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The effect of crop yield potential on disease yield loss relationships in barley (Hordeum vulgare L.)

Proportional loss models commonly used in disease surveys are based on the assumption that per cent yield loss is the same in all crops, regardless of their yield potential. Estimates of regional crop loss may be inaccurate if the relationship between disease and yield loss is affected by crop yield potential. The importance of crop yield potential in disease: yield loss modelling was investigated and models for more accurate regional crop loss estimates were developed, taking crop yield potential into account. Two spring sown barley (cv. Triumph) experiments were conducted in 1987/88 and 1988/89 in Canterbury, New Zealand, to study the effect of crop yield potential on the relationship between disease and yield loss. Crop yield potentials of 323 to 806gDM/m² were generated in seven crops by varying nitrogen and water inputs, sowing date (mid-spring and early-summer) and season. Leaf rust (Puccinia hordei Otth) epidemics of different severity were generated by applying fungicides at different times, frequencies and rates to control the natural epidemics. Disease was measured as per cent disease severity (%DS), green leaf area, radiation interception and near-infrared radiation (NIR) reflectance from crop canopies. Yield was measured as total and grain dry weight. Epidemics were severe in the fully diseased plots from GS 34 and 46 to maturity in the late and early sown crops respectively. Disease reduced grain yield by 50 to 63% in 1987/88 and 24 to 38% in 1988/89 in the fully diseased plots. Disease: yield loss models were derived by regression analysis for each crop in 1987/88. Single point, multiple point and area under curve models were derived from %DS and GLAI variables, and proportional (%) and actual (gDM/m²) grain yield. The effect of yield potential was determined by comparing regression equation coefficients for each crop with crop yield potential. An area under green leaf area index curve (AUGLAIC): actual yield model was best suited to determining the effect of yield potential on yield loss. This model was selected because AUGLAIC summarised the effect of disease on plant growth over the season and actual yield represented the crop yield potential in the absence of disease and the response of actual yield to disease. Crop yield potential did not affect actual yield loss caused by leaf rust. Disease measured as AUGLAIC explained most of the variation in yield (R²adj=0.93) for all crops in both years. Assessment of GLAI is not suitable for estimation of regional crop loss because of the requirement for a rapid and low cost method. Reflectance of NIR from the crop canopy was investigated as an alternative to GLAI measurements. Reflectance was correlated significantly (P<0.001) with GLAI (r=0.66 to 0.89) and green area index (r=0.76 to 0.92). Reflectance measured at grain-filling (GS 85-87) explained most (R²adj=0.94) of the variation in yield for all crops in both years. The relationship between AUGLAIC and yield was validated with data from independent diseased and healthy barley crops. The AUGLAIC: yield model described the effects of disease on yield accurately but overestimated yield by 49 to 108% in the healthy crops. Models based on accumulated PAR (photosynthetically active radiation) intercepted by green leaves explained the observed deviations in yield of these crops from the AUGLAIC: yield model. Accumulated PAR models accounted for differences in incident radiation, canopy structure, radiation interception by green leaves, radiation use efficiency and harvest index which are important in determining dry matter production and grain yield. Accumulated PAR models described the effects of disease on crop growth which were not represented by GLAI alone. Variation in crop yield potential at the regional scale is important in disease: yield loss modelling and can be accounted for by using either separate equations for each yield potential crop or crop category, robust models, inclusion of a form function for yield potential or choice of disease and yield variables which integrate yield potential.

Identiferoai:union.ndltd.org:ADTP/283808
Date January 1992
CreatorsWhelan, Helen G.
PublisherLincoln University
Source SetsAustraliasian Digital Theses Program
LanguageEnglish
Detected LanguageEnglish
Rightshttp://purl.org/net/lulib/thesisrights

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