南方低甲烷排放的高产水稻品种筛选与评价

为综合评价高产和低甲烷(CH4)排放兼具的水稻品种,应对我国粮食安全和环境保护问题,于2013年晚季和2014年早季,以南方稻区50个代表品种为供试材料,在盆栽条件下采用静态箱-气相色谱法测定品种的CH4排放通量,并对生育期的CH4排放量、稻谷产量及植株农艺性状进行了综合评价。结果表明,早、晚季供试品种间的CH4排放量和产量均存在显著差异。早季全生育期的CH4排放量在7.37～21.10 g·m-2之间,晚季分蘖期至幼穗分化期的CH4排放量在1.37～13.97 g·m-2之间;早季产量在0.40～0.65kg·m-2之间,晚季在0.59～0.87 kg·m-2之间。根据系统聚类法的分析结果,将供试品种划分成具有不同CH4排放量和产量水平的类型,从中筛选出了适合推广应用的CH4低排高产、中排高产和低排中产品种,如黄华占、五优308、丰华占、特三矮、茉莉占选、广超丝苗、珍桂矮、广恢998和七桂早25等。其中,恢复系广恢998组配品种、黄华占和五优308已在我国南方稻区大面积推广应用。相关分析结果表明,株高、叶面积指数与CH4排放通量呈显著正相关,这2个性状可作为高产低CH4排放水稻新品种选育的辅助筛选指标。此外,稻谷产量与CH4排放量无显著相关。研究结果为培育高产与低CH4排放兼具的新品种提供了亲本材料和理论依据。

水稻产量遥感监测机器学习算法对比

水稻是我国的主要粮食作物,利用高光谱遥感技术在水稻未成熟之前对水稻产量进行监测,一方面可以及时调整栽培管理方式,指导合理追肥,另一方面,可以准确掌握水稻的产量信息,帮助政府提前做出决策。以2019年—2020年广州市白云区钟落潭试验基地氮肥梯度实验为基础,分别获取水稻分化期和抽穗期冠层高光谱数据、作物群体长势参数(生物量、叶面积指数)及作物养分吸收量,利用贝叶斯岭回归(BRR)、支持向量回归(SVR)、偏最小二乘回归(PLSR)三种方法建立各生育期的产量监测模型并进行精度对比,确定水稻产量的最优估算时期和最佳估测模型。结果表明,三种方法中,BRR和SVR方法更适合产量监测,在不同时期及不同的参数组合下均有较好的表现(R^(2)>0.82,NRMSE<8.22%);基于2019年与2020年数据,采用全波段光谱信息进行产量监测时,分化期最佳监测模型为BRR模型,R^(2)为0.90,抽穗期最优监测模型为SVR模型,R^(2)为0.87;采用全波段光谱协同作物群体长势参数进行产量监测时,两时期最佳监测模型均为BRR模型,R^(2)分别达到0.90和0.92;相较于BRR模型和SVR模型,PLSR模型在不同时期和不同参数组合下,最高R^(2)仅为0.75;基于2020年数据,以三种不同的参数组合作为输入时,两时期估算结果均为BRR模型最优,且分化期建模精度高于抽穗期(R^(2)至少增加0.02,NRMSE至少降低0.61%);当输入参数组合为全波段光谱协同作物群体长势参数、作物养分吸收量时,BRR模型对产量的估算精度达到最高,R^(2)为0.94。分析认为产量的最优监测时期是分化期,最优监测模型为BRR模型。研究结果可为水稻产量的早期遥感监测提供参考。

基于集成学习的水稻氮素营养及籽粒蛋白含量监测

利用高光谱遥感技术在水稻收获前对籽粒品质相关的蛋白质含量进行监测,一方面可以及时调整栽培管理方式,指导合理追肥,另一方面,有助于提前掌握籽粒品质信息,明确市场定位。该研究以广东省典型优质籼稻为研究目标,基于2019年和2020年两年氮肥梯度实验,以水稻分化期和抽穗期冠层尺度高光谱数据、水稻氮素参数,包括叶片氮素含量(LNC)、叶片氮素积累量(LNA)、植株氮素含量(PNC)、植株氮素积累量(PNA)及籽粒蛋白含量数据为基础,利用四种个体机器学习算法partial least square regression(PLSR)、K-nearest neighbor(KNN)、Bayesian ridge regression(BRR)、support vector regression(SVR),三种集成学习算法random forest(RF)、adaboost、bagging,针对水稻不同生育期氮素状况进行监测建模,在此基础上构建基于水稻冠层光谱信息、光谱信息结合水稻农学氮素参数的籽粒蛋白含量的监测模型,并对模型进行精度对比。研究结果表明,在水稻氮素营养监测方面,利用水稻冠层454~950 nm波段信息,采用RF及Adaboost算法,在水稻分化期、抽穗期及全生育期LNC、LNA、PNC及PNA模型R^(2)均达到0.90以上,同时也具有较低的RMSE和MAE。在水稻籽粒蛋白品质监测方面,采用全波段光谱信息进行籽粒蛋白含量监测时,RF具有最高的精确度与稳定性,两生育期的RF模型对籽粒蛋白含量的监测结果R^(2)分别为0.935和0.941,RMSE分别为0.235和0.226,MAE分别为0.189和0.152;两生育期以全波段光谱信息结合长势参数进行籽粒蛋白监测时,Adaboost模型具有最高的精确度和稳定性,其中分化期全波段光谱信息结合PNA作为输入参数,Adaboost模型R^(2)为0.960,RMSE为0.175,MAE为0.150,以抽穗期全波段光谱信息结合PNC作为输入参数,R^(2)为0.963,RMSE为0.170,MAE为0.137。研究结果表明,与PLSR,KNN,BRR和SVR几种个体学习器算法相比,集成算法RF,Adaboost和Bagging具备良好的处理多重共线性的能力,适合用于高光谱数据的分析与处理,在作物氮素营养监测及水稻品质的早期遥感监测方面具有明显优势。

Improving grain yield, nitrogen use efficiency and radiation use efficiency by dense planting, with delayed and reduced nitrogen application, in double cropping rice in South China

Improving both grain yield and resource use efficiencies simultaneously is a major challenge in rice production.However,few studies have focused on integrating dense planting with delayed and reduced nitrogen application to enhance grain yield,nitrogen use efficiency (NUE) and radiation use efficiency (RUE) in rice (Oryza sativa L.) in the double rice cropping system in South China.A high-yielding indica hybrid rice cultivar (Yliangyou 143) was grown in field experiments in Guangxi,South China,with three cultivation managements:farmers’practice (FP),dense planting with equal N input and delayed N application (DPEN) and dense planting with reduced N input and delayed N application (DPRN).The grain yields of DPRN reached 10.6 and 9.78 t ha^(–1) in the early and late cropping seasons,respectively,which were significantly higher than the corresponding yields of FP by 23.9–29.9%.The grain yields in DPEN and DPRN were comparable.NUE in DPRN reached 65.2–72.9 kg kg^(–1),which was 61.2–74.1% higher than that in FP and 24.6–30.2% higher than that in DPEN.RUE in DPRN achieved 1.60–1.80 g MJ^(–1),which was 28.6–37.9% higher than that in FP.The productive tiller percentage in DPRN was 7.9–36.2% higher than that in DPEN.Increases in crop growth rate,leaf area duration,N uptake from panicle initiation to heading and enhancement of the apparent transformation ratio of dry weight from stems and leaf sheaths to panicles all contributed to higher grain yield and higher resource use efficiencies in DPRN.Correlation analysis revealed that the agronomic and physiological traits mentioned above were significantly and positively correlated with grain yield.Comparison trials carried out in Guangdong in 2018 and 2019 also showed that DPRN performed better than DPEN.We conclude that DPRN is a feasible approach for simultaneously increasing grain yield,NUE and RUE in the double rice cropping system in South China.

Response of Nitrate Metabolism in Seedlings of Oilseed Rape(Brassica napus L.) to Low Oxygen Stress

In order to understand the response of nitrate metabolism in seedlings of oilseed rape （Brassica napus L.） to low oxygen stress （LOS）, two cultivars were studied at different light, LOS time and exogenous nitrate concentrations under hydroponic stress. Results show that N-uptake and dry matter of rape seedlings were decreased after LOS stress while nitrate accumulation （NA） under LOS was induced by darkness. Nitrate accumulation peaked at 3 d while root activity （RA, deifned as dehydrogenase activity） decreased with prolonged waterlogging exposure. Exogenous nitrate signiifcantly elevated NA and RA. Tungstate （TS） and LOS inhibited nitrate reductase （NR） activity while NR transcription and activity were enhanced by exogenous nitrate. Low oxygen stress stimulated the activity of superoxide dismutase （SOD） and peroxidase （POD） slightly, but inhibited that of catalase （CAT）. B. napus L. Zhongshuang 10 （ZS10）, a LOS tolerant cultivar, displayed smaller decrease upon dry matter under LOS, higher NA in darkness and lower NA in light than B. napus L. Ganlan CC （GAC）, a LOS sensitive variety. ZS10 had lower NA and higher RA after waterlogging and exogenous nitrate treatment, and higher NR activity under TS inhibition than GAC, but the activity of antioxidant enzymes did not change under LOS. The results indicate that nitrate metabolism involved tolerance of rape seedlings to LOS, with lower accumulation and higher reduction of nitrate being related to higher LOS tolerance of rape seedlings exposed to waterlogging.

Silicon-Mediated Amelioration of Fe^(2+) Toxicity in Rice (Oryza sativa L.) Roots

Silicon (Si) can enhance the resistance of plants to many abiotic stresses. To explore whether Si ameliorates Fe2+ toxicity, a hydroponic experiment was performed to investigate whether and how Si detoxifies Fe2+ toxicity in rice (Oryza sativa L.) roots. Results indicated that rice cultivar Tianyou 998 (TY998) showed greater sensitivity to Fe2+ toxicity than rice cultivar Peizataifeng (PZTF). Treatment with 0.1 mmol L-1 Fe2+ inhibited TY998 root elongation and root biomass significantly. Reddish iron plaque was formed on root surface of both cultivars. TY998 had a higher amount of iron plaque than PZTF. Addition of Si to the solution of Fe treatment decreased the amount of iron plaque on root surface by 17.6% to 37.1% and iron uptake in rice roots by 37.0% to 40.3%, and subsequently restored root elongation triggered by Fe2+ toxicity by 13.5% in the TY998. Compared with Fe treatment, the addition of 1 mmol L-1 Si to the solution of Fe treatment increased xylem sap flow by 19.3% to 24.8% and root-shoot Fe transportation by 45.0% to 78.6%. Furthermore, Si addition to the solution of Fe treatment induced root cell wall to thicken. These results suggested that Si could detoxify Fe2+ toxicity and Si-mediated amelioration of Fe2+ toxicity in rice roots was associated with less iron plaque on root surface and more Fe transportation from roots to shoots.