気温および日射量が秋まき加工・業務用ホウレンソウの乾物生産に及ぼす影響

Abstract

To achieve the stable supply of spinach for processing use, scheduled harvest based on growth prediction is effective. In this study, we investigated the effect of temperature and solar radiation on the dry matter production of spinach, and quantified the relationship. The leaf area index (LAI) was fitted with a logistic curve with integrated temperature as the explanatory variable. The fraction of radiation intercepted by the foliage was approximated by 1-exp(-0.69 × LAI) when LAI was below 3.26, and was constant at 0.89 in the higher LAI range. Average radiation-use efficiency was 0.98 g•MJ -1 , which was approximated by a logarithmic function with the mean temperature of the previous three days as the explanatory variable. The model constructed based on these relationships was validated using the growth data obtained in a different year. As a result, the logistic relationship between the integrated temperature and LAI was applicable for three data-sets obtained from cultivation tests with different planting dates. For a data-set in which early growth was retarded, minor adjustment of the integrated temperature improved the fit of the model. The correlation coefficient between estimated and observed dry matter production based on the modified model was 0.98.

Developing a Growth Model to Predict Dry Matter Production in Broccoli (<i>Brassica oleracea</i> L. var. <i>italica</i>) ‘Ohayou’

“…This finding is useful for actual cultivation at various plant densities and seasons. Many crops have shown a high correlation between accumulated solar radiation intercepted and TDW (Kamada et al, 2021;Okada and Sasaki, 2016;Sumi and Koriyama, 2013;Takada et al, 2020), with similar results obtained in this study (Fig. 3).…”

“…On the other hand, a model (Monteith, 1977) used to estimate dry matter production has helped predict growth in spinach (Kamada et al, 2021), cabbage (Okada and Sasaki, 2016), tomato (Saito et al, 2020), sweet potato (Sumi and Koriyama, 2013), and radish (Takada et al, 2020). This particular model estimates dry matter production by dividing it into the amount of solar radiation shielded by the community and the amount of dry matter produced per amount of solar radiation-shielded.…”

A RICARDIAN ANALYSIS OF CLIMATE CHANGE IMPACTS ON JAPAN’s AGRICULTURE: ACCOUNTING FOR SOLAR RADIATION

“…According to agronomic literature, poor ripening is caused by low solar radiation and high temperatures (e.g., Murata, 1964). Therefore, statistical models for evaluating rice and other crops assume a positive linear relationship between solar radiation, temperature, and crop yield or quality (Okada et al, 2011;Kamada et al, 2021).…”