棉花花后临界氮浓度稀释模型的建立及在施氮量调控中的应用

Development and application of critical nitrogen concentration Dilution Model for cotton after flowering

在大田栽培条件下,于江苏南京(长江中下游棉区)和河南安阳(黄淮棉区)设置了棉花氮素水平试验,依据Justes的临界氮浓度稀释曲线确定方法,建立了棉花花后临界氮浓度稀释曲线模型。结果表明,2试点的临界氮浓度与地上最大生物量间均符合幂指数关系,尽管不同气候区域间的模型存在一定差异,但临界氮稀释曲线斜率相同。棉花最高(Nmax)、最低(Nmin)氮浓度稀释模型也符合幂指数关系,且2试点最高、最低氮稀释曲线斜率亦分别相同。2试点氮稀释模型参数值的差异表明,对于相同的地上部生物量,安阳试点棉株的氮累积能力高于南京。基于临界氮浓度稀释模型,建立了棉株地上部氮素与干物质累积量之间的异速生长模型和氮营养指数模型(NNI),前者可作为施氮量调控的判别指标,后者作为实际与临界氮浓度的比值,能客观、定量地诊断棉株的氮素营养状况。基于临界氮浓度稀释条件下的异速生长参数、氮营养指数及动态临界氮累积量等指标得到施氮量调控的结果一致:(1)尽管安阳、南京2试点的地上生物量、产量差异较大,但临界氮稀释曲线条件下不同气候区域棉花达到最高产量的瞬时氮吸收速率的变化趋势、氮素快速累积期、最大氮吸收速率出现日等基本一致;(2)安阳、南京2试点的适宜施氮量应控制在360 kg.hm-2和240kg.hm-2水平上。由于临界氮浓度具有合理的生物学意义,因而所建模型有精确、简单和生物学意义明确等特点,可以直接用于评估作物的需氮量,亦可用于作物氮动态模拟的复杂模型中,为适时精确施肥提供了新的思路。

The critical nitrogen （N） concentration of a plant can be defined as the minimum nitrogen concentration required for maximum growth rate at any time. To determine the critical N concentration dilution curve for cotton, several field experiments with different levels of N application （0, 120, 240, 360, and 480 kg· hm^-2） were carried out in Nanjing and Anyang, standing for the ecological conditions in the middle lower reaches of Yangtze River Valley and Yellow River Valley in China, respectively. The results show that N concentration in shoot biomass declined with the growth stage after flowering. The relationship between the shoot dry matter and critical N concentration can be described by power equation put the equation here, with b = 0. 131 for both experimental sites, a = 3.837 and 2. 858 for Anyang and Nanjing, respectively. The results mentioned above support the viewpoint that the critical N concentration dilution curve for cotton is independent of ecological region. The maximum and the minimum N concentration dilution curves also follow a power equation put the equations here, with bmax=0. 142 and bmin = 0.158 for both experimental sites, and amax = 3.530 and 3.208 and amin = 3.055 and 2.251 for Anyang and Nanjing, respectively. The same estimate of b in critieal dilution curve at the two experimental sites indieates that growth rate, density and pedoclimatic conditions do not affect the slope of the critical N dilution curve. The difference of the coefficients a between the two experimental sites shows that, the cotton plant in Anyang has a higher capacity of N accumulation in shoot biomass than in Nanjing for the same shoot dry matter. Due to its biological soundness, the critical N concentration dilution curve can be a theoretically sound and practically reliable tool for diagnosing the N nutrition status of plants. Based on the critical N concentration dilution model, the model of allometric relationships between crop N uptake at each N application level and accumulated dry matter in the shoot biomass, and the model of N nutrition index （NNI） were developed. The former can be used as an index for controlling of N application, and the latter can be used to express the N status of the cotton plants. If NNI = 1, N nutrition is considered to be optimum, NNI 〉 1 indicates excess N. NNI 〈 1 indicates N deficiency. Based on the critical N concentration model, the model of N demand at different growth stages for potential growth and yield was developed. According to the allometric growth coefficient,NNI and N accumulation rate under critical N concentration, the following conclusion can be extracted ： （ 1 ） Despite the difference of biomass and lint yield between Anyang and Nanjing, the eigenvalues of the dynamic biomass accumulation model were consistent. （2） The optimal rate of N application in Anyang should be higher than that in Nanjing, and the optimal N application rate is 360 kg hm^-2 and 240 kg hm^-2 in Anyang and Nanjing, respectively. Since the models developed in this study are based on the actual growth rate of the crop, it has the advantages over other models： it is crop specific, exact, simple and biologically sound. The models can be used directly to estimate the intrinsic crop nitrogen demand, and can be integrated, as a submodel, into the crop growth simulation models. The result of this paper has paved the way toward a timely precision nutrient fertilization.