A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery

The accurate and rapid estimation of canopy nitrogen content (CNC) in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture. However, the determination of CNC from field sampling data for leaf area index (LAI), canopy photosynthetic pigments (CPP; including chlorophyll a, chlorophyll b and carotenoids) and leaf nitrogen concentration (LNC) can be time-consuming and costly. Here we evaluated the use of high-precision unmanned aerial vehicle (UAV) multispectral imagery for estimating the LAI, CPP and CNC of winter wheat over the whole growth period. A total of 23 spectral features (SFs; five original spectrum bands, 17 vegetation indices and the gray scale of the RGB image) and eight texture features (TFs; contrast, entropy, variance, mean, homogeneity, dissimilarity, second moment, and correlation) were selected as inputs for the models. Six machine learning methods, i.e., multiple stepwise regression (MSR), support vector regression (SVR), gradient boosting decision tree (GBDT), Gaussian process regression (GPR), back propagation neural network (BPNN) and radial basis function neural network (RBFNN), were compared for the retrieval of winter wheat LAI, CPP and CNC values, and a double-layer model was proposed for estimating CNC based on LAI and CPP. The results showed that the inversion of winter wheat LAI, CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs. The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI, CPP and CNC. The proposed double-layer models (R²=0.67–0.89, RMSE=13.63–23.71 mg g^–1, MAE=10.75–17.59 mg g^–1) performed better than the direct inversion models (R²=0.61–0.80, RMSE=18.01–25.12 mg g^–1, MAE=12.96–18.88 mg g^–1) in estimating winter wheat CNC. The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs (R²=0.89, RMSE=13.63 mg g^–1, MAE=10.75 mg g^–1). The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.     

Effect of grazing time and intensity on growth and yield of spring wheat(Triticum aestivum L.)

A simulated grazing field experiment was conducted to determine the effect of timing and intensity of grazing on the growth and yield of a mid-late maturing spring wheat(cv. Flanker) under different watering regimes, at Wagga Wagga in southeastern Australia. The experiment was a factorial design of watering regime and pasture "grazing" as factors, with three replications. The two watering regimes were rainfed(R) and supplemental irrigation(I). There were four simulated grazing treatments: no grazing, "crash" grazing by mowing to 5 cm height on 13 June(Cut1-5), "crash" grazing by mowing to 5 cm on 15 July(Cut2-5) and "clip" grazing by mowing to 15 cm height on 15 July(Cut2-15). The lowest dry matter(simulated grazing) was obtained from RCut1-5(0.13 t ha~(–1)) and the highest(0.86 t ha~(–1)) was from ICut2-5. There was no significant difference(P0.05) among the grain yields of the grazing treatments in the respective watering regimes. However, there was significant difference(P0.05) between the grain yields of the rainfed(3.60 t ha~(–1)) and irrigated(6.0 t ha~(–1)) treatments. Under both watering regimes, the highest grain yield was obtained from the late "clip" grazings: 3.79 t ha~(–1)(RCut2-15) for rainfed and 6.47 t ha~(–1)(ICut2-15) for irrigated treatments. The lowest grain yield for the rainfed treatment was 3.26 t ha~(–1)(RCut1-5) and for the irrigated treatments, the lowest grain yield was 5.50 t ha~(–1)(ICut2-5). Harvest index(HI) was not significantly affected(P0.05) by either the watering regime or grazing. Seed weight was significantly(P0.05) affected both by the watering regime and grazing with the lowest value for 1 000-seed weight of 30.05 g(RCut2-5) and the highest value of 38.00 g(ICut2-15). Water use efficiency was significantly(P0.05) affected both by the watering regime and grazing with the lowest value of 9.94 kg ha~(–1) mm~(–1)(ICut2-5) and the highest value 13.43 kg ha~(–1) mm~(–1)(RCut2-5). By "crash" grazing late(just before stem elongation stage) to a height of 5 cm, a significantly higher(P0.05) above ground dry matter can be grazed without significantly affecting the yield both in seasons with low amount of rainfall and high amount of rainfall(irrigated in this study) although in a wet season a slightly lower(15% lower) grain yield is obtained relative to "clip grazing" to 15 cm height. Grazing of mid-late maturing wheat cultivars has the potential to fill the feed gap without significantly affecting grain yield.     

宁夏中部干旱带老压砂地枣树生物量模型研究

为准确建立宁夏中部干旱带老压砂地枣树生物量模型,采用一种不伤害枣树的方法,分别以枝条直径和长度、枣吊长度及直径,单枝上的叶片数目M和叶片面积S、枣果纵径和横径等为自变量,以各器官生物量为因变量,使用线性和非线性模型对其地上各器官生物量进行拟合,以决定系数(R~2)、均方根误差(RMSE)、总相对误差(TRE)以及平均预估误差(MPE)为评价指标,综合评价选出最优模型。使用同年9月中旬采集的数据对模型进行验证,采用预测精度A和决定系数R~2作为验证指标。结果表明,所建立的生物量模型均具有较高的精度,其中生物量模型的R2为0.89～0.97,RMSE为0.30～3.67 g,TRE为0.01%～1.55%,MPE为0%～0.14%;经过验证可知,其预测精度A在80.4%～98.5%之间,决定系数R~2在0.81～0.99之间;枣树各器官和枣果生物量模型残差均呈正态分布,整体没有呈现出发散的现象,均分布在直线上下两端。计算及分析表明所建立的老压砂地枣树生物量模型具有一定的准确性以及可靠性。     

2.4GHz无线信号在苹果园中的衰减模型

为解决苹果园中无线传感器网络的规划和部署问题,研究2.4GHz无线信道在苹果园中的传播特性。在山东省肥城市普通的苹果园进行实地试验。选取对信号传播影响最大的一列果树,发射天线固定在两棵树之间发射信号,分别测量6个高度18个位置点的接收信号强度和丢包率。回归分析结果表明:无论发射天线多高,不同水平高度上的接收信号强度衰减均符合对数路径损耗模型,拟合的决定系数为0.927~0.987。发射天线高度不变时,衰减系数n值能用接收天线高度的二次函数曲线拟合,拟合的决定系数为0.71~0.89;模型参数A和接收天线高度符合线性关系,拟合的RMSE为0.2~1.2。建立以发射天线高度、接收天线高度和传播距离为参数的衰减模型并进行验证试验,结果表明:RMSE为2~5,94%的R2值大于0.9,预测模型能较好的估算收发天线高度不同时的信号强度损耗。     

Does heat accumulation alter crop phenology,fibre yield and fibre properties of sunnhemp (Crotalaria juncea L.) genotypes with changing seasons?

Field experiments were carried out in split plot design during the dry and wet seasons for two years(two seasons each in 2016–2017 and 2017–2018) with two genotypes(SH4 and SUIN053), two plant geometry(30×15 cm and 45×15 cm main plots) and three levels of NPK(20 kg N ha~(–1), 40 kg P ha~(–1) and 40 kg K ha~(–1); 20 kg N ha~(–1), 60 kg P ha~(–1) and 60 kg K ha~(–1); 20 kg N ha~(–1), 80 kg P ha~(–1) and 80 kg K ha~(–1)) with an objective to study the relationship between fibre yield of sunhmep and thermal indices. The results indicated that the thermal units such as cumulative heat unit(CHU), photo thermal unit(PTU) and helio thermal unit(HTU) were the highest during dry seasons, while relative temperature disparity(RTD) was the highest during wet seasons irrespective of the genotypes, plant geometry and fertilizer levels. The combined analysis of variance showed that the suitability of sunnhemp genotypes for obtaining fibre and seed yields varied with season. The results further indicated that sunnhemp grew during dry seasons with longer photoperiod and higher values of growing degree days(GDD), HTU and PTU resulted in a higher fibre yield, while a higher seed yield and relatively longer, finer and stronger fibres were obtained during wet seasons with higher RTD values. Regression analysis indicated that CHU was positively related to fibre yield, while RTD was positively related to seed yield. CHU beyond 2 000 °C d reduced seed yield and favoured fibre production. In contrary to CHU, RTD values were positively related to seed yield and negatively related to fibre yield. Similarly, HTU had an inverse relationship with fibre yield while PTU had a positive relationship with fibre yield. The genotype SH4 produced a seed yield of 1 361 kg ha~(–1) during wet seasons, which was significantly higher than SUIN053, while a fibre yield of 990 kg ha~(–1)(significantly higher than that of SH4) was obtained for SUIN053 that required less CHU to attain the phenological events during dry seasons. The per unit area yields of seed and fibre with the closer spacing(30 cm×15 cm) by virtue of higher plant density were 17.0 and 14.9% higher than those with the spacing of 45 cm×15 cm, respectively. Higher doses of P and K resulted in higher seed and fibre yields.     

Leaf area index estimation in vineyards using a ground-based LiDAR scanner

Estimation of grapevine vigour using mobile proximal sensors can provide an indirect method for determining grape yield and quality. Of the various indexes related to the characteristics of grapevine foliage, the leaf area index (LAI) is probably the most widely used in viticulture. To assess the feasibility of using light detection and ranging (LiDAR) sensors for predicting the LAI, several field trials were performed using a tractor-mounted LiDAR system. This system measured the crop in a transverse direction along the rows of vines and geometric and structural parameters were computed. The parameters evaluated were the height of the vines (H), the cross-sectional area (A), the canopy volume (V) and the tree area index (TAI). This last parameter was formulated as the ratio of the crop estimated area per unit ground area, using a local Poisson distribution to approximate the laser beam transmission probability within vines. In order to compare the calculated indexes with the actual values of LAI, the scanned vines were defoliated to obtain LAI values for different row sections. Linear regression analysis showed a good correlation (R ² = 0.81) between canopy volume and the measured values of LAI for 1 m long sections. Nevertheless, the best estimation of the LAI was given by the TAI (R ² = 0.92) for the same length, confirming LiDAR sensors as an interesting option for foliage characterization of grapevines. However, current limitations exist related to the complexity of data process and to the need to accumulate a sufficient number of scans to adequately estimate the LAI.     

Light interception and radiation use efficiency response to tridimensional uniform sowing in winter wheat

Improving radiation use efficiency(RUE) of the canopy is necessary to increase wheat(Triticum aestivum) production. Tridimensional uniform sowing(U) technology has previously been used to construct a uniformly distributed population structure that increases RUE. In this study, we used tridimensional uniform sowing to create a wheat canopy within which light was spread evenly to increase RUE. This study was done during 2014–2016 in the Shunyi District, Beijing, China. The soil type was sandy loam. Wheat was grown in two sowing patterns:(1) tridimensional uniform sowing(U);(2) conventional drilling(D). Four planting densities were used: 1.8, 2.7, 3.6, and 4.5 million plants ha–1. Several indices were measured to compare the wheat canopies: photosynthetic active radiation intercepted by the canopy(IPAR), leaf area index(LAI), leaf mass per unit area(LMA), canopy extinction coefficient(K), and RUE. In two sowing patterns, the K values decreased with increasing planting density, but the K values of U were lower than that of D. LMA and IPAR were higher for U than for D, whereas LAI was nearly the same for both sowing patterns. IPAR and LAI increased with increasing density under the same sowing pattern. However, the difference in IPAR and LAI between the 3.6 and 4.5 million plants ha–1 treatments was not significant for both sowing patterns. Therefore, LAI within the same planting density was not affected by sowing pattern. RUE was the largest for the U mode with a planting density of 3.6 million plants ha–1 treatment. For the D sowing pattern, the lowest planting density(1.8 million plants ha–1) resulted in the highest yield. Light radiation interception was minimal for the D mode with a planting density of 1.8 million plants ha–1 treatment, but the highest RUE and highest yield were observed under this condition. For the U sowing pattern, IPAR increased with increasing planting density, but yield and RUE were the highest with a planting density of 3.6 million plants ha–1. These results indicated that the optimal planting density for improving the canopy light environment differed between the sowing patterns. The effect of sowing pattern×planting density interaction on grain yield, yield components, RUE, IPAR, and LMA was significant(P0.05). Correlation analysis indicated that there is a positive significant correlation between grain yield and RUE(r=0.880, P0.01), LMA(r=0.613, P0.05), and spike number(r=0.624, P0.05). These results demonstrated that the tridimensional uniform sowing technique, particularly at a planting density of 3.6 million plants ha–1, can effectively increase light interception and utilization and unit leaf area. This leads to the production of more photosynthetic products that in turn lead to significantly increased spike number(P0.05), kernel number, grain weight, and an overall increase in yield.     

枣麦间作系统冠层结构特征研究

[目的]对新疆环塔里木盆地绿洲带枣麦间作系统的林冠开放度(canopy openness)和叶面积指数(LAI)进行观测研究,结合间作系统内小麦产量的测定,分析不同树龄、不同株行距的枣麦间作系统对小麦产量的影响,为新疆枣农间作模式的优化及合理株行距的选择提供理论依据.[方法]利用鱼眼冠层仪获得新疆若羌县枣麦间作系统中枣树带的林冠影像,得出LAI和林冠开放度指标,并测定间作系统内枣树生长指标和小麦产量.[结果](1)同一株行距、不同树龄的间作系统,冠幅越小,开放度越大,LAI越小;(2)同一树龄、不同株行距的间作系统,株行距越小,开放度越小,LAI越大;(3)由于间作地枣树保护带宽2.5 m,且小麦距枣树垂直距离为1.25 m,所以测试区间作系统内小麦产量受到枣树影响较小,且开放度的高低并不影响单位面积的小麦产量.[结论]枣麦间作系统是适合新疆环塔里木盆地干旱区大面积推广的枣农间作模式之一.     

新疆喀纳斯国家自然保护区植被叶面积指数观测与遥感估算

叶面积指数(Leaf Area Index,LAI)是重要的植被结构参数,调控着植被与大气之间的物质与能量交换,在生态环境脆弱的我国西北部开展植被LAI的研究对阐明该地区植被对气候变化和人类活动的响应特征具有重要的科学意义。利用LAI-2200和TRAC仪器观测了新疆喀纳斯国家级自然保护区森林和草地的有效叶面积指数(LAI_e)和真实LAI,构建了其遥感估算模型,生成了研究区LAI_e和LAI的空间分布图。在此基础上,分析了LAI随地形因子(海拔、坡度、坡向)的变化特征,探讨了将其应用于估算研究区森林生物量密度的可行性,并评估了研究区MODIS LAI产品的精度。结果表明:研究区阔叶林、针阔混交林、针叶林、草地LAI_e的平均值分别为4.40、3.18、2.57、1.76,LAI的平均值分别为4.76、3.93、3.27、2.30。LAI_e和LAI的高值主要集中分布在湖泊和河流附近;植被LAI随海拔、坡度和坡向的变化表现出明显的垂直地带性的特点。LAI随海拔和坡度的增加呈现先增加后减小的变化趋势,坡向对针叶林和草地LAI的影响明显,但对阔叶林和针阔混交林LAI的影响较弱;森林生物量密度(BD)随LAI增加而线性增加(BD=44.396LAI-25.946,R²=0.83),研究区森林生物量密度平均值为120.3 t/hm²,估算的总生物量为5.0×10⁶t;MODIS LAI产品与利用TM数据生成的LAI之间具有一定的相似性(森林R²=0.42,草地R²=0.53),但森林和草地的MODIS LAI产品分别比利用TM数据生成的LAI偏低16.5%和24.4%。     

Yield Evaluation ofTwenty-EightAIfalfa Cultivars in Hebei Province of China

Cultivar selection is important for alfalfa （Medicago sativa L.） hay production. From 2009 to 2012, a field study was conducted to evaluate the dry matter yield （DMY） of 28 cultivars in Cangzhou District of Hebei Province, China, and to determine the most suitable cultivars for this province and other zones with similar climate conditions. 28 alfalfa cultivars were sown in late March of 2009 and were harvested for hay four times in each subsequent year. The results showed that the climatic conditions resulted in significant differences in annual DMY among years, with the second year being the highest and the first year the lowest. The top five cultivars with the highest total DMY were L2750 （62.75 t ha^-1）, Hom （62.72 t ha^-1）, 86-266 （61.55 t ha^-1）, German （61.44 t ha^-1） and Zhongmu 1 （61.18 t ha^-1）, respectively. Across all four years, first harvest had the highest ratios to annual DMY except the cultivar of Rambler, while the fourth harvest had the lowest ratio. There were positive correlation relationships between DMY of each harvest and annual DMY, and the correlation coefficients were all significant in four years. And the path coefficients of first harvest were always the highest in four years. The qualities showed small variations among these cultivars and the cultivar L3750 presented the highest crude protein in both years. Crude protein had significant positive correlation with relative feed value （RFV） in both years while crude fiber had significant negative correlation with RFV and crude fiber.     

Responses of Ryegrass （Lolium perenne L.） Grown in Mudflats to Sewage Sludge Amendment

Sewage sludge amendment （SSA） is an alternative waste disposal technique and a potential way to increase fertility of mudflats for crop growth. The present study aimed to assess the suitability of SSA by assessing the nitrogen （N） and phosphorous （P） uptakes, heavy metal accumulation, growth, biomass, and yield response of ryegrass （Lolium perenne L.） at 0, 30, 75, 150, and 300 t ha-1 SSA rates at various growth stages. The results showed that the highest biomass ofryegrass at seedling and vegetative stages were at 300 and 150 t ha-1 SSA rate, respectively. The increments of ryegrass yield at reproductive stage at 30, 75, 150, and 300 t ha1 SSA rates were 98.0, 122.6, 88.1, and 61.2%, compared to unamended soil. N and P concentrations in ryegrass increased with increasing SSA rates at all stages except N and P in roots dropped significantly at 300 t ha-1 rate at vegetative stage. The metal concentration for Mn, Cu, Zn, Ni, Cd, Cr, and Pb in shoot of ryegrass at 300 t ha-1 S SA rate increased by 0.63-, 2.34-, 15.02-, 0.97-, 10.00-, 0.01- and 1.13-fold, respectively, compared to unamended soil. However, heavy metal concentrations in shoot of ryegrass were lower than the standard for forage products in China. The study suggested that sewage sludge amendment in mudflat soils might be feasible. However, the impacts of sludge application on edible crop plants and soil environment need further investigations.     

Trends of Yield and Soil Fertility in a Long-Term Wheat-Maize System

The sustainability of the wheat-maize rotation is important to China＇s food security. Intensive cropping without recycling crop residues or other organic inputs results in the loss of soil organic matter （SOM） and nutrients, and is assumed to be non- sustainable. We evaluated the effects of nine different treatments on yields, nitrogen use efficiency, P and K balances, and soil fertility in a wheat-maize rotation system （1991-2010） on silt clay loam in Shaanxi, China. The treatments involved the application of recommended dose of nitrogen （N）, nitrogen and phosphorus （NP）, nitrogen and potassium （NK）, phosphorus and potassium （PK）, combined NPK, wheat or maize straw （S） with NPK （SNPK）, or dairy manure （M） with NPK （M1NPK and M2NPK）, along with an un-treated control treatment （CK）. The mean yields of wheat and maize ranged from 992 and 2 235 kg ha-1 under CK to 5 962 and 6 894 kg ha-1 under M2NPK treatment, respectively. Treatments in which either N or P was omitted （N, NK and PK） gave significantly lower crop yields than those in which both were applied. The crop yields obtained under NP, NPK and SNPK treatments were statistically identical, as were those obtained under SNPK and MNPK. However, M2NPK gave a significant higher wheat yield than NP, and MNPK gave significant higher maize yield than both NP and NPK. Wheat yields increased significantly （by 86 to 155 kg ha-1 yr-1） in treatments where NP was applied, but maize yields did not. In general, the nitrogen use efficiency of wheat was the highest under the NP and NPK treatments; for maize, it was the highest under MNPK treatment. The P balance was highly positive under MNPK treatment, increasing by 136 to 213 kg ha-1 annually. While the K balance was negative in most treatments, ranging from 31 to 217 kg ha^-1 yr^-1, levels of soil available K remained unchanged or increased over the 20 yr. SOM levels increased significantly in all treatments. Overall, the results indicated that combinations of organic manure and inorganic nitrogen, or retuming straw with NP is likely to improve soil fertility, increasing the yields achievable with wheat-maize system in a way which is environmentally and agronomically beneficial on the tested soil.     

Nitrogen Nutrition Index and Its Relationship with N Use Efficiency,Tuber Yield,Radiation Use Efficiency,and Leaf Parameters in Potatoes

Knowledge about crop growth processes in relation to N limitation is necessary to optimize N management in farming system. Plant-based diagnostic method, for instance nitrogen nutrition index (NNI) were used to determine the crop nitrogen status. This study determines the relationship of NNI with agronomic nitrogen use efficiency (AE_N), tuber yield, radiation use efficiency (RUE) and leaf parameters including leaf area index (LAI), areal leaf N content (N_AL) and leaf N concentration (N_L). Potatoes were grown in field at three N levels: no N (N1), 150 kg N ha⁻¹ (N2), 300 kg N ha⁻¹ (N3). N deficiency was quantified by NNI and RUE was generally calculated by estimating of the light absorbance on leaf area. NNI was used to evaluate the N effect on tuber yield, RUE, LAI, N_AL, and N_L. The results showed that NNI was negatively correlated with AE_N, N deficiencies (NNI<1) which occurred for N1 and N2 significantly reduced LAI, N_L and tuber yield; whereas the N deficiencies had a relative small effect on N_AL and RUE. To remove any effect other than N on these parameters, the actual ratio to maximum values were calculated for each developmental stage of potatoes. When the NNI ranged from 0.4 to 1, positive linear relationships were obtained between NNI and tuber yield, LAI, N_L, while a nonlinear regression fitted the response of RUE to NNI.     

Nitrogen nutrition diagnosis for cotton under mulched drip irrigation using unmanned aerial vehicle multispectral images

Remote sensing has been increasingly used for precision nitrogen management to assess the plant nitrogen status in a spatial and real-time manner. The nitrogen nutrition index (NNI) can quantitatively describe the nitrogen status of crops. Nevertheless, the NNI diagnosis for cotton with unmanned aerial vehicle (UAV) multispectral images has not been evaluated yet. This study aimed to evaluate the performance of three machine learning models, i.e., support vector machine (SVM), back propagation neural network (BPNN), and extreme gradient boosting (XGB) for predicting canopy nitrogen weight and NNI of cotton over the whole growing season from UAV images. The results indicated that the models performed better when the top 15 vegetation indices were used as input variables based on their correlation ranking with nitrogen weight and NNI. The XGB model performed the best among the three models in predicting nitrogen weight. The prediction accuracy of nitrogen weight at the upper half-leaf level (R²=0.89, RMSE=0.68 g m^–2, RE=14.62% for calibration and R²=0.83, RMSE=1.08 g m^–2, RE=19.71% for validation) was much better than that at the all-leaf level (R²=0.73, RMSE=2.20 g m^–2, RE=26.70% for calibration and R²=0.70, RMSE=2.48 g m^–2, RE=31.49% for validation) and at the plant level (R²=0.66, RMSE=4.46 g m^–2, RE=30.96% for calibration and R²=0.63, RMSE=3.69 g m^–2, RE=24.81% for validation). Similarly, the XGB model (R²=0.65, RMSE=0.09, RE=8.59% for calibration and R²=0.63, RMSE=0.09, RE=8.87% for validation) also outperformed the SVM model (R²=0.62, RMSE=0.10, RE=7.92% for calibration and R²=0.60, RMSE=0.09, RE=8.03% for validation) and BPNN model (R²=0.64, RMSE=0.09, RE=9.24% for calibration and R²=0.62, RMSE=0.09, RE=8.38% for validation) in predicting NNI. The NNI predictive map generated from the optimal XGB model can intuitively diagnose the spatial distribution and dynamics of nitrogen nutrition in cotton fields, which can help farmers implement precise cotton nitrogen management in a timely and accurate manner.     

Developing a process-based and remote sensing driven crop yield model for maize(PRYM–Maize) and its validation over the Northeast China Plain

Spatial dynamics of crop yield provide useful information for improving the production. High sensitivity of crop growth models to uncertainties in input factors and parameters and relatively coarse parameterizations in conventional remote sensing(RS) approaches limited their applications over broad regions. In this study, a process-based and remote sensing driven crop yield model for maize(PRYM–Maize) was developed to estimate regional maize yield, and it was implemented using eight data-model coupling strategies(DMCSs) over the Northeast China Plain(NECP). Simulations under eight DMCSs were validated against the prefecture-level statistics(2010–2012) reported by National Bureau of Statistics of China, and inter-compared. The 3-year averaged result could give more robust estimate than the yearly simulation for maize yield over space. A 3-year averaged validation showed that prefecture-level estimates by PRYM–Maize under DMCS8, which coupled with the development stage(DVS)-based grain-filling algorithm and RS phenology information and leaf area index(LAI), had higher correlation(R, 0.61) and smaller root mean standard error(RMSE, 1.33 t ha~(–1)) with the statistics than did PRYM–Maize under other DMCSs. The result also demonstrated that DVS-based grain-filling algorithm worked better for maize yield than did the harvest index(HI)-based method, and both RS phenology information and LAI worked for improving regional maize yield estimate. These results demonstrate that the developed PRYM–Maize under DMCS8 gives reasonable estimates for maize yield and provides scientific basis facilitating the understanding the spatial variations of maize yield over the NECP.     

Leaf area index based nitrogen diagnosis in irrigated lowland rice

Leaf area index(LAI)is used for crop growth monitoring in agronomic research,and is promising to diagnose the nitrogen(N)status of crops.This study was conducted to develop appropriate LAI-based N diagnostic models in irrigated lowland rice.Four field experiments were carried out in Jiangsu Province of East China from 2009 to 2014.Different N application rates and plant densities were used to generate contrasting conditions of N availability or population densities in rice.LAI was determined by LI-3000,and estimated indirectly by LAI-2000 during vegetative growth period.Group and individual plant characters(e.g.,tiller number(TN)and plant height(H))were investigated simultaneously.Two N indicators of plant N accumulation(NA)and N nutrition index(NNI)were measured as well.A calibration equation(LAI=1.7787LAI_(2000)–0.8816,R~2=0.870~(**))was developed for LAI-2000.The linear regression analysis showed a significant relationship between NA and actual LAI(R~2=0.863~(**)).For the NNI,the relative LAI(R~2=0.808~(**))was a relatively unbiased variable in the regression than the LAI(R~2=0.33~(**)).The results were used to formulate two LAI-based N diagnostic models for irrigated lowland rice(NA=29.778LAI–5.9397;NNI=0.7705RLAI+0.2764).Finally,a simple LAI deterministic model was developed to estimate the actual LAI using the characters of TN and H(LAI=–0.3375(TH×H×0.01)~2+3.665(TH×H×0.01)–1.8249,R~2=0.875~(**)).With these models,the N status of rice can be diagnosed conveniently in the field.     

Bioclimatological analysis of the bark necrotic rubber tree in Côte d’Ivoire plantations

The necrosis of the rubber tree is an affection of the stem, being expressed by a deterioration of the cortical tissues on the level of which are located the conducting latex tissues. We studied the water relations in a mature rubber tree plantation (clone PB 260; planted in January 1996), in Côte d’Ivoire (May and September 2004), on “healthy” and on “necrosed” trees: mean height 15.0 m; mean circumference at 1.3 m level 59.6 cm; stand density 333 trees ha^?1; leaf area index 3.2; rooting depths 4 m; field capacity, R_FC = 412 mm; permanent wilting point, R_WP = 225 mm; available water content, R_AW = 187 mm. Measurements of water potential allow us to appreciate resistances along the continuum roots-trunk-leaves: the resistances between the trunk and the leaves are identical for “healthy” trees (0.3 ± 0.1 cm³ H₂O s^?1 bars^?1) and “necrosed” trees (0.2 ± 0.1 cm³ H₂O s^?1 bars^?1); on the other hand, concerning the resistances between the roots and the trunk the two types of trees differentiate radically: 0.2 ± 0.1 cm³ H₂O s^?1 bars^?1 for “healthy” trees and 1.1 ± 0.3 cm³ H₂O s^?1 bars^?1 for “necrosed” trees, it is thus on the level of the junction of grafting that resistance is exceptionally high for “necrosed” trees. The results seem to show a difference in functioning between “healthy” and “necrosed” trees in the regulation of transpiration flux. Measurements were made here in optimum conditions for water availability; these measurements would have to be continued in conditions of water shortage for the plant, we could then provide more contrasting results.     

Abundance and Community Composition of Ammonia-Oxidizers in Paddy Soil at Different Nitrogen Fertilizer Rates

Ammonia oxidation, the first and rate-limiting step of nitrification, is carried out by both ammonia-oxidizing bacteria （AOB） and ammonia-oxidizing archaea （AOA）. However, the relative importance of AOB and AOA to nitrification in terrestrial ecosystems is not well understood. The aim of this study was to investigate the effect of the nitrogen input amount on abundance and community composition of AOB and AOA in red paddy soil. Soil samples of 10-20 cm （root layer soil） and 0-5 cm （surface soil） depths were taken from a red paddy. Rice in the paddy was fertilized with different rates of N as urea of N1 （75 kg N ha＂ yr-1）, N2 （150 kg N ha~ yrl）, N3 （225 kg N ha1 yrl） and CK （without fertilizers） in 2009, 2010 and 2011. Abundance and community composition of ammonia oxidizers was analyzed by real-time PCR and denaturing gradient gel electrophoresis （DGGE） based on amoA （the unit A of ammonia monooxygenase） gene. Archaeal amoA copies in N3 and N2 were significantly （P〈0.05） higher than those in CK and N1 in root layer soil or in surface soil under tillering and heading stages of rice, while the enhancement in bacterial amoA gene copies with increasing of N fertilizer rates only took on in root layer soil. N availability and soil NO3--N content increased but soil NH4＋-N content didn＇t change with increasing of N fertilizer rates. Otherwise, the copy numbers of archaeal amoA gene were higher （P〈0.05） than those of bacterial amoA gene in root lary soil or in surface soil. Redundancy discriminate analysis based on DGGE bands showed that there were no obvious differs in composition of AOA or AOB communities in the field among different N fertilizer rates. Results of this study suggested that the abundance of ammonia-oxidizers had active response to N fertilizer rates and the response of AOA was more obvious than that of AOB. Similarity in the community composition of AOA or AOB among different N fertilizer rates indicate that the community composition of ammonia-oxidizers was relatively stable in the paddy soil at least in short term for three years.     

Yield gap and production constraints of mango (Mangifera indica) cropping systems in Tianyang County,China

Mango is an important cash crop in the tropics and subtropics. Determining the yield gap of mango and production constraints can potentially promote the sustainable development of the mango industry. In this study, boundary line analysis based on survey data from 103 smallholder farmers and a yield gap model were used to determine the yield gap and production constraints in mango plantations in the northern mountain, central valley and southern mountains regions of Tianyang County, Guangxi, China. The results indicated that the yield of mango in three representing regions of Tianyang County,Northern Mountains, Central Valley and Southern Mountains, was 18.3, 17.0 and 15.4 t ha~(–1) yr~(–1), with an explainable yield gap of 10.9, 6.1 and 14.8 t ha~(–1) yr~(–1), respectively. Fertilization management, including fertilizer N, P_2O_5 and K_2O application rates, and planting density were the main limiting factors of mango yield in all three regions. In addition, tree age influenced mango yield in the Northern Mountains(11.1%) and Central Valley(11.7%) regions. Irrigation time influenced mango yield in the Northern Mountains(9.9%) and Southern Mountains(12.2%). Based on a scenario analysis, the predicted yield would increase by up to 50%, and fertilizer N use would be reduced by as much as approximately 20%. An improved understanding of production constraints will aid in the development of management strategy measures to increase mango yield.     

基于随机森林算法的冬小麦叶面积指数遥感反演研究

【目的】通过利用随机森林算法（random forest,RF）反演冬小麦叶面积指数（leaf area index, LAI）,及时、准确地监测冬小麦长势状况,为作物田间管理和产量估测等提供科学依据。【方法】本研究依据冬小麦拔节期、挑旗期、开花期及灌浆期地面观测数据,将相关系数分析（correlation coefficient,r）和袋外数据（out-of-bag data,OOB）重要性分析与随机森林算法（random forest,RF）相结合,在优选光谱指数和确定最佳自变量个数的基础上,构建了两种冬小麦LAI反演模型|r|-RF和OOB-RF,并利用独立数据集对两种模型进行验证;然后,将所建LAI反演模型用于无人机高光谱影像,进一步检验所建模型对无人机低空遥感平台的适用性和可靠性。【结果】|r|-RF和OOB-RF反演模型分别采用相关性前5强、重要性前2强的光谱指数作为输入因子时精度最优,验证决定系数（R²）分别为0.805、0.899,均方根误差（RMSE）分别为0.431、0.307,表明这两个模型均能对作物LAI进行精确反演,其中OOB-RF模型的反演效果更好。利用无人机高光谱影像数据结合OOB-RF估算模型反演得到冬小麦LAI与地面实测值的拟合方程的决定系数R²为0.761,RMSE为0.320,数值范围（1.02-6.41）与地面实测（1.29-6.81）亦比较吻合。【结论】本文基于地面数据构建的OOB-RF模型不仅具有较高的反演精度,而且适用性强,可用于无人机高光谱遥感平台提取高精度的冬小麦LAI信息。