Masao Ohnishi

Contribution of Sink and Source Sizes to Yield Variation among Rice Cultivars

Abstract In order to identify the key factors that arrest yield improvement in rice, we observed fifteen divergent cultivars in a field at Kyoto, Japan in 1995 and 2001 under various nitrogen (N) regimes. The contribution that sink size (spikelet number X single fully ripened grain mass), source size (total available carbohydrate), and source components, non-structural carbohydrate (pre-reserved) at full heading (NSCh) and dry matter production during grain filling (DMP) had to the variation in yield among cultivars was examined. The dry weight of rough brown rice (Y) ranged from 310 to 743 g m-2 throughout two years and under all N regimes examined. Although Y correlated with both sink and ource sizes, it tended to correlate more closely with source size than with sink size. In many cultivars, source size was smaller than sink size at all conditions examined except for the low nitrogen regime. The contribution of source components to Y was analyzed with the equation : Y = Cn NSCh + Cd DMP, where C„ and Cd are coefficients of NSC utilization and of DMP utilization for grain filling. Y correlated with DMP more closely than with NSCh. ANSC (NSCh - NSCm), where NSCra is NSC at maturity and “Cn” vaguely correlated with the difference between sink size and DMP, showing that NSC is used to ompensate for the shortage of DMP to fill grains. At the same time, there were cultivar differences in NSCh and “Cn”. The highest yielding cultivar Takanari always had the greatest DMP, relatively high NSCh and stably high values of “Cn”. In conclusion, yield variation among rice cultivars correlated with source size more closely than with sink size, and DMP rather than NSCh primarily contributed to Y. While NSCh tended to be utilized complementarily to DMP, the contribution of NSCh seemed to depend on the ability of rice cultivars to utilize NSC.

Modeling of a photosynthetic crop production index for early warning using NDVI and meteorological data

This paper aims to develop a remote sensing method of monitoring grain production in the early stages of crop growth. It is important to oversee the quantity of grain in production at an early stage in order to raise the alarm well in advance if a poor harvest is looming, especially in view of the rapid population increase in Asia and the long-term squeeze on water resources. Grain production monitoring would allow orderly crisis management to maintain food security in Japan, which is far from producing enough grain for its own population. We propose a photosynthesis-based crop production index CPI that takes into account all of: solar radiation, effective air temperature, vegetation biomass, the effect of temperature on photosynthesis by leaves of grain plants, low-temperature sterility, and high-temperature injury. These later factors, which extend the model of Rasmussen, are significant around the heading period of crops. The proposed photosynthesis-based crop production index CPI has accurately predicted the rice yield expressed by the Japanese Crop Situation Index in three years, including the worst yield in recent years, at a test site in Japan.

Photosynthetic rice production index for early warning using Remote Sensing and meteorological data

This research aims to develop a remote sensing method for monitoring grain production in the early stages of crop growth in Japan and Asia. A photosynthesis based crop production index CPI for rice is proposed that takes into consideration the solar radiation, the effective air temperature, and NDVI as a factor representing vegetation biomass. The CPI index incorporates temperature influences such as the effect of temperature on photosynthesis by grain plant leaves, low-temperature effects of sterility, cool summer damage due to delayed growth, and high-temperature injury. These latter factors are significant at around the heading period of crops. The CPI index for rice was validated at ten monitoring sites in the central and northern half of Japan. The method is based on routine observation data, allowing automated monitoring of crop production at arbitrary sites without any special observations. The CPI index is applied to rice production in five regions of China, using solar irradiation data from the Japanese Geostationary Satellite, the Normalized Vegetation Index (NDVI) derived from NOAA AVHRR, and world weather data.