Engineering topics

Lenhert, D.

Publications and source records attributed to Lenhert, D..

4 records

NASA NTRS1974

Wheat: Its water use, production and disease detection and prediction

The author has identified the following significant results. Discussed in this report are: (1) the effects of wheat disease on water use and yield; and (2) the use of ERTS-1 imagery in the evaluation of wheat growth and in the detection of disease severity. Leaf area index was linearly correlated with ratios MSS4:MSS5 and MSS5:MSS6. In an area of severe wheat streak mosaic virus infected fields, correlations of ERTS-1 digital counts with wheat yields and disease severity levels were significant at the 5% level for MSS bands 4 and 5 and band ratios 4/6 and 4/7. Data collection platforms were used to gather meteorological data for the early prediction of rust severity and economic loss.

Kanemasu, E. T.↗

NASA NTRS1974

ERTS-1 data collection systems used to predict wheat disease severities

The author has identified the following significant results. The feasibility of using the data collection system on ERTS-1 to predict wheat leaf rust severity and resulting yield loss was tested. Ground-based data collection platforms (DCP'S), placed in two commercial wheat fields in Riley County, Kansas, transmitted to the satellite such meteorological information as maximum and minimum temperature, relative humidity, and hours of free moisture. Meteorological data received from the two DCP'S from April 23 to 29 were used to estimate the disease progress curve. Values from the curve were used to predict the percentage decrease in wheat yields resulting from leaf rust. Actual decrease in yield was obtained by applying a zinc and maneb spray (5.6 kg/ha) to control leaf rust, then comparing yields of the controlled (healthy) and the noncontrolled (rusted) areas. In each field a 9% decrease in yield was predicted by the DCP-derived data; actual decreases were 12% and 9%.

Kanemasu, E. T.↗

NASA NTRS1974

ERTS-1 data collection systems used to predict wheat disease severities

The feasibility of using the data collection system on Earth Technology Satellite-1 to predict wheat leaf rust severity and resulting yield loss was tested. Ground-based data-collection platforms (DCPs), placed in two commercial wheat fields, transmitted to the satellite such meteorological information as maximum and minimum temperature, relative humidity, and hours of free moisture. Meteorological data received from the two DCPs from April 23 to 29 were used to estimate the disease progress curve. Values from the curve were used to predict the percentage decrease in wheat yields resulting from leaf rust. Actual decrease in yield obtained by applying a zinc and maneb spray to control leaf rust, and then comparing yields of the controlled (healthy) and the noncontrolled (rusted) areas. In each field, a 9% decrease in yield was predicted by the DCP-derived data; actual decreases were 12% and 9%.

Kanemasu, E. T.↗

NASA NTRS1974

Wheat - Its growth and disease severity as deduced from ERTS-1

The spectral reflectance of a cropped surface changes as the plant develops. An indicator of crop growth is leaf area index (ratio of green leaf area to soil area). The leaf area index, disease severity, and yield were determined for several winter wheat fields in Kansas during the 1973 growing season. Multispectral scanner (MSS) data from Earth Resources Technology Satellite-1 (ERTS-1) showed a high correlation (r greater than or equal to 0.90) between crop growth and MSS4/MSS5, and crop growth and MSS5/MSS6. Wheat disease severity and yields were significantly correlated at the 5% level with MSS4/MSS6 and with MSS4/MSS7. Further investigation is required before ERTS imagery can be routinely used detecting and estimating disease severity and yield reduction.

Kanemasu, E. T.↗