ABSTRACT
The suitability of four popular empirical and semi-empirical stomatal conductance models (Jarvis model, Ball-Berry model, Leuning model and Medlyn model) was evaluated based on para-llel observation data of leaf stomatal conductance, leaf net photosynthetic rate and meteorological factors during the vigorous growing period of potato and oil sunflower at Wuchuan experimental station in agro-pastoral ecotone in North China. It was found that there was a significant linear relationship between leaf stomatal conductance and leaf net photosynthetic rate for potato, whereas the linear relationship appeared weaker for oil sunflower. The results of model evaluation showed that Ball-Berry model performed best in simulating leaf stomatal conductance of potato, followed by Leuning model and Medlyn model, while Jarvis model was the last in the performance rating. The root-mean-square error (RMSE) was 0.0331, 0.0371, 0.0456 and 0.0794 mol·m-2·s-1, the normalized root-mean-square error (NRMSE) was 26.8%, 30.0%, 36.9% and 64.3%, and R-squared (R2) was 0.96, 0.61, 0.91 and 0.88 between simulated and observed leaf stomatal conductance of potato for Ball-Berry model, Leuning model, Medlyn model and Jarvis model, respectively. For leaf stomatal conductance of oil sunflower, Jarvis model performed slightly better than Leuning model, Ball-Berry model and Medlyn model. RMSE was 0.2221, 0.2534, 0.2547 and 0.2758 mol·m-2·s-1, NRMSE was 40.3%, 46.0%, 46.2% and 50.1%, and R2 was 0.38, 0.22, 0.23 and 0.20 between simulated and observed leaf stomatal conductance of oil sunflower for Jarvis model, Leuning model, Ball-Berry model and Medlyn model, respectively. The path analysis was conducted to identify effects of specific meteorological factors on leaf stomatal conductance. The diurnal variation of leaf stomatal conductance was principally affected by vapour pressure saturation deficit for both potato and oil sunflower. The model evaluation suggested that the stomatal conductance models for oil sunflower are to be improved in further research.
Subject(s)
Helianthus/physiology , Models, Biological , Plant Stomata/physiology , Solanum tuberosum/physiology , China , Photosynthesis , Plant Leaves/physiologyABSTRACT
Sowing date is one of the vital factors for determining crop yield. In this study, temporal and spatial variation of optimal sowing date of summer maize was analyzed by statistical model and the APSIM-Maize model in Henan Province, China. The results showed that average summer maize optimal sowing dates ranged from May 30 to June 13 across Henan Province with earlier sowing before June 8 in the southern part and later sowing from June 4 to June 13 in the northern part. The optimal sowing date in mountain area of western Henan Province should be around May 30. Late-maturing variety Nongda 108 should be planted at least two days earlier than middle-maturing variety Danyu 13. Under climate warming background, maize sowing should be postponed for at least 3 days if maize harvesting date could be delayed for a week. It was proposed that sowing should be delayed for about a week for a yearly less precipitation pattern while advanced for about a week for a yearly more precipitation pattern compared to the normal one. Across Henan Province, the optimal sowing dates of summer maize showed no significant change trend in 1971-2010, while the potential sowing period had been extended for some regions, such as south from Zhumadian, Yichuan, Nei-xiang and Nanyang in the middle part of Henan, Linzhou in the northern Henan and Sanmenxia in the western Henan, as a result from advanced maturity of winter wheat due to increasing temperature and winter wheat cultivar change. Optimal sowing dates at 76.7% of the study stations showed no significant difference between the two methods. It was recommended that the northern Henan should sow maize immediately after any rainfall and replant afterward, while the southern Henan should not sow maize until that there were valid precipitation (3.9 mm and 8.3 mm for upper south and south parts, respectively) during sowing period, both required enough precipitation during key water requirement period and optimal temperature during grain-filling period.
