基于无人机影像的冬小麦株高提取与LAI估测模型构建

株高和叶面积指数(Leaf Area Index,LAI)反映着作物的生长发育状况。为了探究基于无人机可见光遥感提取冬小麦株高的可靠性,以及利用株高和可见光植被指数估算LAI的精度,本文获取了拔节期、抽穗期、灌浆期的无人机影像,提取了冬小麦株高与可见光植被指数,使用逐步回归、偏最小二乘、随机森林、人工神经网络四种方法建立LAI估测模型,并对株高提取及LAI估测情况进行精度评价。结果显示:(1)株高提取值Hc与实测值Hd高度拟合(R^(2)=0.894,RMSE=6.695,NRMSE=9.63%),株高提取效果好;(2)与仅用可见光植被指数相比,基于株高与可见光植被指数构建的LAI估测模型精度更高,且随机森林为最优建模方法,当其决策树个数为50时模型估测效果最好(R^(2)=0.809,RMSE=0.497,NRMSE=13.85%,RPD=2.336)。利用无人机可见光遥感方法,高效、准确、无损地实现冬小麦株高及LAI提取估测可行性较高,该研究结果可为农情遥感监测提供参考。

Comparative analysis of GF-1,HJ-1,and Landsat-8 data for estimating the leaf area index of winter wheat

Using simultaneously collected remote sensing data and field measurements, this study firstly assessed the consistency and applicability of China high-resolution earth observation system satellite 1 （GF-1） wide field of view （WFV） camera, environment and disaster monitoring and forecasting satellite （H J-l） charge coupled device （CCD）, and Landsat-8 opera- tional land imager （OLI） data for estimating the leaf area index （LAI） of winter wheat via reflectance and vegetation indices （VIs）. The accuracies of these LAI estimates were then assessed through comparison with an empirical model and the PROSAIL radiative transfer model. The effects of radiation calibration, spectral response functions, and spatial resolution on discrepancies in the LAI estimates between the different sensors were also analyzed. The results yielded the following observations： （1） The correlation between reflectance from different sensors is relative good, with the adjusted coefficients of determination （R2） between 0.375 to 0.818. The differences in reflectance are ranging from 0.002 to 0.054. The correlation between VIs from different sensors is high with the R2 between 0.729 and 0.933. The differences in the VIs are ranging from 0.07 to 0.156. These results show the three sensors＇ images can all be used for cross calibration of the reflectance and VIs. （2） The four VIs from the three sensors are all demonstrated to be highly correlated with LAI （R2 between 0.703 and 0.849）. The linear models associated with the 2-band enhanced vegetation index （EVI2）, which feature the highest R2 （higher than 0.746） and the lowest root mean square errors （RMSE） （less than 0.21）, were selected to estimate the winter wheat LAI. The accuracy of the estimated LAI from Landsat-8 was the highest, with the relative errors （RE） of 2.18% and an RMSE of 0.13, while the H J-1 was the lowest, with the RE of 2.43% and the RMSE of 0.15. （3） The inversion errors in the different sensors＇ LAI estimates using the PROSAIL model are small. The accuracy of the GF-1 is the highest with the RE of 3.44%, and the RMSE of 0.22, whereas that of the H J-1 is the lowest with the RE of 4.95%, and the RMSE of 0.26. （4） The effects of the spectral response function and radiation calibration for the different sensors are small and can be ignored, but the effects of spatial resolution are significant and must be taken into consideration in practical applications.

Determination of critical nitrogen dilution curve based on leaf area index for winter wheat in the Guanzhong Plain, Northwest China

Excessive use of nitrogen (N) fertilizers in agricultural systems increases the cost of production and risk of environmental pollution. Therefore, determination of optimum N requirements for plant growth is necessary. Previous studies mostly established critical N dilution curves based on aboveground dry matter (DM) or leaf dry matter (LDM) and stem dry matter (SDM), to diagnose the N nutrition status of the whole plant. As these methods are time consuming, we investigated the more rapidly determined leaf area index (LAI) method to establish the critical nitrogen (Nc) dilution curve, and the curve was used to diagnose plant N status for winter wheat in Guanzhong Plain in Northwest China. Field experiments were conducted using four N fertilization levels (0, 105, 210 and 315 kg ha?1) applied to six wheat cultivars in the 2013–2014 and 2014–2015 growing seasons. LAI, DM, plant N concentration (PNC) and grain yield were determined. Data points from four cultivars were used for establishing the Nc curve and data points from the remaining two cultivars were used for validating the curve. The Nc dilution curve was validated for N-limiting and non-N-limiting growth conditions and there was good agreement between estimated and observed values. The N nutrition index (NNI) ranged from 0.41 to 1.25 and the accumulated plant N deficit (Nand) ranged from 60.38 to –17.92 kg ha?1 during the growing season. The relative grain yield was significantly affected by NNI and was adequately described with a parabolic function. The Nc curve based on LAI can be adopted as an alternative and more rapid approach to diagnose plant N status to support N fertilization decisions during the vegetative growth of winter wheat in Guanzhong Plain in Northwest China.

Simulation of leaf area index and biomass at landscape scale

The method for simulating the temporal and spatial distribution patterns of leaf area index (LAI) and biomass at landscape scale using remote sensing images and surface data was discussed in this paper. The procedure was: (1) annual maximum normalized difference vegetation index (NDVI) over the landscape was calculated from TM images; (2) the relationship model between NDVI and LAI was built and annual maximum LAI over the landscape was simulated; (3) the relationship models between LAI and biomass were built and annual branch, stem, root and maximum leaf biomass over the landscape were simulated; (4) spatial distribution patterns of leaf biomass and LAI in different periods all the year round were obtained. The simulation was based on spatial analysis module GRID in ArcInfo software. The method is also a kind of scaling method from patch scale to landscape scale. A case study of Changbai Mountain Nature Reserve was dissertated. Analysis and primary validation were carried out to the simulated LAI and biomass for the major vegetation types in the Changbai Mountain in 1995.

Comprehensive Study on the Influence of Evapotranspiration and Albedo on Surface Temperature Related to Changes in the Leaf Area Index

Many studies have investigated the influence of evapotranspiration and albedo and emphasize their separate effects but ignore their interactive influences by changing vegetation status in large amplitudes. This paper focuses on the comprehensive influence of evapotranspiration and albedo on surface temperature by changing the leaf area index （LAD between 30^-90~N. Two LAI datasets with seasonally different amplitudes of vegetation change between 30^-90~N were used in the simulations. Seasonal differences between the results of the simulations are compared, and the major findings are as follows. （1） The interactive effects of evapotranspiration and albedo on surface temperature were different over different regions during three seasons [March-April-May （MAM）, June-July-August （JJA）, and September-October-November （SON）], i.e., they were always the same over the southeastern United States during these three seasons but were opposite over most regions between 30°-90°N during JJA. （2） Either evapotranspiration or albedo tended to be dominant over different areas and during different seasons. For example, evapotranspiration dominated almost all regions between 30^-90~N during JJA, whereas albedo played a dominant role over northwestern Eurasia during MAM and over central Eurasia during SON. （3） The response of evapotranspiration and albedo to an increase in LAI with different ranges showed different paces and signals. With relatively small amplitudes of increased LAI, the rate of the relative increase in evapotranspiration was quick, and positive changes happened in albedo. But both relative changes in evapotranspiration and albedo tended to be gentle, and the ratio of negative changes of albedo increased with relatively large increased amplitudes of LAI.

Assimilation of temporal-spatial leaf area index into the CERES-Wheat model with ensemble Kalman filter and uncertainty assessment for improving winter wheat yield estimation

To accurately estimate winter wheat yields and analyze the uncertainty in crop model data assimilations, winter wheat yield estimates were obtained by assimilating measured or remotely sensed leaf area index （LAI） values. The performances of the calibrated crop environment resource synthesis for wheat （CERES-Wheat） model for two different assimilation scenarios were compared by employing ensemble Kalman filter （EnKF）-based strategies. The uncertainty factors of the crop model data assimilation was analyzed by considering the observation errors, assimilation stages and temporal-spatial scales. Overalll the results indicated a better yield estimate performance when the EnKF-based strategy was used to comprehen- sively consider several factors in the initial conditions and observations. When using this strategy, an adjusted coefficients of determination （R2） of 0.84, a root mean square error （RMSE） of 323 kg ha-1, and a relative errors （RE） of 4.15% were obtained at the field plot scale and an R2 of 0.81, an RMSE of 362 kg ha-1, and an RE of 4.52% were obtained at the pixel scale of 30 mx30 m. With increasing observation errors, the accuracy of the yield estimates obviously decreased, but an acceptable estimate was observed when the observation errors were within 20%. Winter wheat yield estimates could be improved significantly by assimilating observations from the middle to the end of the crop growing seasons. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. It is important to consider reasonable temporal-spatial scales and assimilation stages to obtain tradeoffs between accuracy and computation time, especially in operational systems used for regional crop yield estimates.

Performance and Analysis of a Model for Describing Layered Leaf Area Index of Rice

Layered leaf area index （LAIk） is one of the major determinants for rice canopy. The objective of this study is to attain rice LAI k using morphological traits especially leaf traits that affected plant type. A theoretical model based on rice geometrical structure was established to describe LAI k of rice with leaf length （Li）, width （Wi）, angle （Ai）, and space （Si）, and plant pole height （H） at booting and heading stages. In correlation with traditional manual measurement, the model was performed by high R2-values （0.95-0.89, n=24） for four rice hybrids （Liangyoupeijiu, Liangyou E32, Liangyou Y06, and Shanyou 63） with various plant types and four densities （3 750, 2 812, 1 875, and 1 125 plants per 100 m2） of a particular hybrid （Liangyoupeijiu）. The analysis of leaf length, width, angle, and space on LAI k for two hybrids （Liangyoupeijiu and Shanyou 63） showed that leaves length and space exhibited greater effects on the change of rice LAI k . The radiation intensity showed a significantly negative exponential relation to the accumulation of LAI k , which agreed to the coefficient of light extinction （K）. Our results suggest that plant type regulates radiation distribution through changing LAI k . The present model would be helpful to acquire leaf distribution and judge canopy structure of rice field by computer system after a simple and less-invasive measurement of leaf length, width, angle （by photo）, and space at field with non-dilapidation of plants.

Direct measurement of the three-dimensional distribution of leaf area density and light conditions in a mature oak stand by the cube method

Although the distributions of foliage and light play major roles in various forest functions,accurate,nondestructive measurement of these distributions is difficult due to the complexity of the canopy structure.To evaluate the foliage and light distributions directly and nondestructively in a mature oak stand,we used the cube method by dividing the forest canopy into small cubes(50 cm per side)and directly measured leaf area density(LAD,the total one-sided leaf area per unit volume,i.e.,cube)and relative irradiance(RI)within each cube.The distribution of LAD and of RI was highly heterogeneous,even at the same canopy height.This heterogeneity reflected the presence of foliage clusters associated with multiple forking branches.The relationship between cumulative LAD at the canopy surface and average RI followed the Beer-Lambert law.The mean light extinction coefficient(K)was 0.32.However,K was overestimated by more than double(0.80)when calculated based on the classical method using RI at the forest floor.This overestimation was caused by the lower RI due to light absorption by nonleaf plant parts below the canopy.Our findings on the complex foliage and light distributions in canopy layers should help improve the accuracy of RI and K measurements and thus more accurate predictions of environmental responses and forest functions.

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=1.7787LAI2o00-0.8816, R2=0.870＂） was developed for LAI-2000. The linear regression analysis showed a significant relationship between NA and actual LAI （R2=0.863^＊＊）. For the NNI, the relative LAI （R2=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（THxHx0.01）2＋3.665（TH×H×0.01）-1.8249, R2=0.875＊＊）. With these models, the N status of rice can be diagnosed conveniently in the field.

Improved Land Use and Leaf Area Index Enhances WRF-3DVAR Satellite Radiance Assimilation： A Case Study Focusing on Rainfall Simulation in the Shule River Basin during July 2013

The application of satellite radiance assimilation can improve the simulation of precipitation by numerical weather prediction models. However, substantial quantities of satellite data, especially those derived from low-level（surface-sensitive）channels, are rejected for use because of the difficulty in realistically modeling land surface emissivity and energy budgets.Here, we used an improved land use and leaf area index（LAI） dataset in the WRF-3 DVAR assimilation system to explore the benefit of using improved quality of land surface information to improve rainfall simulation for the Shule River Basin in the northeastern Tibetan Plateau as a case study. The results for July 2013 show that, for low-level channels（e.g., channel 3）,the underestimation of brightness temperature in the original simulation was largely removed by more realistic land surface information. In addition, more satellite data could be utilized in the assimilation because the realistic land use and LAI data allowed more satellite radiance data to pass the deviation test and get used by the assimilation, which resulted in improved initial driving fields and better simulation in terms of temperature, relative humidity, vertical convection, and cumulative precipitation.

The Stimulating Effects of Rewatering on Leaf Area of Winter Wheat Suffering Water Stress

After water stress at various levels and durations at different growth stages, rewatering could greatly stimulate the leaf area development of winter wheat. The results showed that the stimulation effect changed with water stress time, degree and duration. Rewatering under earlier stress had greater stimulation effect on leaf area than that under later stress. Higher stimulation effect was observed under severe water stress than that under moderate stress. Longer duration of stress resulted in low stimulation effect. In spite of the greater stimulation effect under severe and longer stress, the final leaf area in these situations was lower than that under moderate stress and shorter duration. Whenever the stress occurred, the stimulating effect was due to the increase of the leaf area of the tillers. Once the leaf on the main stem emerged during stress period, rewatering had no effect on its size, and consequently its leaf area. The stimulation of rewateirng on leaf area contributed to the final grain yield by 45% under moderate stress, and 67% under severe stress. Although the stimulation partly compensated for the loss during stress, the final leaf area and the grain yield could not reach the level without water stress.

Tracking leaf area index and coefficient of light extinction over the harvesting cycle of black wattle

The amount of photosynthetic radiation inter- cepted by a crop is a function of the incident solar radiation on the plants, the leaf area index （LAI）, and the light extinction coefficient （k）. We quantified LAI and k in stands of black wattle （Acacia mearnsii De Wild.） over a 7-year growth cycle at two locations in the state of Rio Grande do Sul, Brazil. Our study was conducted in commercial stands in agroecological regions with high densities of black wattle plantations. LAI was calculated as the ratio between the leaf area of a tree and its planting space, and k was derived from Beer＇s law. LAI depends on the planting site and stand age. Between the two sites, the LAI was similar over time, the amount of variation differed. Values of k depended only on stand age, with the highest average observed for stands up to 5 years old. The trend of k during the plantation cycle was inversely proportional to LAI and was correlated with LAI, leaf area, leaf dry mass, canopy volume, height, branches dry mass, total dry mass, and crown diameter.

Individual Leaf Area of Early Flowering Arabidopsis Genotypes Is More Affected by Drought than Late Flowering Ones: A Multi-Scale Analysis in 35 Genetically Modified Lines

Plants acclimate to drought optimizing the trade-off between biomass production and water loss while ensuring their survival and reproduction. Plants also modify their growth or phenology as complementary strategies in response to stress. Despite evidence of an interaction between flowering time and plant growth response to environmental stresses, this interaction in response to drought is under debate. To contribute to the analysis of this interaction, leaf growth of 35 genetically modified lines of Arabidopsis thaliana and their common wild-type, Col-0 was analyzed by a quantitative multi-scale phenotyping approach from cellular to whole plant scale both in well-watered and soil moderate water deficit conditions. These genotypes were selected for the various physiological functions potentially altered by their genetic modification and that could interact with plant growth and/or their drought responses. In all genotypes, leaf expansion decreased in response to drought both at the whole rosette and the individual leaf levels. Additionally, epidermal cell area and/or epidermal cell number decreased in response to the drought treatment. In contrast, the number of rosette leaves was reduced in only half of the genotypes and leaf growth duration was only modified in 4 of them. Despite long photoperiod conditions, the duration of the vegetative phase, i.e. the time elapsed between germination and flowering stage, varied from 12 to 27 days among genotypes under well watered conditions. Our analyses revealed that the differences of flowering time observed in well-watered condition impacted the leaf area response to drought. Early-flowering genotypes slightly decreased their final leaf number, but strongly reduced their individual leaf area compared with the late-flowering ones. This result underlines the difficulty to analyze plant response to environmental stresses when genotypes with different flowering dates are compared.

Optimization of Green Space Plant Configuration in Residential Areas of Chongqing Central Business District Based on Green Plot Ratio—A Case Study of Xuhui City Residential Community

The current urban green space construction was guided by a two-dimensional index evaluation system, resulting in weak ecological benefits of green space. Green plot ratio (GPR), as a three-dimensional indicator, can characterize the ecological benefits of green areas and the ability of green areas to participate in the operation and regulation of urban ecosystems. As an important component of urban green space, the GPR index was added to the two-dimensional index evaluation system to optimize the green space, which can promote the development of low-carbon, healthy and ecological green space. Based on the research of 22 residential districts in the central city of Chongqing, the Leaf area index (LAI) index of common native garden plants in Chongqing was formed to improve the accuracy of green capacity index measurement in Chongqing. The study also took the residential community of Sunrise City in Banan District of Chongqing as an example, and carried out the optimization design practice from four types of residential community green areas: green areas next to houses, road green areas, concentrated green areas, and green areas attached to public service facilities.

Relationship of 2 100-2 300 nm Spectral Characteristics of Wheat Canopy to Leaf Area Index and Leaf N as Affected by Leaf Water Content

The effects of leaf water status in a wheat canopy on the accuracy of estimating leaf area index (LAI) and N were determined in this study using extracted spectral characteristics in the 2 000-2 300 nm region of the short wave infrared (SWI) band. A newly defined spectral index, relative adsorptive index in the 2000-2300 nm region (RAI2000-2300), which can be calculated by RAI2000-2300 = (R2224 - R2054) (R2224 + R2054)-1 with R being the reflectance at 2224 or 2054 nm, was utilized. This spectral index, RAI2000-2300, was significantly correlated (P < 0.01) with green LAI and leaf N concentration and proved to be potentially valuable for monitoring plant green LAI and leaf N at the field canopy scale. Moreover, plant LAI could be monitored more easily and more successfully than plant leaf N. The study also showed that leaf water had a strong masking effect on the 2 000-2 300 nm spectral characteristics and both the coefficient between RAI2000-2300 and green LAI and that between RAI2000-2300 and leaf N content decreased as leaf water content increased.

Using leaf area index(LAI) to assess vegetation response to drought in Yunnan province of China

Climatic extremes such as drought have becoming a severe climate-related problem in many regions all over the world that can induce anomalies in vegetation condition. Growth and CO2 uptake by plants are constrained to a large extent by drought.Therefore, it is important to understand the spatial and temporal responses of vegetation to drought across the various land cover types and different regions. Leaf area index(LAI) derived from Global Land Surface Satellite(GLASS) data was used to evaluate the response of vegetation to drought occurrence across Yunnan Province, China(2001-2010). The meteorological drought was assessed based on Standardized Precipitation Index(SPI)values. Pearson's correlation coefficients between LAI and SPI were examined across several timescales within six sub-regions of the Yunnan. Further, the drought-prone area was identified based on LAI anomaly values. Lag and cumulative effects of lack of precipitation on vegetation were evident, with significant correlations found using 3-, 6-, 9-and 12-month timescale. We found 9-month timescale has higher correlations compared to another timescale.Approximately 29.4% of Yunnan's area was classified as drought-prone area, based on the LAI anomaly values. Most of this drought-prone area was distributed in the mountainous region of Yunnan.From the research, it is evident that GLASS LAI can be effectively used as an indicator for assessing drought conditions and it provide valuable information for drought risk defense and preparedness.

Litter production and leaf nutrient concentration and remobilization in response to climate seasonality in the central Amazon

Litterfall is the largest source of nutrients to for-est soils of tropical rainforests.However,variability in lit-terfall production,nutrient remobilization,and changes in leaf nutrient concentration with climate seasonality remain largely unknown for the central Amazon.This study meas-ured litterfall production,leaf nutrient remobilization,and leaf area index on a forest plateau in the central Amazon.Litterfall was measured at monthly intervals during 2014,while nitrogen,phosphorus,potassium,calcium and mag-nesium concentrations of leaf litter and canopy leaves were measured in the dry and rainy seasons,and remobilization rates determined.Leaf area index was also recorded in the dry and rainy seasons.Monthly litterfall varied from 33.2(in the rainy season)to 87.6 g m^(-2) in the dry season,while leaf area index increased slightly in the rainy season.Climatic seasonality had no effect on concentrations of nitrogen,calcium,and magnesium,whereas phosphorous and potassium responded to rainfall seasonality oppositely.While phosphorous increased,potassium decreased during the dry season.Over seasons,nitrogen,potassium,and phosphorous decreased in leaf litter;calcium increased in leaf litter,while magnesium remained unaffected with leaf aging.Regardless,the five nutrients had similar remobilization rates over the year.The absence of climate seasonality on nutrient remobilization suggests that the current length of the dry season does not alter nutrient remobilization rates but this may change as dry periods become more prolonged in the future due to climate change.

基于集合卡尔曼滤波的帽儿山森林多源LAI产品重建及融合校正方法

【目的】现有叶面积指数(LAI)产品大多存在分辨率低、数据异常和精度低等问题,难以满足某些应用需求。因此,本研究提出一种多源LAI数据的融合方法,以减少不同来源数据的差异并提高产品精度。【方法】以帽儿山实验林场的阔叶林和针叶林区域为研究区,基于2017年的MODIS、VIIRS和PROBA-V的LAI产品,利用多年LAI数据作为先验知识建立LAI背景库修正低质量数据,对3种LAI数据集进行混合像元分解的降尺度处理,基于Sentinel-2反射率产品耦合集合卡尔曼滤波(EnKF)算法、LAI动态模型和辐射传输模型进行数据同化,最后对同化后的3种LAI数据进行赋权融合,使用实测数据进行精度评价。【结果】在阔叶林,同化后的MODIS、VIIRS和PROBA-V LAI与实测数据的相关系数分别为0.59、0.56和0.62,比原始数据提升了0.57、0.52和0.57;均方根误差分别为0.37、0.31和0.14,比原始数据减小了1.23、1.69和1.06。在针叶林,同化后的MODIS、VIIRS和PROBA-V LAI与实测数据的相关系数分别为0.59、0.49和0.56,比原始数据提升了0.52、0.30和0.40;均方根误差分别为0.24、0.28和0.19,比原始数据减小了1.22、0.67和1.35。通过融合方法,阔叶林LAI和针叶林LAI的相关系数分别为0.83和0.76,比同化后数据的相关性更高;均方根误差分别为0.15和0.13,比同化后数据的误差更小。【结论】通过数据同化提升了3种LAI产品精度,融合后LAI较同化后单一LAI具有更高的精度和可靠性。

The scaling relationship of leaf area and total mass of sample plots across world trees

The scaling relationship between leaf area and total mass of plant has important implications for understanding resource allocations in the plant.The model of West,Brown and Enquist(WBE model)considers that a 3/4 scaling exponent of metabolic rate versus total mass to be optimal for each plant and has been confirmed numerous times.Although leaf area is a better proxy of the metabolic rate than leaf mass,few studies have focused on the scaling exponent of leaf area versus total mass and even fewer have discussed the diversification of this scaling exponent across different conditions.Here,I analyzed the scaling exponent of leaf area versus total mass of sample plots across world plants.I found that as the plant grows,it allocates fewer resources to photosynthetic tissues than expected by the WBE model.The results also empirically show that this scaling exponent varies significantly for different plant leaf habit,taxonomic class and geographic region.Therefore,leaf strategy in response to environmental pressure and constraint clearly plays a significant role.

Simulation of Growth and Leaf Area Index of Quality Protein Maize Varieties in the Southwestern Savannah Region of the DR-Congo

Logistic and exponential approaches have been used to simulate plant growth and leaf area index (LAI) in different growing conditions. The objective of the present study was to develop and evaluate an approach to simulate maize LAI that expresses key physiological and phonological processes using a minimum entry requirement for Quality Protein maize (QPM) varieties grown in the southwestern region of the DR-Congo. Data for the development and testing of the model were collected manually in experimental plots using a non-destructive method. Simulation results revealed measurable variations between crop seasons (long season A and short season B) and between the two varieties (Mudishi-1 and Mudishi-3) for height, number of visible leaves, and LAI. For both seasons, Mudishi-3, a short stature variety was associated with expected stable yield based on simulation data. In general, the model simulated reliably all the parameters including the LAI. The LAI value for mudishi-1 was higher than that of Mudishi-3. There were significant differences among the model parameters (K, Ti, a, b, Tf) and between the two varieties. In all crop conditions studied and for the two varieties, the senescence rate (a) was higher, while the growth rate (b) was lower compared to the estimates based on the STICS model.