Effects of free-air CO_2 enrichment on adventitious root development of rice under low and normal soil nitrogen levels

Free air CO2 enrichment(FACE) and nitrogen(N) have marked effects on rice root growth,and numerical simulation can explain these effects. To further define the effects of FACE on root growth of rice, an experiment was performed, using the hybrid indica cultivar Xianyou63. The effects of increasing atmospheric CO2 concentration [CO2], 200 μmol mol-1higher than ambient, on the growth of rice adventitious roots were evaluated, with two levels of N: low(LN, 125 kg ha-1) and normal(NN, 250 kg ha-1). The results showed a significant increase in both adventitious root number(ARN) and adventitious root length(ARL) under FACE treatment. The application of nitrogen also increased ARN and ARL, but these increases were smaller than that under FACE treatment. On the basis of the FACE experiment, numerical models for rice adventitious root number and length were constructed with time as the driving factor. The models illustrated the dynamic development of rice adventitious root number and length after transplanting, regulated either by atmospheric [CO2] or by N application.The simulation result was supported by statistical tests comparing experimental data from different years, and the model yields realistic predictions of root growth. These results suggest that the models have strong predictive potential under conditions of atmospheric [CO2] rises in the future.

二氧化碳、施氮量和移栽密度对汕优63产量形成的影响--FACE研究

大气二氧化碳(CO2)浓度升高使水稻产量增加,但这种影响是否因不同栽培条件而异尚不清楚。2011年利用中国稻田FACE(Free Air CO2Enrichment)系统平台,以敏感水稻品种汕优63为供试材料,二氧化碳设环境CO2浓度(Ambient)和高CO2浓度(Ambient+200μmol·mol-1),施氮量设低氮(15 g·m-2)和高氮(25 g·m-2),移栽密度设低密度(16穴·m-2)和高密度(24穴·m-2),研究了不同栽培条件下大气CO2浓度升高对杂交水稻产量形成的影响。结果表明:高浓度CO2对水稻抽穗期和成熟期没有影响,但使结实期株高显著增高(+7%);使单位面积穗数(+8%)和每穗颖花数(+19%)明显增多,进而使单位面积颖花量大幅增加(+29%)。高浓度CO2条件下穗数增多主要与最高分蘖数明显增加有关,而分蘖成穗率显著下降;穗型增大主要由单茎干重而非单位干重形成的颖花数增加所致。高浓度CO2环境下水稻结实能力呈增加趋势,其中平均粒重的增幅达显著水平。大气CO2浓度升高使水稻籽粒产量平均增加36%,其中在低氮低密度、低氮高密度、高氮低密度和高氮高密度条件下分别增加43%、46%、34%、23%。增施氮肥或增加移栽密度使水稻产量略有下降,但均未达显著水平。以上结果表明,高浓度CO2环境下杂交水稻因库容量增大导致产量大幅增加,调整施氮水平和移栽密度可在一定程度上改变这种肥料效应。

开放式空气中CO_2浓度增高(FACE)对水稻生长和发育的影响

人类活动导致的大气和气候变化将极大地改变作物的生长环境,其中最大的一个变化就是大气二氧化碳(CO2)浓度的迅速上升:从工业革命前的平均270μmol/mol上升到目前的381μmol/mol,到2050年至少超过550μmol/mol。FACE(Free-air CO2 enrichment,开放式空气中CO2浓度增高)试验是目前评估未来高浓度CO2对作物生长和产量实际影响的最佳方法。水稻无疑是人类最重要的食物来源,迄今为止人类利用FACE技术开展水稻响应和适应的研究已有10a(19982008年)的历史。以生长发育为主线,首次系统综述了10a水稻FACE试验在该领域的研究成果,总结了FACE情形下高浓度CO2(模拟本世纪中叶大气CO2浓度)对主要供试水稻品种(小区面积大于4m2)光合作用、生育进程、地上部生长、地下部生长、物质分配、籽粒灌浆、产量构成以及倒伏性状等影响的研究进展,比较了FACE与非FACE研究之间以及中国和日本FACE研究(世界上唯一的两个大型水稻FACE研究)之间的异同点。根据研究进展以及当前的技术水平,文章最后提出了该领域的3个优先课题:(1)FACE情形下杂交稻生产力响应高于预期的生物学机制;(2)FACE情形下CO2与主要栽培措施的互作效应;(3)FACE情形下CO2与主要空气污染物臭氧的互作效应。这些响应的机理性解析将有助于从根本上减少人类预测未来粮食安全的不确定性,进而更加有效地制订出应对全球变化的适应策略。

Universality of an improved photosynthesis prediction model based on PSO-SVM at all growth stages of tomato

CO_(2)concentration is an environmental factor affecting photosynthesis and consequently the yield and quality of tomatoes.In this study,a photosynthesis prediction model for the entire growth stage of tomatoes was constructed to elevate CO_(2)level on the basis of crop requirements and to evaluate the effect of CO_(2)elevation on leaf photosynthesis.The effect of CO_(2)enrichment on tomato photosynthesis was investigated using two CO_(2)enrichment treatments at the entire growth stage.A wireless sensor network-based environmental monitoring system was used for the real-time monitoring of environmental factors,and the LI-6400XT portable photosynthesis system was used to measure the net photosynthetic rate of tomato leaf.As input variables for the model,environmental factors were uniformly preprocessed using independent component analysis.Moreover,the photosynthesis prediction model for the entire growth stage was established on the basis of the support vector machine(SVM)model.Improved particle swarm optimization(PSO)was also used to search for the best parameters c and g of SVM.Furthermore,the relationship between CO_(2)concentration and photosynthetic rate under varying light intensities was predicted using the established model,which can determine CO_(2)saturation points at the various growth stages.The determination coefficients between the simulated and observed data sets for the three growth stages were 0.96,0.96,and 0.94 with the improved PSO-SVM and 0.89,0.87,and 0.86 with the original PSO-SVM.The results indicate that the improved PSO-SVM exhibits a high prediction accuracy.The study provides a basis for the precise regulation of CO_(2)enrichment in greenhouses.