compared calibration equations for estimating herbage standing crop (HSC) from comparative yield (CY) rank or stubble height (SH) to determine 1) if CY rank is a better estimator than SH of standing crop, 2) if addition of SH to CY rank will improve the estimation of standing crop, 3) if there is a seasonal effect on CY rank or SH, and 4) if botanical composition influences the prediction of HSC from CY. The results of this study indicate that CY is a slightly better predictor of HSC than is SH. Addition of SH to CY did not improve the prediction of HSC. Models that predict HSC from CY in summer were weaker than models for winter, early spring, and late spring. Thus the CY method can be used with confidence throughout the year. The presence of filaree (Erodium cicutarium L.) in winter and early spring resulted in steeper calibration equations than were present in nonfilaree quadrats. ResumenComparamos ecuaciones de calibració n para estimar la biomasa en pie (HSC) a partir de los método de rendimiento comparativo por clases (CY) o la altura del rastrojo (SH) para determinar: (1) si el rendimiento comparativo por clases (CY) es un mejor estimador de la biomasa que la altura del rastrojo (SH); (2) si la adició n de la altura del rastrojo SH a el rendimiento comparativo por clases mejoraría la estimació n de la biomasa y (3) si hay un efecto estacional sobre el rendimiento comparativo por clase (CY) o la altura del rastrojo (SH) y (4) si la composició n botá nica influye la predicció n de la biomasa (HSC) a partir del rendimiento comparativo por clases (CY). Los resultados de este estudio indican que CY es un estimador de la biomasa ligeramente mejor que la SH. La adició n de la SH a CY no mejoró la predicció n de HSC. Los modelos que predicen la HSC a partir de CY en verano fueron mas débiles que los modelos para invierno, inicios de primavera y finales de primavera, por lo tanto, el método de CY puede ser usado con confianza a lo largo del añ o. La presencia de ''Filaree'' (Erodium cicutarium L.) en invierno e inicio de primavera resultó en ecuaciones de calibració n mas precisas que las obtenidas de los cuadrantes sin ''Filaree.''

Training reduced subjectivity of comparative yield method of estimation of grassland biomass

“…The comparative yield method, first introduced by Haydock and Shaw (), is a technique which involves a combination of harvesting sampled plots, measuring their actual biomass, and then developing a regression equation between the biomass scored, based on simple visual assessment or comparison with the actual biomass measured. Therefore, unlike many techniques involving harvesting, which are time‐consuming and destructive, the comparative yield method involves harvesting of reference and calibration plots, which are usually only 20 small plots of size ranging 0·25–1 m 2 for developing a regression equation that relates a visually estimated biomass with an actually measured one (George et al ., ). Once the regression equations relating visual estimation of biomass with actually measured biomass are developed, the equations can be used over a longer period of time, provided there are no significant changes in management factors, such as burning, fertilization, change of grass species and varieties, all of which affect biomass distribution (Haydock and Shaw, ; George et al ., ).…”

“…Therefore, unlike many techniques involving harvesting, which are time‐consuming and destructive, the comparative yield method involves harvesting of reference and calibration plots, which are usually only 20 small plots of size ranging 0·25–1 m 2 for developing a regression equation that relates a visually estimated biomass with an actually measured one (George et al ., ). Once the regression equations relating visual estimation of biomass with actually measured biomass are developed, the equations can be used over a longer period of time, provided there are no significant changes in management factors, such as burning, fertilization, change of grass species and varieties, all of which affect biomass distribution (Haydock and Shaw, ; George et al ., ). While the method is usually used to estimate total biomass per given area of grassland, it can also be used in combination with dry weight rank methods (Nijland, ), to estimate the biomass contribution of individual species in a grassland (Despain and Smith, ).…”

“…The technique is accurate (Haydock and Shaw, ; Despain and Smith, ; George et al ., ; Redjadj et al ., ; Boyda, ), and as a result is a preferred method for rapid surveys of grass biomass over large areas of annual rangeland (Hofstede and Witte, ; George et al ., ). Most importantly, the technique does not require sophisticated technical skills and instrumentation, thus making it easily applied by people with minimal technical skills (Haydock and Shaw, ).…”

“…Most importantly, the technique does not require sophisticated technical skills and instrumentation, thus making it easily applied by people with minimal technical skills (Haydock and Shaw, ). Similarly, while the accuracy of the remote sensing technique is influenced by weather or seasonal variation in vegetation forms and availability (Durante et al ., ), the comparative yield method is suitable to use throughout the year in many agro‐ecological settings (George et al ., ; Redjadj et al ., ). This technique therefore has a potential for use by more than 900 million small‐scale livestock producers and pastureland managers in developing countries (Staal et al ., ), who have minimal technical skills and who cannot afford quick, but expensive hand‐held electronic tools and other instrumentation.…”

Ground truthing protocols for biomass estimation in rangeland environments

“…Visual estimates can be highly subjective and inconsistent ground truth data can confound the subsequent calibration or interpretation of remotely sensed data [7]. In order to accurately correlate ground data with the satellite observations, suitable sampling regimes must be robust to the effects of seasonal conditions and subjectivity of observers [4]. At the same time, the measured parameters must be linked to what the remote sensing systems actually measure; namely top of atmosphere spectral reflectance characteristics and their derived spectral indices.…”