Optimum Leaf Area Index Research Articles

Non-Destructive Monitoring of Peanut Leaf Area Index by Combing UAV Spectral and Textural Characteristics

The leaf area index (LAI) is a crucial metric for indicating crop development in the field, essential for both research and the practical implementation of precision agriculture. Unmanned aerial vehicles (UAVs) are widely used for monitoring crop growth due to their rapid, repetitive capture ability and cost-effectiveness. Therefore, we developed a non-destructive monitoring method for peanut LAI, combining UAV vegetation indices (VI) and texture features (TF). Field experiments were conducted to capture multispectral imagery of peanut crops. Based on these data, an optimal regression model was constructed to estimate LAI. The initial computation involves determining the potential spectral and textural characteristics. Subsequently, a comprehensive correlation study between these features and peanut LAI is conducted using Pearson’s product component correlation and recursive feature elimination. Six regression models, including univariate linear regression, support vector regression, ridge regression, decision tree regression, partial least squares regression, and random forest regression, are used to determine the optimal LAI estimation. The following results are observed: (1) Vegetation indices exhibit greater correlation with LAI than texture characteristics. (2) The choice of GLCM parameters for texture features impacts estimation accuracy. Generally, smaller moving window sizes and higher grayscale quantization levels yield more accurate peanut LAI estimations. (3) The SVR model using both VI and TF offers the utmost precision, significantly improving accuracy (R2 = 0.867, RMSE = 0.491). Combining VI and TF enhances LAI estimation by 0.055 (VI) and 0.541 (TF), reducing RMSE by 0.093 (VI) and 0.616 (TF). The findings highlight the significant improvement in peanut LAI estimation accuracy achieved by integrating spectral and textural characteristics with appropriate parameters. These insights offer valuable guidance for monitoring peanut growth.

Simulation-Based Study on the Effect of Green Roofs on Summer Energy Performance in Melbourne

Green roofs are increasingly recognised as a crucial urban solution, addressing climate change, enhancing energy efficiency, and promoting sustainable architecture in densely populated areas. In this manuscript, the research study delves into the influence of green roofs on energy consumption, focusing on the Treasury Place building in Melbourne, Australia. The utilisation of DesignBuilder and EnergyPlus simulations was explored. Various green roof parameters such as the Leaf Area Index (LAI), plant height, soil moisture, and tree coverage were optimised and compared against base case scenarios. The key findings indicate an optimal LAI of 1.08 for maximum energy savings, with diminishing returns beyond an LAI of 2.5. The soil moisture content was most effective, around 50%, while a plant height of approximately 0.33 m optimised energy reduction. The introduction of 50% canopy tree coverage provided temperature regulation, but increased soil moisture due to trees and their influence on wind flow had an adverse energy impact. These results emphasise the necessity for precise green roof representation and parameter optimisation for maximum energy efficiency. This research offers essential insights for those in urban planning and building design, endorsing green roofs as a pivotal solution for sustainable urban environments.

Afforestation vegetation uses water from very deep soil layers in the semi‐arid Loess Plateau

AbstractInvestigating the availability of water in very deep soil layers for vegetation is important for assessing the eco‐hydrological effects of the thick loess soil and optimizing soil water management and vegetation restoration initiatives. However, the proportion of soil water sourced from the soil layer deeper than 300 cm by typical afforestation species on the Loess Plateau, along with its seasonal variations, remains unclear. In this study, we examined the composition of the water sources used by two typical afforestation plant species (Caragana korshinskii and Medicago sativa) from soil depths of up to 10 m during the growing season, using isotopic analysis. The results showed that soil desiccation occurred in the soil layers of 260–1000 cm and 260–700 cm at the upper and lower slope positions, respectively, for both species plots. Relative stability of the isotopic values in >260 cm soil layers explained a piston flow occurred in these layers. The contribution proportion of soil water in 260–1000 cm was at 41.7% ± 9.0% and 51.3% ± 8.8% for C. korshinskii on upper and lower slope, respectively and was at 67.6% ± 6.7% and 73.7% ± 7.0% for M. sativa on upper and lower slope, respectively. The serious soil desiccation was induced by the dependence on very deep soil water and the slow recharge with the piston flow. Meanwhile, C. korshinskii converted the main water source between 0–260 cm and 260–1000 cm in different seasons. In contrast, M. sativa used water mainly from soil layers deeper than 260 cm in June and increased the utilization proportion of deeper water sources in July and September. The persistent dependence on very deep soil water for M. sativa, induced a deeper desiccation depth as compared to C. korshinskii. Thus, thinning to the optimal leaf area index and converting to natural grassland were recommended for C. korshinskii and M. sativa plots, respectively to restore the depleted soil water and to promote water cycle.

Influence of Structure and Texture Feature on Retrieval of Ramie Leaf Area Index

Leaf area index (LAI), as one of the important crop field phenotypes, plays a crucial role in evaluating crop growth and yield. The remote sensing method for crop parameters monitoring makes it possible to obtain crop LAI in a large area, efficiently and non-destructively. Fusion of spectral features, structure features, and texture features obtained by remote sensing images can effectively improve LAI estimation performance, but the impact of crop structure and texture feature differences on LAI remote sensing monitoring remains unclear. In this study, ramie was classified according to the differences of canopy structure, and then the LAI inversion performance of ramie was evaluated under different datasets to determine the contribution of structure features (canopy coverage, plant height) and texture features derived from remote sensing images to LAI estimation. Finally, multi-feature remote sensing indexes were integrated to construct the optimal ramie LAI estimation model. The results showed that structure features and texture features had an effect on ramie LAI inversion performance, and there were significant differences in LAI estimation accuracy among different data sets. A single structure feature or texture feature could not significantly optimize LAI inversion performance, while multi-feature fusion could effectively improve LAI estimation accuracy (R2 = 0.776, RMSE = 0.740). This study provides a robust, practical, and low-cost method for ramie LAI estimation, and provides a reference for crop LAI inversion research based on structural features and texture features.

Estimation of the optimal leaf area index (LAI) of an eggplant canopy based on the relationship between the nighttime respiration and daytime photosynthesis of the lowermost leaves

• Optimal leaf area index (LAI opt ) of eggplant was estimated based on carbon balance. • Night respiration (Σ R n ) and day photosynthesis (Σ A ) of lower leaves were correlated. • LAI opt can change considerably depending on solar radiation. • LAI should be maintained lower in winter and higher in summer. In year-round horticultural fruit production, proper management of the leaf area index (LAI) is important to improve fruit yields. This study investigated the optimal LAI (LAI opt ) of an eggplant canopy based on the daily incoming solar radiation (Σ S out ) and the carbon balance of the lowermost leaves. A simple analytical LAI opt model was developed by combining the Beer–Lambert law and a linear relationship between nighttime respiration (Σ R n ) and daytime net photosynthesis (Σ A ). Model parameters were derived from measurements (i.e., measurements of Σ R n and Σ A , the light-photosynthesis curve, and light extinction within the canopy). Σ R n and Σ A were linearly correlated ( R 2 = 0.67) for the lowermost leaves but not for the uppermost leaves ( R 2 = 0.13); the light-photosynthesis curves of the uppermost and lowermost leaves did not differ much under low-light conditions but differed considerably under high-light conditions; and the extinction coefficient of light was estimated to be 0.93. Using the obtained model parameters and publicly available Σ S out data, LAI opt was simulated. The simulation revealed that the LAI opt of an eggplant canopy can vary considerably depending on Σ S out ; a tenfold increase in Σ S out (3.0 to 30 MJ m −2 ) corresponded to a threefold increase in LAI opt (1.2 to 3.7 m 2 m −2 ). The developed model will be useful for obtaining estimates of LAI opt in year-round horticultural fruit production.

Cytogenetics and Crop Improvement Studies in Pigeonpea [Cajanus cajan (L.) Millsp.]: A Review

Pigeonpea has 2n= 2x= 22 chromosomes with length ranging from 5.73±1.15 μm to 10.92±2.69 μm. The largest long arm of chromosome pair (q) was 6.22±1.05 μm, while the shortest measured 3.37±0.83 μm. The longest short arm chromosome pair was 4.70±1.65 μm, while the shortest measured 2.37±0.43 μm. Metacentric and submetacentric chromosomal shapes exist in pigeonpea, but metacentric dominating. Pigeonpea’s karyotypic formula is 2n= 2x= 9m+2 sm. In pigeonpea, regardless of maturity groups, the main and foremost goal is to breed for higher yield/area/time. Resistance to diseases including wilt, sterility mosaic, phytophthora and alternaria blights, as well as insect pests like pod borers and pod flies, is being bred for. Resistance to abiotic stresses like drought tolerance can be achieved by osmotic adjustment (OA), dehydration tolerance and relative water content (RWC). Reduced Na and Cl translocation from root to stem, osmoprotectants and the optimal leaf area index (LAI) for salinity tolerance. Many outstanding varieties has been developed in pigeonpea through germplasm selection, pedigree breeding, mutation breeding and Heterosis breeding.

Exploring Transferable Techniques to Retrieve Crop Biophysical and Biochemical Variables Using Sentinel-2 Data

The current study aimed to determine the spatial transferability of eXtreme Gradient Boosting (XGBoost) models for estimating biophysical and biochemical variables (BVs), using Sentinel-2 data. The specific objectives were to: (1) assess the effect of different proportions of training samples (i.e., 25%, 50%, and 75%) available at the Target site (DT) on the spatial transferability of the XGBoost models and (2) evaluate the effect of the Source site (DS) (i.e., trained) model accuracy on the Target site (i.e., unseen) retrieval uncertainty. The results showed that the Bothaville (DS) → Harrismith (DT) Leaf Area Index (LAI) models required only fewer proportions, i.e., 25% or 50%, of the training samples to make optimal retrievals in the DT (i.e., RMSE: 0.61 m2 m−2; R2: 59%), while Harrismith (DS) →Bothaville (DT) LAI models required up to 75% of training samples in the DT to obtain optimal LAI retrievals (i.e., RMSE = 0.63 m2 m−2; R2 = 67%). In contrast, the chlorophyll content models for Bothaville (DS) → Harrismith (DT) required significant proportions of samples (i.e., 75%) from the DT to make optimal retrievals of Leaf Chlorophyll Content (LCab) (i.e., RMSE: 7.09 µg cm−2; R2: 58%) and Canopy Chlorophyll Content (CCC) (i.e., RMSE: 36.3 µg cm−2; R2: 61%), while Harrismith (DS) →Bothaville (DT) models required only 25% of the samples to achieve RMSEs of 8.16 µg cm−2 (R2: 83%) and 40.25 µg cm−2 (R2: 77%), for LCab and CCC, respectively. The results also showed that the source site model accuracy led to better transferability for LAI retrievals. In contrast, the accuracy of LCab and CCC source site models did not necessarily improve their transferability. Overall, the results elucidate the potential of transferable Machine Learning Regression Algorithms and are significant for the rapid retrieval of important crop BVs in data-scarce areas, thus facilitating spatially-explicit information for site-specific farm management.

Estimation of the Optimal Leaf Area Index (Lai) of an Eggplant Canopy Based on the Relationship between the Nighttime Respiration and Daytime Photosynthesis of the Lowermost Leaves

Estimation of the Optimal Leaf Area Index (Lai) of an Eggplant Canopy Based on the Relationship between the Nighttime Respiration and Daytime Photosynthesis of the Lowermost Leaves

Analysis of changes in topdressing application effect on rice by NDVI using hierarchical Bayesian model

AbstractAlthough many studies have investigated the relationship between rice (Oryza sativa L.) growth and normalized difference vegetation index (NDVI), few have used it as a practical indicator to determine whether topdressing application is necessary. Therefore, in this study, to verify the usefulness of NDVI as a fertilizer application indicator, changes of topdressing effect on rice depending on the NDVI immediately before application were analyzed using hierarchical Bayesian model. When NDVI was low, topdressing application 45–50 d after transplantation greatly improved yield and quality. However, when NDVI was high, the effect of topdressing was scarce. Since the timing of topdressing application in this study is highly effective in increasing leaf area and number of panicles, which was a positive effect when NDVI was low and both sink and source capacity were insufficient, but when NDVI was high, topdressing application would exceed the optimum leaf area index and could not improve source capacity, so there would be little benefit from topdressing application. Sales of rice was simulated using the posterior distribution of parameters and it was considered worthwhile to apply topdressing when NDVI was <0.75. In addition, weather effects were extracted using the posterior distribution of random effects. As a result, the random effects reflected the effects of weather fairly accurately, suggesting that the effects of other factors could be eliminated. In this study, NDVI was found to be useful as an indicator to determine whether topdressing application is needed. In addition, these analytical methods are useful for getting more information from less data.

Селекционные пути оптимизации структуры листового аппарата сои в засушливых регионах

Water supply is one of the key factors limiting soybean yield. Coming from the monsoon climate region, soybean lacks effective means of leaf surface growth restriction and is prone to produce excessive leaf area that leads to undesirable transpiration increase. Reducing branching rate and, correspondingly, leaf number per plant is usually proposed to decrease leaf area. However, as far as branching ability is generally a useful trait contributing to yield stability, we have undertaken a search for possible alternative ways of leaf area reduction. Soybean canopy structure was studied in our germplasm nursery in Kursk region. We have updated an express method of soybean trifoliate leaf surface calculation. A regression index for soybean trifoliate leaf surface by central leaflet length and width product characterizes leaflet shape and depends from its length to width ratio. In the sampling studied, trifoliate leaf surface varied from 79 to 150sq. cm. Leaf area index (LAI) varied from 4.0 to 8.6 sq. m/sq. m, with optimal LAI equal to 6.0 sq. m/sq. m. Excessive LAI (over 7.7 sq. m/sq. m) decreased yield by 20 %. Optimal LAI may be achieved by various combinations of leaf size and leaf number per plant. Lines possessing good branching rate but remaining within optimal LAI values due to small leaf size were revealed. Thus, lamina size reduction may be proposed as an alternative breeding direction to solve a conflict of bushy plant type and drought tolerance.

Response of sweet potato yield components to stakes angle and mulch type: Sweet potato cultivation in the Papua highlands

This study aims to improve the ability of sweet potato plants to obtain photosynthetic active radiation through a combination of stake angle with mulch as a reflector. The experiments were arranged in a separate plot design with three replications. The main plot consisted of Cangkuang variety with 90° stakes and Cangkuang varieties with 60° stakes. The subplots consisted of no mulch, straw mulch, white sand mulch, clear plastic mulch, and black silver plastic mulch. The results showed that the use of mulch as a reflector on both stake angle was able to increase photosynthesis active radiation by 27.84% to 34.63% compared to without mulch at 16.82%, and the optimum leaf area index at the age of 100 DAP was 3.74 to 4.45 at stake angle of 90° and 3.23 to 4.10 at stake angle of 60°. The number of tubers per plant increased and reached the highest of 3.17 and 4.50 in straw mulch, the number of marketable tubers from 51.90% to 59.23% compared to without mulch of 41.97% for stakes angle of 90° and 44.67% to 65.83% compared without mulch of 32.47% for stakes angle of 60°.

Optimizing Grain Yield and Water Use Efficiency Based on the Relationship between Leaf Area Index and Evapotranspiration

Achieving optimal balance between maize yield and water use efficiency is an important challenge for irrigation maize production in arid areas. In this study, we conducted an experiment in Xinjiang China in 2016 and 2017 to quantify the response of maize yield and water use to plant density and irrigation schedules. The treatments included four irrigation levels: 360 (W1), 480 (W2), 600 (W3), and 720 mm (W4), and five plant densities: 7.5 (D1), 9.0 (D2), 10.5 (D3), 12.0 (D4), and 13.5 plants m−2 (D5). The results showed that increasing the plant density and the irrigation level could both significantly increase the leaf area index (LAI). However, LAI expansion significantly increased evapotranspiration (ETa) under irrigation. The combination of irrigation level 600 mm (W3) and plant density 12.0 plants m−2 (D4) produced the highest maize yield (21.0–21.2 t ha−1), ETa (784.1–797.8 mm), and water use efficiency (WUE) (2.64–2.70 kg m−3), with an LAI of 8.5–8.7 at the silking stage. The relationship between LAI and grain yield and evapotranspiration were quantified, and, based on this, the relationship between water use and maize productivity was analyzed. Moreover, the optimal LAI was established to determine the reasonable irrigation level and coordinate the relationship between the increase in grain yield and the decrease in water use efficiency.

Does economic optimisation explain LAI and leaf trait distributions across an Amazon soil moisture gradient?

Leaf area index (LAI) underpins terrestrial ecosystem functioning, yet our ability to predict LAI remains limited. Across Amazon forests, mean LAI, LAI seasonal dynamics and leaf traits vary with soil moisture stress. We hypothesise that LAI variation can be predicted via an optimality-based approach, using net canopy C export (NCE, photosynthesis minus the C cost of leaf growth and maintenance) as a fitness proxy. We applied a process-based terrestrial ecosystem model to seven plots across a moisture stress gradient with detailed in situ measurements, to determine nominal plant C budgets. For each plot, we then compared observations and simulations of the nominal (i.e. observed) C budget to simulations of alternative, experimental budgets. Experimental budgets were generated by forcing the model with synthetic LAI timeseries (across a range of mean LAI and LAI seasonality) and different leaf trait combinations (leaf mass per unit area, lifespan, photosynthetic capacity and respiration rate) operating along the leaf economic spectrum. Observed mean LAI and LAI seasonality across the soil moisture stress gradient maximised NCE, and were therefore consistent with optimality-based predictions. Yet, the predictive power of an optimality-based approach was limited due to the asymptotic response of simulated NCE to mean LAI and LAI seasonality. Leaf traits fundamentally shaped the C budget, determining simulated optimal LAI and total NCE. Long-lived leaves with lower maximum photosynthetic capacity maximised simulated NCE under aseasonal high mean LAI, with the reverse found for short-lived leaves and higher maximum photosynthetic capacity. The simulated leaf trait LAI trade-offs were consistent with observed distributions. We suggest that a range of LAI strategies could be equally economically viable at local level, though we note several ecological limitations to this interpretation (e.g. between-plant competition). In addition, we show how leaf trait trade-offs enable divergence in canopy strategies. Our results also allow an assessment of the usefulness of optimality-based approaches in simulating primary tropical forest functioning, evaluated against in situ data.

Leaf Removal Affects Maize Morphology and Grain Yield

Increasing planting density is an important practice associated with increases in maize yield, but densely planted maize can suffer from poor light conditions. In our two-year field experiments, two morphologically different cultivars, ZD958 (less compact) and DH618 (more compact), were planted at 120,000 plants ha−1 and 135,000 plants ha−1, respectively. We established different leaf area index (LAI) treatments by removing leaves three days after silking: (1) control, no leaves removed (D0); (2) the two uppermost leaves removed (D1); (3) the four uppermost leaves removed (D2); (4) the leaves below the third leaf below the ear removed (D3); (5) the leaves of D1 and D3 removed (D4); (6) the leaves of D2 and D3 removed (D5). Optimal leaf removal improved light distribution, increased photosynthetic capacity and the post-silking source-sink ratio, and thus the grain yield, with an average LAI of 5.9 (5.6 and 6.2 for ZD958 and DH618, respectively) for the highest yields in each year. Therefore, less-compact cultivars should have smaller or fewer topmost leaves or leaves below the ear that quickly senesce post-silking, so as to decrease leaf area and thus improve light distribution and photosynthetic capacity in the canopy under dense planting conditions. However, for more compact cultivars, leaves below the ear should senesce quickly after silking to reduce leaf respiration and improve the photosynthetic capacity of the remaining top residual leaves. In future maize cultivation, compact cultivars with optimal post-silking LAI should be adopted when planting densely.

Dynamic change in rice leaf area index and spectral response under flooding stress

Analysis of the canopy structure change and spectral response mechanism of rice under flooding stress is an important prerequisite for large-scale monitoring of rice flooding disasters. The leaf area index (LAI) was used as the characterization indicator. The response rule of the canopy spectrum to flooding stress intensity was analyzed. The sensitive spectral characteristic parameters were screened to construct the LAI spectral response model of rice under flooding stress. The results showed that the rice LAI under flooding stress decreased with an increase in waterlogging depth. The spectral reflectance of the rice canopy under flooding stress significantly changed in the near-infrared band and decreased with an increase in waterlogging depth. In 680–760 nm, the double peak in the first-order differential spectrum of the rice canopy was more obvious with advancement of the growth process and a multiple peak appeared during the late growth stage. The blueshift of the red edge parameters was the most obvious in the submerged top during the tillering stage. A power function regression model based on the ratio of the first-order differential spectral amplitude at 737 nm to 719 nm in the red edge range was the optimal LAI response model for rice under flooding stress. A field waterlogging experiment was used to simulate and analyze the influence of flooding on the rice canopy structure and the canopy spectral response rule, providing a reference for the subsequent analysis of rice growth and disaster loss assessment under flooding stress.

Optimum Leaf Area Index to Reach Soybean Yield Potential in Subtropical Environment

Core Ideas The optimum leaf area index (LAI) at beginning bloom to reach the yield potential is between 3.6 (indeterminate stem termination) and 4.5 (determinate stem termination).The optimum LAI maximum to reach the yield potential is between 6.0 and 6.5 for indeterminate and determined cultivars.The optimum LAI for high yield potential should be adjusted according to the maturity group, stem termination and sowing date.The increase in yield was observed with the increase of LAI for experiments and farms with a high technological level (soybean yield more than 4.5 Mg ha−1). The interactive influence of climate and management factors on leaf area index (LAI) of soybean yield potential has not been investigated in subtropical environments. Such information can help in optimizing current soybean management practices to increase seed yield and efficiency of resource use. The objective of this study was to identify the optimum leaf area index for attaining yield potential in modern soybean cultivars grown in a subtropical environment. A large soybean yield, LAI, and phenology database collected from a combination of on‐farm and research‐station experiments conducted during four soybean growing seasons (2011–2015) in southern Brazil was used. The database represented a wide range of weather conditions, soil types, sowing dates, and cultivar maturity groups (MGs). A boundary function was derived for the relationship between soybean yield and LAI at R1 and R5 developmental stages for cultivars of determinate and indeterminate growth habit, and for both types combined. An increase in yield was in line with the increase of LAI for experiments with a small yield gap (soybean yield more than 4.5 Mg ha−1). In this case, optimum LAI at R1 growth stage (LAIR1, opt) to reach the soybean yield potential was between 3.4 (indeterminate) and 4.5 (determinate) whereas the optimal maximum LAI (LAImax,opt) is between 6.0 and 6.5 for indeterminate and determinate cultivars, respectively. These results suggested that management practices for soybean crops that involve the LAI and high yield potential must be adjusted according to the maturity group, growth habit and sowing date in a subtropical environment.

Canopy Development of Annual Legumes and Forage Sorghum Intercrops and Its Relation to Dry Matter Accumulation

Core Ideas Canopy and light interception characteristics of forage sorghum and sorghum‐legume systems were evaluated.Intercropping with sorghum increased canopy coverage and light interception early in the growing season.Intercropping legumes with forage sorghum increased LAI compared with sole forage sorghum.LAI was an accurate growth predictor of light interception in sole sorghum and all sorghum‐legume systems. Livestock production is an important agro‐industry in many semiarid regions of the world including the southern High Plains, USA. Therefore, objectives of this study were to understand how canopy development and light interception patterns impact biomass yield of several legume species when intercropped with forage sorghum [Sorghum bicolor (L.) Moench] grown under irrigation. Field studies were conducted at Tucumcari and Clovis, NM in 2008 and 2009, respectively. Five annual legumes [cowpea (Vigna unguiculata L.), pigeon pea (Cajanus cajan L.), lima bean (Phaseolus lunatus L.), lablab (Lablab purpureus L.), and pole bean (P. vulgaris L.)] were intercropped between two rows of sorghum spaced at 0.75 m and compared with sole sorghum under irrigation. Intercropping legumes with sorghum increased leaf area index (LAI) compared with sorghum alone, at both locations. Intercropping legumes with sorghum resulted in faster canopy coverage and greater light interception early in the growing season compared with sorghum alone. Lablab, cowpea, lima bean, and pole bean were promising legumes to improve light interception when intercropping with sorghum. The LAI was an accurate growth predictor of light interception in all intercropping systems and sorghum alone (R2 > 0.94). Overall, optimum LAI (90% light interception) was lower in all intercropping systems compared with sorghum alone. Dry matter accumulation had strong correlations with LAI and light interception at both locations before canopy closure (58 d after planting). Overall, intercropping legumes in forage sorghum benefits producers by providing protein to livestock and reduces the need to purchase dietary supplements in their traditional livestock production systems.

Estimation and Mapping of Winter Oilseed Rape LAI from High Spatial Resolution Satellite Data Based on a Hybrid Method

Leaf area index (LAI) is a key input in models describing biosphere processes and has widely been used in monitoring crop growth and in yield estimation. In this study, a hybrid inversion method is developed to estimate LAI values of winter oilseed rape during growth using high spatial resolution optical satellite data covering a test site located in southeast China. Based on PROSAIL (coupling of PROSPECT and SAIL) simulation datasets, nine vegetation indices (VIs) were analyzed to identify the optimal independent variables for estimating LAI values. The optimal VIs were selected using curve fitting methods and the random forest algorithm. Hybrid inversion models were then built to determine the relationships between optimal simulated VIs and LAI values (generated by the PROSAIL model) using modeling methods, including curve fitting, k-nearest neighbor (kNN), and random forest regression (RFR). Finally, the mapping and estimation of winter oilseed rape LAI using reflectance obtained from Pleiades-1A, WorldView-3, SPOT-6, and WorldView-2 were implemented using the inversion method and the LAI estimation accuracy was validated using ground-measured datasets acquired during the 2014–2015 growing season. Our study indicates that based on the estimation results derived from different datasets, RFR is the optimal modeling algorithm amidst curve fitting and kNN with R2 > 0.954 and RMSE <0.218. Using the optimal VIs, the remote sensing-based mapping of winter oilseed rape LAI yielded an accuracy of R2 = 0.520 and RMSE = 0.923 (RRMSE = 93.7%). These results have demonstrated the potential operational applicability of the hybrid method proposed in this study for the mapping and retrieval of winter oilseed rape LAI values at field scales using multi-source and high spatial resolution optical remote sensing datasets. Details provided by this high resolution mapping cannot be easily discerned at coarser mapping scales and over larger spatial extents that usually employ lower resolution satellite images. Our study therefore has significant implications for field crop monitoring at local scales, providing relevant data for agronomic practices and precision agriculture.

Growth of sesame (Sesamum indicum L.) as affected by nitrogen and sulfur under semiarid climate

Field trail was conducted during kharif season, 2013 at Agronomy research farm the University of Agriculture, Peshawar to determine the effect of nitrogen and sulfur on the growth of sesame. The study was conducted in RCB design (control vs. rest) having three replications. Nitrogen (N) levels viz. 30, 70,110 and 150 and S levels viz. 10, 20 and 30 were used. Data were documented on Days to emergence, emergence m-2, days to flowering, leaf area, leaf area index, plant height, days to maturity and plant at harvest. It was found from these results that higher leaf area plant-1 (384.2 cm2) were gained from the plot that received 70 kg N ha-1, while minimum (366 cm2) were achieved from control plots. The application of N at the rate of 70 kg ha-1 resulted in optimum leaf area index (2.2), while control plots have lower leaf area index (1.95). Taller plants (187.1 cm) were observed in plot treated with 70 kg N ha-1 and dwarf plants (169 cm) were seen in control plots. In case of S the plant height increases up to (185.2 cm) in the plots that received 20 kg S ha-1which was statistically comparable to 30 kg S ha-1 against control. Plants at harvest were not influenced significantly by any of the factor and control vs. rest. Days to emergence, Emergence m-2 was non-significantly affected by nitrogen and sulfur. Yield and yield components were significantly improved by the sole addition of N and S or in combination. Sesame production enhances and profitable with application of N and S at the rate of 70 and 20 kg ha-1 respectively in this study. Keywords: Sesame; Nitrogen; Sulfur; RCB http://dx.doi.org/10.19045/bspab.2016.50164

MERIS GPP/NPP product for Estonia: II. Complex meteorological limiting factor and optimum leaf area index / MERIS’e GPP/NPP tulem Eesti jaoks: II. Kompleksne meteoroloogiline piirangutegur ja optimaalne lehepinnaindeks

Abstract. The merits and possible problems of the light use efficiency-concept based GPP/NPP models applied together with satellite images and meteorological data to quantitatively understand the role of different meteorological factors in forest productivity are analysed. A concept of the complex meteorological limiting factor for plant productivity is introduced. The factor includes the effects of incoming photosynthetically active radiation as well as the temperature and water limiting factors. Climatologically averaged seasonal courses of the complex meteorological limiting factor derived from the records of two contrasting meteorological stations in Estonia - inland Tartu/Tõravere and coastal Sõrve - are shown. Leaf phenology, here described via the seasonal course of leaf area index (LAI), is interpreted as a possible means to maximise the carbon gain under particular meteorological conditions. The equations for the optimum seasonal course of LAI as derived from the NPP model are presented. As the daily adjustment of plant LAI to sudden changes in meteorological conditions is not possible, several approximate strategies for LAI seasonal course to maximise the yearly NPP of vegetation are analysed. Typical optimal courses of LAI show some seasonal asymmetry resulting in lower values of LAI in the second half of the vegetation period due to higher air temperatures and respiration costs. Knowledge about optimum LAI courses has a cognitive value, but can also be used as the simulated LAI courses in several models when the measured LAI values are not available. As the considered GPP/NPP models fail to adequately describe the local trends in forest and agricultural productivity in Estonia, the ways to improve the model’s performance are shown.