Relationship Of Leaf Area Index Research Articles

Hyperspectral response of agronomic variables to background optical variability: Results of a numerical experiment

Understanding how biophysical and biochemical variables contribute to the spectral characteristics of vegetation canopies is critical for their monitoring. Quantifying these contributions, however, remains difficult due to extraneous factors such as the spectral variability of canopy background materials, including soil/crop-residue moisture, soil-type, and non-photosynthetic vegetation (NPV). This study focused on exploring the spectral response of two important agronomic variables (1) leaf chlorophyll content (Cab) and (2) leaf area index (LAI) under various canopy backgrounds through a global sensitivity analysis of wheat-like canopy spectra simulated using the physically-based PROSAIL radiative transfer model. Our results reveal the following general findings: (1) the contribution of each agronomic variable to the simulated canopy spectral signature varies considerably with respect to the background optical properties; (2) the influence of the soil-type and NPV on the spectral response of canopy to Cab and LAI is more significant than that caused by soil/crop-residue moisture; (3) spectral bands at 560 and 704 nm remain sensitive to Cab while being least affected by the impacts of variations in the NPV, soil-type and moisture; (4) the near-infrared (NIR) spectral bands exhibit higher sensitivity to LAI and lower background effects only in the cases of soil/crop-residue moisture but are relatively strongly affected by soil-type and NPV. Comparative analysis of the correlations of twelve widely used vegetation indices with agronomic variables indicates that LICI (LAI-insensitive chlorophyll index) and Macc01 (Maccioni index) are more effective in estimating Cab, while OSAVI (optimized soil adjusted vegetation index) and MCARI2 (modified chlorophyll absorption ratio index 2) are better LAI predictors under the simulated background variability. Overall, our results highlight that background reflectance variability introduces considerable differences in the agronomic variables’ spectral response, leading to inconsistencies in the VI- Cab /-LAI relationship. Further studies should integrate these results of spectral responsivity to develop trait-specific hyperspectral inversion models.

Remote estimation of leaf area index (LAI) with unmanned aerial vehicle (UAV) imaging for different rice cultivars throughout the entire growing season

BackgroundRice is one of the most important grain crops worldwide. The accurate and dynamic monitoring of Leaf Area Index (LAI) provides important information to evaluate rice growth and production.MethodsThis study explores a simple method to remotely estimate LAI with Unmanned Aerial Vehicle (UAV) imaging for a variety of rice cultivars throughout the entire growing season. Forty eight different rice cultivars were planted in the study site and field campaigns were conducted once a week. For each campaign, several widely used vegetation indices (VI) were calculated from canopy reflectance obtained by 12-band UAV images, canopy height was derived from UAV RGB images and LAI was destructively measured by plant sampling.ResultsThe results showed the correlation of VI and LAI in rice throughout the entire growing season was weak, and for all tested indices there existed significant hysteresis of VI vs. LAI relationship between rice pre-heading and post-heading stages. The model based on the product of VI and canopy height could reduce such hysteresis and estimate rice LAI of the whole season with estimation errors under 24%, not requiring algorithm re-parameterization for different phenology stages.ConclusionsThe progressing phenology can affect VI vs. LAI relationship in crops, especially for rice having quite different canopy spectra and structure after its panicle exsertion. Thus the models solely using VI to estimate rice LAI are phenology-specific and have high uncertainties for post-heading stages. The model developed in this study combines both remotely sensed canopy height and VI information, considerably improving rice LAI estimation at both pre- and post-heading stages. This method can be easily and efficiently implemented in UAV platforms for various rice cultivars during the entire growing season with no rice phenology and cultivar pre-knowledge, which has great potential for assisting rice breeding and field management studies at a large scale.

Canopy cover evolution, diurnal patterns and leaf area index relationships in a Mchare and Cavendish banana cultivar under different soil moisture regimes

The biggest abiotic threat to banana (Musa spp.) production is water deficit, but physiological indicators in plantations are lacking. Canopy Cover (CC) seems to be a relevant parameter, but so far not used in banana fields. Field experiments were conducted to determine the effect of optimal irrigation (FI) versus rainfed (RF) on CC and Leaf Area Indices (LAI) in two experiments with different cultivars (Mchare ‘Huti Green’ [HG, AA] and Cavendish ‘Grand Naine’[GN, AAA]) (n = 3 for HG, n = 4 for GN) until harvest of cycle 1 (C1), studying C1 and C2 plants. Soil moisture was followed using Time Domain reflectometry. CC and LAI were reduced 8–9 weeks after the start of soil moisture divergence between RF and FI treatments in both experiments (p < 0.05), leading to a reduction in CC growth rate (r) and maximum CC (CCmax) in RF plots (p < 0.05). On a daily timescale, CC varied diurnally (i.e. was reduced) under high evaporative demands, whereby soil moisture depletion increased the CC reduction. Cultivar specific CC-LAI curves were created following the Lambert-Beer equation, whereby HG had a lower extinction coefficient than GN (0.52 vs. 0.67). CC growth over time seems a promising indicator for water deficit in the field. Diurnally, CC is more affected by evaporative demand than soil moisture depletion, although soil moisture depletion increases CC diurnal drops under high ET0.

Using heterogeneity indices to adjust basal area – Leaf area index relationship in managed coniferous stands

The structure of contemporary managed forests is complex and deviates from experimental forests which are usually even-aged monocultures and single-storied. To apply theoretical growth and yield functions on managed forests, adjustments are required, especially for leaf area index (LAI) which is a key biophysical variable in process-based growth models. To asses this, the performance of canopy LAI in modelling the basal area (BA) of managed boreal forests dominated by Norway spruce (Picea abies (L). Karst) and Scots pine (Pinus sylvestris L.) was investigated by heterogeneity analysis. The study was based on the assumption that canopy LAI and BA are strongly related and are vital for estimating stand productivity and growth. Managed forests were represented by field data from the 2016 and 2017 Swedish National Forest Inventory (NFI) campaigns. Species-specific LAI conversion parameters were applied on the general plant area index (PAI) values from hemispheric fish-eye photos taken from the permanent and temporary NFI sample plots. The heterogeneity analysis was studied in two parts by: (a) ground-based stand structural heterogeneity (SSH) described by species composition, coefficient of tree diameter variation, tree social status and height-diameter ratio, and (b) spectral heterogeneity (SPH) by vegetation and textural indices developed from Sentinel-2. Species-specific final (with heterogeneity metrics) and base (without heterogeneity metrics) models were fitted for BA-LAI and BA-PAI relationships by nonlinear least squares and generalised additive regression functions, respectively. The performance of models was assessed by the root-mean-squared error (RMSE, m2 ha−1) and the relative root-mean-squared error (RelRMSE, %) metrics. For both species, BA-LAI final models (FMs) accounting for heterogeneity resulted in larger explained variance than the base models (BMs). Compared with the BMs, FMs with SSH reduced the variance by 55% in Norway spruce (RMSE = 3.33, RelRMSE = 15.39) and 43% in Scots pine (RMSE = 3.70, RelRMSE = 17.38). The fit between BA-LAI with SPH also showed an improvement for Norway spruce (RMSE = 5.56) and Scots pine (RMSE = 5.66) over the BMs, suggesting the potential use of Sentinel-2 in future growth models. The results of the study suggest that in growth models when extrapolating theoretical growth functions to managed forests, there is a need to calibrate the models with the forest structural heterogeneity. This is important for drawing realistic conclusions from growth and yield modelling of managed stands of Norway spruce and Scots pine.

Relating X-band SAR Backscattering to Leaf Area Index of Rice in Different Phenological Phases

The objective of this study is to provide complete information on the dynamic relationship between X-band (3.11 cm) backscattering intensity (σ°) and rice crop’s leaf area index (LAI) at all growth phases. Though the relationship between X-band σ° and LAI has been previously explored, details on the relationship at the reproductive phase remain unstudied. LAI at the reproductive phase is important particularly at the heading stage where LAI reaches its maximum as it is closely related to grain yield, and at flowering stage where the total leaf area affects the amount of photosynthates. Therefore, this study examined the relationship of increasing LAI (vegetative to reproductive phase) and decreasing LAI (ripening phase) with TerraSAR-X (TSX) ScanSAR (3.11 cm) σ° at HH polarisation and 45° incidence angle. The results showed a statistically significant (R2 = 0.51, p value &lt; 0.001) non-linear relationship of LAI with σ° at the vegetative to reproductive phase while no significant linear relationship was found at the ripening phase. This study completes the response curve of X-band σ° to LAI by filling in the information on the reproductive phase which more accurately characterises the dynamic relationship between the rice crop’s LAI and X-band’s σ°. This contributes to improved knowledge on the use of X-band data for estimating LAI for the whole crop cycle which is essential for the modelling of crop growth and estimation of yield.

Determination of critical nitrogen concentration and dilution curve based on leaf area index for summer maize

Injudicious nitrogen (N) fertilizer application has increased the risk of environmental pollution and decreased grain yields, farm profits, and N use efficiency. The plant based N diagnostic tools can be used to optimize N management of summer maize production. This study was designed to develop and validate a leaf area index (LAI) based critical N (Nc) dilution curve and to establish a theoretical framework to link the relationship of LAI and plant dry matter (DM) based curves, as well as to compare the differences between LAI, plant DM and growth stage-based Nc curves. Six field experiments were set with a range of N application rates (0–320 kg N ha−1) and plant densities (6 to 8 × 104 plants ha-1) using four summer maize cultivars (Zhengdan958, Denghai605, Xundan20, and Denghai661) in Henan province of China. LAI and plant N concentration (PNC) were determined from V6 to R1 stages in each experiment for the development of the Nc curve. LAI and PNC ranged from 1.28 to 6.12 and 1.34% to 3.31% under different N levels, respectively. Allometric relationships between LAI, plant DM, and critical N uptake (Nuc) were developed under non-N-limiting treatments. The relationship between Nc and LAI during vegetative growth period was described by a power function (Nc = 3.84LAI−0.45). N nutrition index (NNI) increased with the increasing N application rate and ranged from 0.56 and 1.23 across different N treatments. Our results validated that plant N uptake was proportional with LAI, and the allometric parameter between LAI and plant DM of summer maize was close to the theoretical value 2/3. The newly developed LAI-based Nc curve could identify plant N status (N-limiting and non-N-limiting) during key N requirement period of summer maize and can be used for precision N management for summer maize grown in China.

Estimation of leaf area index from high resolution ZY-3 satellite imagery in a catchment dominated by Larix principis-rupprechtii, northern China

Leaf area index (LAI) is a key factor that determines a forest ecosystem’s net primary production and energy exchange between the atmosphere and land surfaces. LAI can be measured in many ways, but there has been little research to compare LAI estimated by different methods. In this study, we compared the LAI results from two different approaches, i.e., the dimidiate pixel model (DPM) and an empirical statistic model (ESM) using ZY-3 high-accuracy satellite images validated by field data. We explored the relationship of LAI of Larix principis-rupprechtii Mayr plantations with topographic conditions. The results show that DPM improves the simulation of LAI (r = 0.86, RMSE = 0.57) compared with ESM (r = 0.62, RMSE = 0.79). We further concluded that elevation and slope significantly affect the distribution of LAI. The maximum peak of LAI appeared at an aspect of east and southeast at an elevation of 1700–2000 m. Our results suggest that ZY-3 can satisfy the needs of quantitative monitoring of leaf area indices in small-scale catchment areas. DPM provides a simple and accurate method to obtain forest vegetation parameters in the case of non-ground measurement points.

Evaluating the effect of alternative carbon allocation schemes in a land surface model (CLM4.5) on carbon fluxes, pools, and turnover in temperate forests

Abstract. How carbon (C) is allocated to different plant tissues (leaves, stem, and roots) determines how long C remains in plant biomass and thus remains a central challenge for understanding the global C cycle. We used a diverse set of observations (AmeriFlux eddy covariance tower observations, biomass estimates from tree-ring data, and leaf area index (LAI) measurements) to compare C fluxes, pools, and LAI data with those predicted by a land surface model (LSM), the Community Land Model (CLM4.5). We ran CLM4.5 for nine temperate (including evergreen and deciduous) forests in North America between 1980 and 2013 using four different C allocation schemes: i. dynamic C allocation scheme (named "D-CLM4.5") with one dynamic allometric parameter, which allocates C to the stem and leaves to vary in time as a function of annual net primary production (NPP); ii. an alternative dynamic C allocation scheme (named "D-Litton"), where, similar to (i), C allocation is a dynamic function of annual NPP, but unlike (i) includes two dynamic allometric parameters involving allocation to leaves, stem, and coarse roots; iii.–iv. a fixed C allocation scheme with two variants, one representative of observations in evergreen (named "F-Evergreen") and the other of observations in deciduous forests (named "F-Deciduous"). D-CLM4.5 generally overestimated gross primary production (GPP) and ecosystem respiration, and underestimated net ecosystem exchange (NEE). In D-CLM4.5, initial aboveground biomass in 1980 was largely overestimated (between 10 527 and 12 897 g C m−2) for deciduous forests, whereas aboveground biomass accumulation through time (between 1980 and 2011) was highly underestimated (between 1222 and 7557 g C m−2) for both evergreen and deciduous sites due to a lower stem turnover rate in the sites than the one used in the model. D-CLM4.5 overestimated LAI in both evergreen and deciduous sites because the leaf C–LAI relationship in the model did not match the observed leaf C–LAI relationship at our sites. Although the four C allocation schemes gave similar results for aggregated C fluxes, they translated to important differences in long-term aboveground biomass accumulation and aboveground NPP. For deciduous forests, D-Litton gave more realistic Cstem ∕ Cleaf ratios and strongly reduced the overestimation of initial aboveground biomass and aboveground NPP for deciduous forests by D-CLM4.5. We identified key structural and parameterization deficits that need refinement to improve the accuracy of LSMs in the near future. These include changing how C is allocated in fixed and dynamic schemes based on data from current forest syntheses and different parameterization of allocation schemes for different forest types. Our results highlight the utility of using measurements of aboveground biomass to evaluate and constrain the C allocation scheme in LSMs, and suggest that stem turnover is overestimated by CLM4.5 for these AmeriFlux sites. Understanding the controls of turnover will be critical to improving long-term C processes in LSMs.

Reflectance-based crop coefficients REDUX: For operational evapotranspiration estimates in the age of high producing hybrid varieties

Methodologies based on earth observation remote sensing allow for a precise estimation of actual water requirements for irrigated crops across large areas. In spite of the many number of experiments using or analyzing the relationship between the basal crop coefficient (Kcb) and the soil adjusted vegetation index (SAVI) for maize, the development of new maize hybrid varieties with modifications related to canopy architecture suggest a possible change of the leaf area index (LAI) for maximum Kcb and its relationship with the SAVI or other vegetation indices. In addition, we noted a lack of analysis of these relationships for cultivated soybean. The objective of this paper is to analyze the Kcb, SAVI and LAI relationships in maize and soybean based on the non-linear relationships proposed by Choudhury et al. (1994). In addition, we propose a modification of the Choudhury et al. (1994) approach based on field-based experimental evidence of a minimum Kcb greater than 0. For sites with limited field data, we also analyze the utility of a simple linear regression based on the Kcb and SAVI values for bare soil and maximum Kcb values. The resulting Kcb-SAVI relationships are assimilated into a remote sensing based soil water balance model. The results of the model are analyzed in terms of irrigation requirements and crop evapotranspiration (ETa) for 11 growing seasons in two fields cultivated with irrigated and rain-fed maize and soybean in eastern Nebraska. Comparisons of measured and modelled ETa values indicate a good agreement, with RMSE lower than 0.7mmd−1 for weekly averaged values. The comparison of actual irrigation applied and irrigation requirements indicate the central pivot systems could not supply adequate water in some growing seasons with higher demands.

Modeling of terracette‐hillslope soil moisture as a function of aspect, slope and vegetation in a semi‐arid environment

AbstractIn the semi‐arid western United States, water availability plays a defining role in land use. Soil moisture, vegetation, and microtopography are key variables in the hydrologic function of these ecosystems. Previous research has not addressed the influence of site‐specific aspect, vegetation, or slope gradient on terracette soil moisture patterns in semi‐arid rangelands. Therefore, the objectives of this study were to: (1) assess the influence of terracette site aspect, vegetation cover, and slope on soil moisture; (2) conceptualize conditions at the hillslope scale given terracette morphology; and (3) estimate the extent of terracettes at a regional scale. The Simultaneous Heat and Water (SHAW) model was used to simulate soil water dynamics of terracettes given variations in site conditions. These results were coupled with time‐of‐flight laser scans to quantify terracette bench and riser percent‐area, and statewide assessments of terracette extent using digital orthoimagery and a geographical information system (GIS). Modeling results indicated site aspect had minimal influence (±0.005 m3 m−3) on terracette soil moisture. Vegetation, represented as leaf area index (LAI), had the single‐most influential effect on terracette volumetric water content (θv) demonstrated by an inverse relationship of LAI to mean terracette hillslope θv; and slope increases of ≥15% on northern azimuths increased mean θv which contrasted with southern azimuths for similar slope increases. Laser scanning results indicated bench width and riser length could be estimated from mean site slope (R2 = 0.82 risers and R2 = 0.93 benches). Aerial orthoimagery/GIS assessments estimated &gt;159 000 ha of terracettes throughout the State of Idaho, with &gt;41 000 ha (~26%) occurring on lands managed as grazing allotments. These findings provide an increased understanding of rangeland hydrologic processes as influenced by cattle density, vegetation, and terracettes which can aide land managers in their selection and application of best management practices on these lands. Copyright © 2017 John Wiley &amp; Sons, Ltd.

Impact of land‐use intensity on the relationships between vegetation indices, photosynthesis and biomass of intensively and extensively managed grassland fens

AbstractVegetation indices are widely used as model inputs and for non‐destructive estimation of biomass and photosynthesis, but there have been few validation studies of the underlying relationships. To test their applicability on temperate fens and the impact of management intensity, we investigated the relationships between normalized difference vegetation index (NDVI), leaf area index (LAI), brown and green above‐ground biomass and photosynthesis potential (PP). Only the linear relationship betweenNDVIandPPwas management independent (R2 = 0·53).LAItoPPwas described by a site‐specific and negative logarithmic function (R2 = 0·07–0·68). The hyperbolic relationship ofLAIversusNDVIshowed a high residual standard error (s.e.) of 1·71–1·84 and differed between extensive and intensive meadows. Biomass andLAIcorrelated poorly (R2 = 0·30), with high species‐specific variability. Intensive meadows had a higher ratio ofLAIto biomass than extensive grasslands. The fraction of green to total biomass versusNDVIshowed considerable noise (s.e. = 0·13). These relationships were relatively weak compared with results from other ecosystems. A likely explanation could be the high amount of standing litter, which was unevenly distributed within the vegetation canopy depending on the season and on the timing of cutting events. Our results show there is high uncertainty in the application of the relationships on temperate fen meadows. For reliable estimations, management intensity needs to be taken into account and several direct measurements throughout the year are required for site‐specific correction of the relationships, especially under extensive management. UsingNDVIinstead ofLAIcould reduce uncertainty in photosynthesis models.

Investigating the impact of leaf area index temporal variability on soil moisture predictions using remote sensing vegetation data

The impact of leaf area index (LAI) seasonality on three-year (2005–2007) daily soil moisture predictions was investigated for two different land surface models (IBIS and HYDRUS) at the Stanley semi-arid grassland field site. Three daily LAI time series derived from different empirical NDVI–LAI relationships using the MODIS NDVI data were used in the analysis. Calibration results from both models consistently suggested that an average LAI over time, rather than a time varying daily LAI, was sufficient to reproduce daily soil moisture at our site. We did, however, find that the sensitivity of the impact of LAI time variability on soil moisture estimation was a function of soil parameters. The influence of LAI time variation on the soil moisture simulations is controlled by the sensitivity of modelled soil moisture to the average LAI values over that period, and soil parameters affected the sensitivity of the model to LAI. Those parameter sets that were most sensitive to the long-term mean LAI were also those that were the most sensitive to the time variability. In our case, model calibrations using a constant LAI adjusted the soil parameters to reduce the impact of LAI variability. Results also suggested that the LAI variability could be significant if the varying LAI approached a very low level (i.e. LAI < 1) for a significant proportion of the simulation period. This is most likely to be the case for short grasses in grasslands.

Applications of georeferenced high-resolution images obtained with unmanned aerial vehicles. Part II: application to maize and onion crops of a semi-arid region in Spain

Leaf area index (LAI) is involved in biological, environmental and physiological processes, which are related to photosynthesis, transpiration, interception of radiation and energy balance. Thus, most crop models use LAI as a key feature to characterize the growth and development of crops. However, direct measures of LAI are destructive and tedious so that samplings can seldom be repeated in time and in space. Green canopy cover (GCC) is directly involved in crop growth and development. GCC estimation can benefit from aerial observation, as it can be measured by using image analysis or estimated by obtaining different vegetation indices. The main purpose of the second part of this paper was to study the relationships between GCC and LAI by using aerial images from UAVs in order to characterize crop growth. Also, the relationships between GCC and a vegetation index based on the visible spectrum was calibrated and validated. Relationships between LAI and GCC, growing degree days (GDD) and GCC and GDD and LAI were calibrated and validated for maize and onion crops with proper fitting. Visible atmospherically resistant index also appears to be a sensitive indicator to different growing stages and could generally be applied to any field crop. To apply this methodology, GCC and LAI relationships must be calibrated for many other crops in different irrigable areas. In addition, the cost of the UAV is expected to decrease while autonomy increases through improved battery life and reductions in the weight of on-board sensors.

Optical methods for the non-destructive estimation of leaf area index in kohlrabi and lettuce

The dimensionless leaf area index (LAI) is a fundamental crop characteristic. Since the direct measurement of LAI or leaf area is labour intensive and destructive, fast and reliable indirect methods have been devised to estimate LAI of different crops. The objective of this work was to test indirect methods for the non-destructive estimation of LAI in kohlrabi (Brassica oleracea var. gongylodes L.) and lettuce (Lactuca sativa var. capitata L.). Focusing on the gap fraction methodology, digital photographs and simultaneous radiation interception measurements were taken using a Li-Cor plant canopy analyser (LAI-2200) on 12 sampling dates from planting to harvest, with concurrent destructive estimations of the leaf area. Several geometric protocols of the LAI-2200 and inversion algorithms of the accompanying software were evaluated. Very good indirect-direct LAI relationships were obtained for kohlrabi (R2>0.97, n=12) and lettuce (R2>0.99, n=9) for the most suitable protocols and algorithms.

Two separate periods of the LAI–VIs relationships using in situ measurements in a deciduous broadleaf forest

Leaf area index (LAI) is an important parameter in many ecological models and is used to link remote sensing and ecological models. The relationship of LAI and vegetation indices (VIs) such as the normalized difference vegetation index (NDVI) or the enhanced vegetation index (EVI) are not as linear relationship especially for deciduous broadleaf forests due to leaf seasonality. To obtain the accurate LAI–VI relationship, in this study, in situ observation data of LAI and VIs were used to rigorously examine the LAI–VI relationship for a deciduous broadleaf forest. Spectral reflectance data from a hemispherical spectroradiometer (HSSR) system were used to calculate in situ NDVI and EVI. LAI was estimated using leaf seasonality (in situ measurement of sample shoots) and litter trap approaches. The LAI–VI relationship was analyzed in two ways: (1) as a one-period relationship using data for the entire year and (2) for two separate periods, leaf-expansion to -saturation and leaf-saturation to -fall. There were nearly linear correlations between both in situ NDVI and EVI and in situ LAI when looking at data for the entire year. When separated into two time periods, the LAI–EVI relationship was clearer than the LAI–NDVI relationship, with a linear regression (r2=0.96) in the leaf-expansion to -saturation period and a clear logarithmic curve (r2=0.97) in the leaf-saturation to -fall period. NDVI and EVI estimates of LAI derived from a moderate resolution imaging spectroradiometer (MODIS) were evaluated by applying the regression equations for both the one- and two-period LAI–VI relationships. The two-period relationships presented better results than the single relationship, because they responded directly to the leaf phenological cycle of the deciduous broadleaf forest. These results indicate that the two-period relationships estimate LAI more accurately than the single relationship, especially in the leaf-expansion to -saturation period. However, the LAI–VI relationship in the leaf-saturation to -fall period should be carefully reconsidered to improve the accuracy of primary productivity estimations.

Ground cover and leaf area index relationship in a grass, legume and crucifer crop

Canopy characterization is essential for describing the interaction of a crop with its environment. The goal of this work was to determine the relationship between leaf area index (LAI) and ground cover (GC) in a grass, a legume and a crucifer crop, and to assess the feasibility of using these relationships as well as LAI-2000 readings to estimate LAI. Twelve plots were sown with either barley (Hordeum vulgare L.), vetch (Vicia sativa L.), or rape (Brassica napus L.). On 10 sampling dates the LAI (both direct and LAI-2000 estimations), fraction intercepted of photosynthetically active radiation (FIPAR) and GC were measured. Linear and quadratic models fitted to the relationship between the GC and LAI for all of the crops, but they reached a plateau in the grass when the LAI &amp;gt; 4. Before reaching full cover, the slope of the linear relationship between both variables was within the range of 0.025 to 0.030. The LAI-2000 readings were linearly correlated with the LAI but they tended to overestimation. Corrections based on the clumping effect reduced the root mean square error of the estimated LAI from the LAI-2000 readings from 1.2 to less than 0.50 for the crucifer and the legume, but were not effective for barley. &amp;nbsp;

Spatial simulation of evapotranspiration of semi-arid Inner Mongolian grassland based on MODIS and eddy covariance data

In semi-arid regions of Central Asia, water shortage results from low annual precipitation (P) with high interannual variability. Evapotranspiration (ET) dominates water balance losses entirely. Previous studies showed large differences between individual grassland sites in the partitioning of ET into evaporation and transpiration, but only little difference in the evaporative ratio ET/P. The hydrological model BROOK90 was applied to the Xilin river catchment in Inner Mongolia (China) in combination with data from moderate resolution imaging spectroradiometer (MODIS) and ET measurements. The ET part of the model was parameterised using several years of eddy covariance (EC) measurements over grasslands differing in grazing intensity and leaf area index (LAI). Using a relatively well-established relationship of LAI and P as well as LAI and temperature derived from MODIS, the water balance components of a 16 km² sample area in the catchment were modelled with a 1 km² resolution for the vegetation period of 2006. All pixels were modelled assuming a similar ET control as at the EC sites. Spatial variation in ET as well as in the partitioning of ET between transpiration and evaporation could be identified. The results indicate the potential of using MODIS data and BROOK90 to upscale ET of semi-arid grasslands from site to larger grass dominated catchments.

Comparison of Three Methods for Estimating Leaf Area Index of Upland Rice Cultivars

Nondestructive estimation of leaf area index (LAI) using two canopy analyzers (LAI‐2000 and SunScan) was compared with destructively measured LAI for four upland rice (Oryza spp.) cultivars with various types of canopy development during the period from 21 to 56 d after sowing (DAS). For the LAI‐2000, LAI was estimated with or without the wide angle reading (Ring 5). When data from 50 and 56 DAS, which included LAI measured destructively of &gt;4 m2 m−2, were omitted, the relationship of LAI measured destructively with LAI estimated by the SunScan was highly significant (R2 = 0.96) and had lower root mean square error than that with LAI estimated by the LAI‐2000 without Ring 5. We conclude that by restricting the range of LAI to &lt;4 m2 m−2, the SanScan can provide reasonable estimates of LAI.

Evapotranspiration and energy balance of Brazilian savannas with contrasting tree density

Tropical savannas including those of central Brazil (Cerrado) serve as an important regulator of energy and mass exchange with the atmosphere. However, the effects of interactions between grasses and deeply rooted trees and shrubs on energy and water fluxes of savannas are not well understood. In this study, eddy-covariance measurements were used to observe and compare the energy balance and evapotranspiration at two adjacent Cerrado sites differing in woody plant density. Latent energy flux (LE) and evapotranspiration (ET) for Cerrado Denso (8–10-m trees with a cover density of approximately 50% and an understory of grass and shrubs) were generally similar to or greater than for Campo Cerrado (widely scattered 3–4-m trees with a cover density of about 5% and an understory of grasses, shrubs, and sedges), with the greatest difference between the sites occurring during the dry season. Mean annual ET was 823 and 689 mm yr −1, and LE averaged 63.9 ± 11.7 and 53.5 ± 14.3 W m −2 at Cerrado Denso and Campo Cerrado, respectively. At both sites, ET reached a minimum at near the end of the dry season, indicating that reduced water availability constrained dry season ET at both sites. The Bowen ratio at both sites increased as soil moisture decreased, but with a stronger dependency at the Campo Cerrado site, presumably due to the dominance of shallow-rooted plants and the larger contribution of soil evaporation there. Energy partitioning, as indicated by the Bowen ratio, was also strongly influenced by variations in leaf area index (LAI). The strong similarity in the Bowen ratio–LAI relationship for the two sites suggests that LAI can be used to explain much of the observed temporal and spatial ET variability across seasons and variations in woody plant density in the Cerrado.

Denaturing high performance liquid chromatography used in detection of germline mutation in South American hereditary non-polyposis colorectal cancer families

Bolivia is part of the eight most important centres of biodiversity and domestication of plants in the world, including a broad diversity of Andean grains, roots and tubers. A study was implemented to obtain the quantitative information to develop and validate, a simple growth potential model of Andean tubers in production areas located above 3000 m altitude, and to analyze the difference between species in growth attributes and the resulting tuber production. Three potato species and sub-species (Solanum tuberosum ssp. andigenum and ssp. tuberosum, and Solanum juzepczukii) as well as Oca (Oxalis tuberosa), Isaño (Tropaeolum tuberosum) and Papalisa (Ullucus tuberosus) were studied. Trials were conducted under normal field conditions prevailing in Bolivia but with the best cropping techniques available locally to obtain optimal growing conditions. Data on dry weight (of leaves, stems, tubers and roots) and leaf area were taken at several dates in five trials conducted between 1993 and 2003. The percentage of ground cover was also measured. Beta functions were fitted to data of dry weight and leaf area to establish growth curves. The potato groups have a smaller cycle duration than other Andean tubers. The Crop Growth Analysis indicated three important characteristics differentiating Andean tubers: the S. juzepczukii potato has a high Relative Growth Rate (RGR) and a higher leaf mass ratio but a smaller tuber yield, due to a smaller harvest index (HI) and a very low Net Assimilation Rate (NAR). S. tuberosum ssp. tuberosum potatoes have smaller Leaf Area Index (LAI), and RGR than juzepczukii, but their NAR and HI are higher. S. tuberosum potatoes are quite productive for the size of their LAI. The Tropaeolum tuberosum or Isaño has a great capacity of Ground Cover (GC) or a great LAI that is not translated into a greater tubers yield. It has low RGR, NAR and HI compared to all the other species studied. The crop growth was interpreted in Light Use Efficiency (LUE) and evolution of light interception through a linear model. The LUE of potato group is more elevated than the LUE of the other Andean tubers. Within each group there is no statistical difference for the LUE value. The relationship of LAI with GC or fraction of light interception was determined with both linear and exponential relations. The low slope value for the relationship between LAI and GC characterises all Andean tubers studied compared to results reported for potato under other latitudes.