Plant Area Index Research Articles

Impact of Live Mulch, Brown Manuring and Herbicides on Growth and Yield of Sorghum (Sorghum bicolor (L.) Moench) in Southern Midland Zone of Kerala, India

A field experiment was conducted during the rabi season of 2023 at the Farming System Research Station, Sadanandapuram, Kerala, to evaluate the effects of live mulch, brown manuring and herbicides effect as weed management method on growth and yield of sorghum. The experiment was conducted in a randomized block design with ten treatments, replicated thrice. The treatments included: Broadcasting cowpea seeds @ 15 kg ha -1 fb live mulch at 25 DAS, Broadcasting cowpea seeds @ 30 kg ha -1 fb live mulch at 25 DAS, Broadcasting cowpea seeds @ 15 kg ha -1 fb brown manuring with 2,4-D 1 kg ha-1 at 20 DAS, Broadcasting cowpea seeds @ 30 kg ha -1 fb brown manuring with 2,4-D 1 kg ha-1 at 20 DAS, PE pretilachlor 0.75 kg ha-1fb one HW at 25 DAS, fenoxaprop-p-ethyl 0.06 kg ha-1 at 20 DAS, T7:PoE 2,4-D 1 kg ha-1 at 20 DAS, HW at 20 DAS, Weed free control and Weedy check. Among the treatments, broadcasting cowpea seeds at 30 kg ha⁻¹ followed by live mulch at 25 DAS (T2) resulted the higher growth parameters, including plant height, number of leaves per plant, and dry matter production at 45 DAS and harvest. Leaf area per plant and leaf area index were also significantly higher in T2 at 30 and 45 DAS. Yield attributes, such as panicle length, number of grains per panicle, grain weight per panicle, grain yield, stover yield and harvest index were higher in T2. Broadcasting cowpea seeds @ 30 kg ha -1fb brown manuring with 2,4-D 1 kg ha-1 at 20 DAS, pre-emergence pretilachlor 0.75 kg ha⁻¹ fb hand weeding at 25 DAS, post-emergence fenoxaprop-p-ethyl 0.06 kg ha⁻¹ at 20 DAS and weed free control performed statistically comparable with T2 in terms of growth and yield attributes.

Accuracy assessment of LAI, PAI and FCOVER from Sentinel-2 and GEDI for monitoring forests and their disturbance in Central Germany

ABSTRACT Forest monitoring benefits from biophysical parameters such as Leaf Area Index (LAI), Plant Area Index (PAI) and fractional vegetation cover (FCOVER) and can be obtained from optical and LiDAR remote sensing, such as Sentinel-2 (S2) and the Global Ecosystem Dynamics Investigation (GEDI). While GEDI-derived products consider all phyto-elements, those from S2 refer to green elements only. Apart from individual accuracies, systemic deviations among products are thus expectable. However, products from S2 and GEDI lack inter-comparison. We evaluated S2 and GEDI-derived LAI, PAI and FCOVER with digital hemispherical photography observations (DHP) in a forest disturbance hotspot in Germany across various forest conditions, including vital stands, standing deadwood and clearings. We found moderate to high agreement with in-situ data, with highest accuracy for S2-derived LAI (R2 = 0.54) and GEDI-derived FCOVER (R2 = 0.73). Agreements between S2 and GEDI products were low, which we attribute to systematic influences of woody components, GEDI’s limitations in sloped terrain, and saturation of optical signals in dense canopy. In conclusion, findings suggest that while GEDI is effective in dense canopies, S2 products are beneficial for monitoring forest recovery. We also see potential for synergistic use in monitoring standing deadwood for habitat mapping and fire risk assessment.

Variability and temporal stability of throughfall along a hillslope

Tree species composition, tree size, and microclimate can vary along hillslopes in forest ecosystems. This will affect the amount of throughfall (TF) that reaches the ground. However, few studies have focused on the effects of topography on the spatio-temporal variability of throughfall. We therefore, measured throughfall on a steep hillslope roughly 110 m long covered by European beech trees (Fagus sylvatica) in the Re della Pietra experimental catchment, Tuscany Apennines, Central Italy. Although the trees all have a similar age, the basal area, Plant Area Index (PAI), and canopy cover are higher on the lower part of the hillslope than the upper part of the hillslope. We installed 49 throughfall collectors in two 144 m2 square grids: one on the lower hillslope and one on the upper hillslope. In addition, we installed 29 collectors on a transect connecting the lower and upper grid. Throughfall was measured from all collectors at an approximately monthly interval and compared with gross precipitation (GP) measured in a nearby open area. The overall a TF/GP ratio for the 61 manual measurements during the period from July 2020 to September 2023 study period was 70 ± 3.4 %. It was higher for the growing season (71 ± 4.0 %) characterised by higher rainfall intensities than the dormant season (69 ± 2.7 %). TF/GP was lower for the lower grid than the upper grid (61 ± 5.1 vs 73 ± 6.0 % of GP, respectively). The differences between the measurements at the lower and upper grid were significant for almost all measurement days and reflect the differences in tree size and stand density. Temporal stability analysis indicated a high persistence of the spatial TF pattern for both grids, especially in the dormant season. The higher temporal stability for in the upper grid seems to be related to the lower variability in tree size. Although these results need to be confirmed by larger-scale studies at different hillslopes, they suggest that topographic differences in stand characteristics should be taken into account when determining the water balance for forested catchments.

Avian diversity across guilds in North America versus vegetation structure as measured by the Global Ecosystem Dynamics Investigation (GEDI)

Avian diversity, a key indicator of ecosystem health, is closely related to canopy structure. Most avian diversity models are based on either optical remote sensing or airborne lidar data, but the latter is limited to small study areas. The launch of the Global Ecosystem Dynamics Investigation (GEDI) instrument in 2018 has opened new avenues for exploring the influence of vegetation structure on avian diversity. To examine how direct measurements of canopy structural characteristics explain bird diversity across North America, we analyzed 18 GEDI metrics from 2019 to 2022, along with corresponding Breeding Bird Survey (BBS) counts and AVONET morphological data, analyzing effects across broad regions and at varying spatial extents. We grouped 440 bird species into 20 ecological guilds under six guild categories and employed random forest algorithms to model avian diversity across eight spatial extents (1, 2, 3, 4, 5, 10, 20, and 39.2 km). The models predicted six diversity indices, including species richness (sRich), functional richness (fRich), evenness (fEve), dispersion (fDis), divergence (fDiv), and redundancy (fRed) across eight spatial extents. The best-predicted guilds varied for each diversity index. The most accurate models were sRich (pseudo-R2 = 0.71, RMSE = 4.28) and fRed (pseudo-R2 = 0.60, RMSE = 0.13) for forest specialists guilds; fRich (pseudo-R2 = 0.55, RMSE = 0.18) for urban guilds; fEve (pseudo-R2 = 0.28, RMSE = 0.08) for insectivore guilds; and fDiv (pseudo-R2 = 0.38, RMSE = 0.12) and fDis (pseudo-R2 = 0.53, RMSE = 0.87) for short distance migrants guilds. Our results highlight the critical role of canopy structure, including its horizontal and vertical distribution and variation, in predicting avian diversity, as measured by the mean number of detected modes (num_detectedmodes), the standard deviation of foliage height diversity (FHD), num_detectedmodes, canopy cover, and plant area index (PAI) across the spatial extents centered on BBS routes. Therefore, we recommend incorporating the GEDI metrics into avian diversity modeling and mapping across North America, thereby potentially enhancing bird habitat management and conservation efforts.

Integrative plant area index retrieval and spatiotemporal analysis in Taihu Lake Basin via synergistic active-passive remote sensing techniques

ABSTRACT The Taihu Lake basin is one of the fastest-growing regions in China, where the natural environment has been seriously affected by humans. The plant area index (PAI) is an important parameter reflecting the change in vegetation growth, which plays a crucial role in studying vegetation growth and protecting the ecological environment. Advancements in remote sensing technology, complemented by machine learning techniques, have facilitated the accurate and efficient acquisition of PAI over large areas. In this study, the Taihu Lake Basin vegetation area was taken as the research object. Global Ecosystem Dynamics Investigation (GEDI) point cloud data and Landsat-8 remote sensing images were the primary information sources. MODIS land cover types were utilized to classify the vegetation into six categories. Three classical machine learning models, namely, Random Forest (RF), Support Vector Regression (SVR), and Back Propagation Neural Network (BPNN), were used to estimate the PAI in the Taihu Lake Basin. It was found that the RF model showed the best performance. The determination coefficients (R2) for grassland, evergreen forest, mixed forest, deciduous forest, farmland, and wetland were 0.71, 0.67, 0.69, 0.66, 0.65, and 0.69, respectively. Over 2000-2022, the PAI exhibited an absolute change rate of 0.035, with an overall increasing trend. The area of improved and degraded vegetation accounted for 58.33% and 41.67% of the total area, respectively. The study also revealed that PAI was positively correlated with precipitation (R = 0.64, P < 0.05) and negatively correlated with temperature (R = -0.58, P < 0.05). Different land types’ effects on PAI were also analyzed, with wetland PAI having the smallest mean value and evergreen forest PAI having the most considerable mean value. This research underscores the effectiveness of integrating GEDI data and Landsat-8 imagery in PAI assessment, providing valuable insights for environmental monitoring and analysis in the Taihu Lake Basin.

Characterizing low‐lying coastal upland forests to predict future landward marsh expansion

AbstractSea level rise (SLR) is causing vegetation regime shifts on both the seaward and landward sides of many coastal ecosystems, with the eastern coast of North America experiencing accelerated impacts due to land subsidence and the weakening of the Gulf Stream. Tidal wetland ecosystems, known for their significant carbon storage capacity, are crucial but vulnerable blue carbon habitats. Recent observations suggest that SLR rates may exceed the threshold for elevation gain primarily through vertical accretion in many coastal regions. Therefore, research has focused on mapping the upslope migration of marshes into suitable adjacent lands, as this landward gain may be the most salient process for estimating future wetland resiliency to accelerated rates of SLR. However, our understanding of coastal vegetation characteristics and dynamics in response to SLR is limited due to a lack of in situ data and effective mapping strategies for delineating the boundaries, or ecotones, of these complex coastal ecosystems. In order to effectively study these transitioning ecosystems, it is necessary to employ reliable and scalable landscape metrics that can differentiate between marsh and coastal forests. As such, integrating vegetation structure metrics from light detection and ranging (lidar) could enhance traditional mapping strategies compared to using optical data alone. Here, we used terrestrial laser scanning (TLS) to measure changes in forest structure along elevation gradients that may be indicative of degradation associated with increased inundation in the Delaware Bay estuary. We analyzed a set of TLS‐derived forest structure metrics to investigate their relationships with elevation, specifically seeking those that showed consistent change from the forest edge to the interior. Our findings revealed a consistent pattern between elevation and the plant area index (PAI), a metric that holds potential for enhancing the delineation of complex coastal ecosystem boundaries, particularly in relation to landward marsh migration. This work provides support for utilizing lidar‐derived forest structural metrics to enable a more accurate assessment of future marsh landscapes and the overall coastal carbon sink under accelerated SLR conditions.

A new method for mapping vegetation structure parameters in forested areas using GEDI data

The spatially continuous mapping of vegetation structure parameters in forested areas serves as a crucial foundation for research in various fields, including ecosystem ecology, climate change, hydrology, and forest management and protection. However, there is a notable scarcity of regional scale data regarding vegetation structure parameters. Therefore, our objective was to fill this data gap by providing a methodology for mapping vegetation structure parameters in forested areas at regional-scale. Global Ecosystem Dynamics Investigation (GEDI) provides precise observational data on vegetation structure parameters across the world. Using GEDI vegetation structure parameter point data, we developed a vegetation state coefficient based on the ratio of plant area index (PAI) and vegetation coverage (COVER). Subsequently, we proposed a method for decomposing vegetation coverage to simulate layered vegetation coverage. Taking Jiangxi Province as a case study, we integrated machine learning models to generate a spatially continuous map of vegetation structure parameters in forested areas, including layered coverage and layered leaf area index, in 2020. The map has a spatial resolution of 30 m and a vertical resolution of 5 m. The model exhibits excellent performance. Among the 140 sets of models, approximately 63 % of them achieved an R2 surpassing 0.6, while 89 % of the models achieved a root mean square error (RMSE) below 0.3. This study can serve as a valuable reference for the decomposition of vegetation structure parameters at the regional scale and provide a more precise depiction of the spatial characteristics of vertical vegetation structure at the regional scale. It contributes by providing data support and methodological guidance for research related to forest structure analysis, resource management, and ecological process simulation at the regional scale.

Assessment of Leaf Area Index and its Relationship with Growth and Yield of Fodder Cowpea as Influenced by Sources of Irrigation and Nutrients

Background: Fodder cowpea is an important leguminous, short duration, warm season crop widely cultivated. Though India ranks first in livestock population, the productivity is found lesser than the world average due to deficiency in availability of green fodders. As fresh water application usage is increasing for the cultivation of field crops and high value crops, fodder crops are found negligible crops but responds well to water and nutrients. Leaf area index is one of the determinative parameters for the increased yield, an attempt was made to determine the relationship between the growth and yield of fodder cowpea with the application of domestic sewage water and nutrients. Methods: studies were conducted during 2019 and 2020 to study the relationship between different sources of irrigation water and nutrients on the growth and yield of fodder cowpea. Treatments in main plot consists of irrigation with water using well water (I1), domestic sewage water (I2) and well water and domestic sewage water alternatively (I3). Treatments in sub plot consists of no nutrient (N1), Farm Yard manure alone (N2), Farm Yard manure + NPK (N3) and NPK alone (N4), in split plot design and replicated thrice. The leaf constant was determined graphically plotted in regression and used for calculation of leaf area. The relationship between leaf area index and the growth and yield parameters of fodder cowpea were determined. Result: Our investigation concluded that the application of domestic sewage water along with farmyard manure + NPK significantly increased plant height, number of leaves per plant, leaf length, leaf width, leaf area and leaf area index of fodder cowpea. The data also reveals that leaf area index correlated positively with plant height, number of leaves per plant, biomass per plant and yield of fodder cowpea.

Rainfall interception by urban trees: Event characteristics and tree morphological traits

AbstractThe rapid expansion of impermeable surfaces in cities has a major impact on urban hydrology by reducing rainwater infiltration and increasing runoff rates and peaks. The use of urban trees as stormwater management tools is gaining recognition for their potential to mitigate flood risk and provide additional ecosystem services. We conducted an in‐situ field experiment to measure throughfall on Norway maple (Acer platanoides) and small‐leaved lime (Tilia cordata) to assess the interception capacity of solitary urban trees under different degrees of surface sealing in the city of Freiburg, Germany. We examined the relationships between rainfall characteristics, tree morphological traits, and the interception behaviour, analysing eight trees per species over 76 recorded rainfall events from April to September 2021. Average interception values were higher for small‐leaved lime trees (70.3% ± 6.6%) than for Norway maple (54.8% ± 10.3%) surpassing those in typical forested environments. The average interception loss of all recorded events was 2.6 ± 0.6 mm for Norway maple and 3.7 ± 0.3 mm for small‐leaved lime. For both tree species, significant linear correlations were found between the relative interception and factors such as rainfall depths, the leaf area index (LAI), and the plant area index (PAI) (adj. R2 &gt;0.45). Unlike Norway maple, small‐leaved lime demonstrated significant relationships between several tree morphological parameters and interception (adj. R2 &gt;0.43). The LAI of both species significantly decreased with the increasing degree of surface sealing, which in turn affected interception rates. Our results enhance the understanding of the interception process by solitary trees in urban contexts and enables the parameterization of interception rates using measurable properties. To develop a comprehensive database for modelling parameters and aid urban planners in stormwater management, further field experiments involving various tree species are essential.

Intercomparison of leaf area index observed by destructive sampling, plant canopy analyzer and tracing radiation and architecture of canopies in paddy fields

ABSTRACT Leaf area index (LAI) is a canopy structure parameter used to characterize vegetation growth. It is increasingly important to obtain continuous and accurate ground LAI measurement data for the validation of remote-sensing products. In this study, direct destructive sampling method and indirect optical measurement including LAI-2200 plant canopy analyser (PCA) and Tracing Radiation and Architecture of Canopies (TRAC) were used for long-term continuous observation of paddy fields in southern China, to explore the variation of LAI during the growth period of paddy, and to quantitatively analyse the errors of indirect optical measurement. The results show that (1) LAI and Plant area index (PAI) increased first and then decreased in the growing period. The proportion of green leaf area gradually decreased at reproductive growth stage (less than 50%). There was a linear relationship between the proportion of green leaf area and PAI at this stage, with a good correlation (R2 = 0.73). This linear relationship can be used to convert PAI into LAI more conveniently. (2) Effective PAI (PAIe) calculated by the two methods (2000 method and Lang method) from PCA showed good consistency between each other in the growing season (the maximum value of R2 is 0.99). With the increase of zenith angle, the consistency between 2000 method and Lang method decreases (R2 = 0.83). Multiple scattering has an effect on PCA measurement, and this effect is more obvious at high zenith angle. Lang method is more sensitive to the change of zenith angle. (3) Compared to the destructive values, both 2000 method and Lang method are able to compute accurate LAI using four ring sensors (4 R), while TRAC underestimates the LAI and PAI from destructive sampling method. This study provided a new idea for reducing the impact of ground reference data errors on LAI product validation.

Quantifying the Plant Area Index of Overstory and Understory Vegetation on Sloped Terrain Using Single-Station Terrestrial Laser Scanner

Quantifying the Plant Area Index of Overstory and Understory Vegetation on Sloped Terrain Using Single-Station Terrestrial Laser Scanner

Near-infrared digital hemispherical photography enables correction of plant area index for woody material during leaf-on conditions

Indirect optical measurement techniques enable efficient and non-destructive estimation of plant area index (PAI). However, because they cannot distinguish between foliage and other canopy elements, corrections are needed to determine leaf area index (LAI), which is typically the property of interest. In this study, we investigate near-infrared digital hemispherical photography (DHP) as a means of estimating and correcting for woody material. Using data collected at a deciduous broadleaf forest site, we show that near-infrared DHP could successfully estimate effective wood area index (WAIe) and wood area index (WAI) during leaf-on conditions, providing similar mean values (WAIe = 0.88, WAI = 1.53) to those determined from visible DHP during leaf-off conditions (WAIe = 0.87, WAI = 1.38). This information was used to correct estimates of effective PAI (PAIe) and PAI, enabling effective LAI (LAIe) and LAI to be derived with low RMSD (0.33 for LAIe and 0.76 for LAI), NRMSD (12% for LAIe and 19% for LAI), and bias (−0.01 for LAIe and −0.16 for LAI). Not correcting for woody material led to overestimation of LAIe by 31% on average and 46% in the worst observed case, and the degree of overestimation was further enlarged for LAI (42% on average and 61% in the worst observed case). In agreement with previous studies, the effects of clumping and woody area were found to be partly compensatory. On average, PAIe provided a reasonable approximation of LAI without correction, though overestimation of 52% and underestimation of 20% occurred at the lowest and highest LAI values, respectively. Compared to WAIe and WAI measurement using leaf-off visible DHP, near-infrared DHP offers two crucial advantages: i) data collection can be conducted at the same time as leaf-on PAIe and PAI measurements, and ii) it is likely that the approach could provide an indirect WAIe and WAI measurement option for evergreen species.

Evaluation of Some Growth Parameters of Millet (Pennisetum glaucum (L.) R. Br.) Landraces Cultivated in Al-Mawaset District, Taiz Governorate, Yemen

Pearl Millet (Pennisetum glaucum (L.) R. Br.) is an important crop plant cultivated in many countries, including Yemen, due to its high nutritional value. Landrace, temperatures, and rainfall are crucial factors when it comes to improving the cultivation of crops for the benefit of man and animals. Therefore, the aim of this study was to statistically evaluate the morphological (Plant height, Leaf area, area index and number, and inflorescence length) &amp; physiological (Biomass, Grain Yield, and Seed weight) parameters of 20 Pearl Millet landraces that comply with the amount of rainfall to produce the best grain yielding and/or high biomass production when cultivated in the Al-Mawasit district, Taiz. The Grain Yield of landrace 25508 (mean grain yield of 3.1 t/ha) was found to be greater than the grain yield produced by the other studied landraces. Moreover, landraces 25366 and 25353 were found to give the greatest biomass (mean biomass of 6.7t/ha).

Note on the compatibility of ICOS, NEON, and TERN sampling designs, different camera setups for effective plant area index estimation with digital hemispherical photography

Abstract Environmental monitoring networks such as the Integrated Carbon Observation System (ICOS) in Europe, the National Ecological Observatory Network (NEON) in the U.S., or the Terrestrial Ecosystem Research Network (TERN) in Australia deploy different sampling schemes for in situ measurements. We report on the intercomparison of measurements of the canopy gap fraction with different digital hemispherical photography setups adopting ICOS, NEON, and TERN sampling schemes. The test was carried out at the Järvselja Radiation Transfer Model Intercomparison (RAMI) birch stand. Results show that spreading out sampling points which cover more of the plot is important for a good representation of the forest as a whole. The NEON tower plot layout scheme may be more prone to errors in overall canopy properties estimation than ICOS or TERN due to its compact sampling layout and should always be used in conjunction with its distributed plots. Different camera setups involving different camera operators, camera bodies, lenses and settings yield slightly varied results, and it is important to ensure that the images are taken in such a way that they would not be over or underexposed, or out of focus. As a conclusion we recommend always to carry out intercomparison measurements with old and new cameras when devices are upgraded. Our study contributes towards establishing the uncertainty and evaluating potential error budget stemming from collecting in situ measurements using different sampling schemes and camera setups.

Enhancing leaf area index and biomass estimation in maize with feature augmentation from unmanned aerial vehicle-based nadir and cross-circling oblique photography

Rapid and accurate estimation of plant phenotypes plays a vital role in effective breeding and management of maize crops. Unmanned aerial vehicle (UAV) platforms are emerging as a valuable tool in the assessment of crop phenotypes, offering a promising avenue to enhance the accuracy and efficiency of phenotypic analysis. This study executed two UAV photography methods to acquire aerial images for the estimation of leaf area index (LAI) and above-ground biomass (AGB) in summer maize. Nadir photography was implemented to obtain data from three sources including multi-spectral (MS), RGB, and thermal infrared (TIR) sensors. Additionally, RGB imaging-based cross-circling oblique (CCO) photography was implemented to obtain accurate 3D point cloud data. The nadir photography data was processed to generate orthomosaic image and extract features including vegetation index, canopy cover, canopy height, canopy temperature, and textural information. Features including canopy occupation volume, plant area index, canopy cover, and canopy height were extracted from the CCO photography-derived point cloud data. Furthermore, a data augmentation method, called linear regression-based feature augmentation (LRFA), was proposed for augmenting features extracted from nadir and CCO photography. The results showed that the introduction of CCO photography improved the accuracy of LAI and AGB estimation during the big trumpet stage and LAI estimation during the milk stage compared to the integrated multi-source nadir photography data. Notably, the LRFA derived features outperformed raw features in the majority of modeling scenarios, with the random forest achieved an average accuracy (R2) improvement of 6.1% for LAI estimation and 3.7% for AGB estimation. This study highlights the significance of combining different photography technologies and feature augmentation method for estimating maize phenotypes, providing novel opportunities for crop growth monitoring in modern agriculture.

Growth and Yield of Grain Cowpea (Vigna unguiculata sub sp. cylindrica) in Response to Foliar Nutrition and Graded Levels of Phosphorus and Potassium

Aim: To assess the growth and yield of grain cowpea in response to varied doses of phosphorus (P) and potassium (K) supplemented with foliar application of nano diammonium phosphate (DAP) and potassium salt of active phosphorus (PSAP).&#x0D; Study Design: The field experiment was a factorial experiment, laid out in randomised block design with two levels of P, three levels of K and two sources of foliar nutrition, compared against a control, with three replications.&#x0D; Place and Duration of Study: The study was conducted at the Integrated Farming System Research Station, Karamana, Nedumcaud, Thiruvananthapuram, Kerala, India, during the period from December 2022 to February 2023.&#x0D; Methods: The study utilized PGCP-6 cowpea variety and adopted fertilizer recommendations as per KAU POP (Package of Practices Recommendations of the Kerala Agricultural University). The entire dose of FYM, P and K and half dose of nitrogen (N) were applied as basal dose. The remaining half dose of N was applied at 15 days after sowing (DAS). Additionally, foliar application of nano DAP and PSAP (each @ 0.4 %) was done at 20 DAS and 40 DAS.&#x0D; Results: The results of the study revealed that the treatment combination, p2k1f2 and p2k1f1 had comparable leaf area per plant and leaf area index (LAI) (at 60 DAS). The highest seed yield (1642 kg ha-1) was recorded with p2k1f2. Higher dose of P and K along with foliar application of nano DAP (0.4 % at 20 DAS and 40 DAS) had significant effect on the total DMP and haulm yield.&#x0D; Conclusion: Combined application full dose of N, half dose of P, full dose of K and foliar application of PSAP (0.4 %) at 20 DAS and 40 DAS could be recommended as the best treatment combination for yield enhancement in grain cowpea.

Practical LAI Estimation with DHP Images in Complex Forest Structure with Rugged Terrain

Leaf area index is a key structural parameter for biological and physical processes. Korea is planning to launch CAS500-4 in 2025, so in situ data is needed to validate the leaf area index. Unlike other networks (e.g., NEON and TERN), establishing an elementary sampling unit is difficult in Korea due to the complex forest structure and rugged terrain. Therefore, pixel-level correspondence between the satellite product and fisheye footprints is the best way to verify in complex terrain. In this study, we analyzed the spatial footprint of fisheye lenses in different forest types using terrestrial LiDAR data for the first time. The three-dimensional forest structure was analyzed at various viewing zenith angles, and the footprint radius was approximately 3 m at view zenith angle (VZA) 20° and approximately 10 m at VZA 90°. We also analyzed the Z-values from terrestrial laser data and the plant area index on leafless seasons to assess the impact of obstacles, such as tree trunks, under various viewing zenith angles. The analysis showed that the influence of woody components increases dramatically as the VZA exceeds 40°. Such factors influenced the increase in LAI and the decrease in the clumping index as the VZA increased. Overall, we concluded that narrowing VZA between 20° and 40° is appropriate for Korean forests with complex structures.

Quantifying the influence of tree species richness on community drought resistance using drone-derived NDVI and ground-based measures of Plant Area Index and leaf chlorophyll in a young tree diversity experiment

Tree diversity may buffer the negative impact of drought events according to the diversity insurance hypothesis. During the extreme pan-European drought of 2018, we tested whether tree species richness modulated drought impacts on communities of a young tree diversity experiment in Freiburg, Germany. We utilized drone-based hyperspectral images to capture early symptoms of drought stress indicated by variation in Normalized Difference Vegetation Index (NDVI), and ground-based measures that are indicative of tree canopy function, including Plant Area Index (PAI) and leaf chlorophyll content (Chl). Measures were taken the fifth growing season after planting on six broadleaved and six conifer species originating from Europe and North America in monocultures, two-, four- and six-species mixtures. NDVI decreased successively in response to the summer drought. In contrast to our expectation, tree species richness did not reduce declines in NDVI at the plot level. NDVI values were generally lower in six-species mixtures, compared to monocultures or two-species mixtures, which may be indicative of antagonistic tree species interactions in mixtures of high diversity. Changes in PAI in response to the drought were similar to changes in NDVI; however, modeled differences in PAI between mixtures were not statistically significant. Chl initially decreased, but recovered within a period of two months following the drought. Remotely sensed NDVI facilitated monitoring of the drought response of our large field experiment and could aid in monitoring canopy health in response to extreme drought events. Our observations suggest that NDVI responses are likely more strongly related to leaf shedding within tree canopies than decreases in Chl. Tree stands with a high abundance of deciduous trees may therefore be especially sensitive in triggering NDVI changes in response to drought during the stages leading up to tree mortality. Future studies of drought-related canopy responses should include monitoring of leaf shedding as well as the Chl of shed leaves.

Using airborne LiDAR to map forest microclimate temperature buffering or amplification

Mapping the microclimate effect of forest canopies on understory temperature requires spatially explicit predictors at very fine spatial resolutions. Light Detection And Ranging (LiDAR) offers promising prospects in that regard, as it allows capturing the vertical dimension of vegetation structure at a very high resolution over large areas.To explore the potential of airborne LiDAR-derived metrics to predict understory temperature, we focused on the forest of Blois (France), a 2740-ha lowland managed forest dominated by oak (Quercus petraea). We installed HOBO sensors measuring microclimate air temperature at one-metre height in 53 stands of contrasting vegetation structure, from open to very dense and from young regeneration to mature stages. Using a nearby weather station as the macroclimate temperature reference, we calculated the slope (log scale) coefficient of the linear regression between microclimate and macroclimate, as a simple parameter describing the microclimatic buffering (log(slope) < 0) or amplification (log(slope) > 0) capacity of the habitat.An airborne LiDAR flight was conducted during summer 2021, matching the timing of our temperature measurements. From the resulting 3D point cloud, three complementary metrics of forest structure were derived: the maximum height, the Plant Area Index and the Vertical Complexity Index. They were calculated for circular buffers of different radii (1 m to 100 m) centred on each HOBO sensor.We found that the 5-m radius combining the three metrics into a single multivariate model explained the greatest proportion of variance in the microclimate effect of each stand (R2 = 0.91). We mapped the buffering or amplification effect of vegetation structure on understory temperatures over the entire forest of Blois at a 10-m resolution. 91.4% of the surface of the forest was significantly buffered relative to macroclimate temperature, while 2.7% was amplified, especially in road verges, clear-cut and regeneration areas. Based on our simple linear model, we were able to derive understory air temperature maps for any temporal resolution (i.e. hourly, daily, or seasonal). The results highlight the great capacity of airborne LiDAR to retrieve forest structure parameters and generate high-resolution maps of the thermal environment.Applications for mapping the buffering or amplification of microclimate temperature are plentiful, especially in the context of climate change. They include improving the understanding of physiological processes such as thermoregulation or phenology, modelling the thermal connectivity of the landscape, improving species redistribution models, spotting microrefugia for conservation, informing forest management for tree regeneration or wildfire control, or even prioritising cooling recreational areas for humans to escape heatwaves in urban forests.

Evaluating the Effect of Boron &amp; Molybdenum Micronutrient Application Methods on Growth in Groundnut (Arachis hypogaea L.)

The present investigation was conducted during Rabi 2019 season at the central farm, Regional Research and Technology Transfer Station, Coastal Zone, OUAT, Bhubaneswar. The fresh seeds of groundnut variety Devi (ICGV - 91114) were used as sowing material. The field was laid out in randomized block design with twelve treatments and three replications. The treatments were T1 (Seed priming with Borax solution @ 0.01%), T2 (Seed priming with Borax solution @ 0.05%), T3 (Seed priming with Borax solution @ 0.10%), T4 (Foliar application of Borax @ 100 ppm), T5 (Soil application of Borax @ 10 kg/ha ), T6 (Seed priming with Sodium Molybdate @ 0.25 g/l for 2 hours), T7 (Seed priming with Sodium Molybdate @ 0.50 g/l for 2 hours), T8 (Seed priming with Sodium Molybdate @ 0.75 g/l for 2 hours), T9 (Foliar application of Sodium Molybdate @ 50 ppm), T10 (Soil application of Sodium Molybdate @ 1.5 kg/ha), T11 (T2 + T7) and T12 (Control). The results revealed that different growth phases have shown profound effect. Morphological characters of the T11 showed significant increase in the plant height, number of branches, number of leaves and leaf area. Physiological analysis of treatments revealed T11 significantly higher increment as compare to other treatments in the dry weight of the plant and leaf area index. It was revealed that the treatment T11 has shown as superior performance over other treatments in groundnut.