A simple and non-destructive model for individual leaf area estimation in citrus

Introduction. Leaf area is often related to plant growth, development, physiology and yield. Many non-destructive models have been proposed for leaf area estimation of several plant genotypes, demonstrating that leaf length, leaf width and leaf area are closely correlated. Thus, the objective of our study was to develop a reliable model for leaf area estimation from linear measurements of leaf dimensions for citrus genotypes. Materials and methods. Leaves of citrus genotypes were harvested, and their dimensions (length, width and area) were measured. Values of leaf area were regressed against length, width, the square of length, the square of width and the product (length x width). The most accurate equations, either linear or second-order polynomial, were regressed again with a new data set; then the most reliable equation was defined. Results and discussion. The first analysis showed that the variables length, width and the square of length gave better results in second-order polynomial equations, while the linear equations were more suitable and accurate when the width and the product (length x width) were used. When these equations were regressed with the new data set, the coefficient of determination (R(2)) and the agreement index 'd' were higher for the one that used the variable product (length x width), while the Mean Absolute Percentage Error was lower. Conclusion. The product of the simple leaf dimensions (length x width) can provide a reliable and simple non-destructive model for leaf area estimation across citrus genotypes.

Non-destructive linear model for leaf area estimation in Vernonia ferruginea Less

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Grapevine Yield and Leaf Area Estimation Using Supervised Classification Methodology on RGB Images Taken under Field Conditions

The aim of this research was to implement a methodology through the generation of a supervised classifier based on the Mahalanobis distance to characterize the grapevine canopy and assess leaf area and yield using RGB images. The method automatically processes sets of images, and calculates the areas (number of pixels) corresponding to seven different classes (Grapes, Wood, Background, and four classes of Leaf, of increasing leaf age). Each one is initialized by the user, who selects a set of representative pixels for every class in order to induce the clustering around them. The proposed methodology was evaluated with 70 grapevine (V. vinifera L. cv. Tempranillo) images, acquired in a commercial vineyard located in La Rioja (Spain), after several defoliation and de-fruiting events on 10 vines, with a conventional RGB camera and no artificial illumination. The segmentation results showed a performance of 92% for leaves and 98% for clusters, and allowed to assess the grapevine’s leaf area and yield with R2 values of 0.81 (p < 0.001) and 0.73 (p = 0.002), respectively. This methodology, which operates with a simple image acquisition setup and guarantees the right number and kind of pixel classes, has shown to be suitable and robust enough to provide valuable information for vineyard management.

Estimation of leaf area in grapevine cv. Syrah using empirical models

Aiming to obtain empirical models for the estimation of Syrah leaf area a set of 210 fruiting shoots was randomly collected during the 2013 growing season in an adult experimental vineyard, located in Lisbon, Portugal. Samples of 30 fruiting shoots were taken periodically from the stage of inflorescences visible to veraison (7 sampling dates). At the lab, from each shoot, primary and lateral leaves were separated and numbered according to node insertion. For each leaf, the length of the central and lateral veins was recorded and then the leaf area was measured by a leaf area meter. For single leaf area estimation the best statistical models uses as explanatory variable the sum of the lengths of the two lateral leaf veins. For the estimation of leaf area per shoot it was followed the approach of Lopes & Pinto (2005), based on 3 explanatory variables: number of primary leaves and area of the largest and smallest leaves. The best statistical model for estimation of primary leaf area per shoot uses a calculated variable obtained from the average of the largest and smallest primary leaf area multiplied by the number of primary leaves. For lateral leaf area estimation another model using the same type of calculated variable is also presented. All models explain a very high proportion of variability in leaf area. Our results confirm the already reported strong importance of the three measured variables (number of leaves and area of the largest and smallest leaf) as predictors of the shoot leaf area. The proposed models can be used to accurately predict Syrah primary and secondary leaf area per shoot in any phase of the growing cycle. They are inexpensive, practical, non-destructive methods which do not require specialized staff or expensive equipment.

Determination of Merremia cissoides leaf area using the linear measures of the leaflets

O conhecimento da área foliar de plantas daninhas pode auxiliar o estudo das relações de interferência entre elas e as culturas agrícolas. O objetivo desta pesquisa foi determinar uma equação matemática que estime a área foliar de Merremia cissoides, a partir da relação entre as dimensões lineares dos limbos foliares. Folhas da espécie foram coletadas de diferentes locais na Universidade Estadual Paulista, Jaboticabal, Estado de São Paulo, Brasil, medindo-se o comprimento (C), a largura máxima (L) e a área foliar de três tipos de folíolos. Foram estimadas equações lineares Y = a x (X) para cada tipo de folíolo. Houve sobreposição dos intervalos de confiança das equações dos folíolos primário e secundário, por isso considerou-se uma única equação da média desses folíolos, além da equação do folíolo principal, para caracterização da área foliar de M. cissoides. Assim, a área foliar dessa espécie pode ser estimada pelo somatório das áreas dos limbos foliares dos folíolos principal e primário + secundário, por meio da equação AFnest = 0,501 x (X) + 2,181 x (Z), em que X indica C x L do folíolo principal e Z indica C x L médios dos folíolos primário + secundário, respectivamente.

Leaf area index estimation with MODIS reflectance time series and model inversion during full rotations of Eucalyptus plantations

The leaf area index (LAI) of fast-growing Eucalyptus plantations is highly dynamic both seasonally and interannually, and is spatially variable depending on pedo-climatic conditions. LAI is very important in determining the carbon and water balance of a stand, but is difficult to measure during a complete stand rotation and at large scales. Remote-sensing methods allowing the retrieval of LAI time series with accuracy and precision are therefore necessary. Here, we tested two methods for LAI estimation from MODIS 250m resolution red and near-infrared (NIR) reflectance time series. The first method involved the inversion of a coupled model of leaf reflectance and transmittance (PROSPECT4), soil reflectance (SOILSPECT) and canopy radiative transfer (4SAIL2). Model parameters other than the LAI were either fixed to measured constant values, or allowed to vary seasonally and/or with stand age according to trends observed in field measurements. The LAI was assumed to vary throughout the rotation following a series of alternately increasing and decreasing sigmoid curves. The parameters of each sigmoid curve that allowed the best fit of simulated canopy reflectance to MODIS red and NIR reflectance data were obtained by minimization techniques. The second method was based on a linear relationship between the LAI and values of the GEneralized Soil Adjusted Vegetation Index (GESAVI), which was calibrated using destructive LAI measurements made at two seasons, on Eucalyptus stands of different ages and productivity levels. The ability of each approach to reproduce field-measured LAI values was assessed, and uncertainty on results and parameter sensitivities were examined. Both methods offered a good fit between measured and estimated LAI (R(2) = 0.80 and R(2) = 0.62 for model inversion and GESAVI-based methods, respectively), but the GESAVI-based method overestimated the LAI at young ages. (C) 2010 Elsevier Inc. All rights reserved.

Calibration of a Species-Specific Spectral Vegetation Index for Leaf Area Index (LAI) Monitoring: Example with MODIS Reflectance Time-Series on Eucalyptus Plantations

The leaf area index (LAI) is a key characteristic of forest ecosystems. Estimations of LAI from satellite images generally rely on spectral vegetation indices (SVIs) or radiative transfer model (RTM) inversions. We have developed a new and precise method suitable for practical application, consisting of building a species-specific SVI that is best-suited to both sensor and vegetation characteristics. Such an SVI requires calibration on a large number of representative vegetation conditions. We developed a two-step approach: (1) estimation of LAI on a subset of satellite data through RTM inversion; and (2) the calibration of a vegetation index on these estimated LAI. We applied this methodology to Eucalyptus plantations which have highly variable LAI in time and space. Previous results showed that an RTM inversion of Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared and red reflectance allowed good retrieval performance (R-2 = 0.80, RMSE = 0.41), but was computationally difficult. Here, the RTM results were used to calibrate a dedicated vegetation index (called "EucVI") which gave similar LAI retrieval results but in a simpler way. The R-2 of the regression between measured and EucVI-simulated LAI values on a validation dataset was 0.68, and the RMSE was 0.49. The additional use of stand age and day of year in the SVI equation slightly increased the performance of the index (R-2 = 0.77 and RMSE = 0.41). This simple index opens the way to an easily applicable retrieval of Eucalyptus LAI from MODIS data, which could be used in an operational way.

Sugarcane leaf area estimate obtained from the corrected normalized difference vegetation index (NDVI)

v. 46, n. 2, p. 140-148, apr./jun. 2016.

The potential for increasing rubber production by matching tapping intensity to leaf area index

Understanding resource capture can help design appropriate species combinations, planting designs and management. Leaf area index (LAI) and its longevity are the most important factors defining dry matter production and thus growth and productivity. The ecophysiological modifications and yield of rubber (Hevea spp.) in an agroforestry system (AFS) with beans (Phaseolus vulgaris L.) were studied. The experiment was established in Southeast-Brazil, with three rubber cultivars: IAN 3087, RRIM 600 and RRIM 527. The AFS comprised double rows of rubber trees along with beans sown in autumn and winter seasons in 1999. There was about 50% higher rubber yield per tree in the AFS than the rubber monoculture. Trees within the AFS responded to higher solar radiation availability with higher LAI and total foliage area, allowing its greater interception. All three cultivars had higher LAI in the AFS than monoculture, reaching maximum values in the AFS between April and May of 3.17 for RRIM 527; 2.83 for RRIM 600 and 2.28 for IAN 3087. The maximum LAI values for monocrop rubber trees were: 2.65, 2.62 and 1.99, respectively, for each cultivar. Rubber production and LAI were positively correlated in both the AFS and monoculture but leaf fall of rubber trees in the AFS was delayed and total phytomass was larger. It is suggested that trees in the AFS were under exploited and could yield more without compromising their life cycle if the tapping system was intensified. This shows how knowledge of LAI can be used to manage tapping intensity in the field, leading to higher rubber yield.

Within-stand and seasonal variations of specific leaf area in a clonal Eucalyptus plantation in the Republic of Congo

Specific leaf area (SLA; m(leaf)(2) kg(leaf)(-1)) is a key ecophysiological parameter influencing leaf physiology, photosynthesis, and whole plant carbon gain. Both individual tree-based models and other forest process-based models are generally highly sensitive to this parameter, but information on its temporal or within-stand variability is still scarce. In a 2-4-year-old Eucalyptus plantation in Congo, prone to seasonal drought, the within-stand and seasonal variability in SLA were investigated by means of destructive sampling carried out at 2-month intervals, over a 2-year period. Within-crown vertical gradients of SLA were small. Highly significant relationships were found between tree-average SLA (SLA(t)) and tree size (tree height, H(t), or diameter at breast height, DBH): SLA(t) ranged from about 9 m(2) kg(-1) for dominant trees to about 14-15 m(2) kg(-1) for the smallest trees. The decrease in SLA(t) with increasing tree size was accurately predicted from DBH using power functions. Stand-average SLA varied by about 20% during the year, with lowest values at the end of the 5-month dry season, and highest values about 2-3 months after the onset of the wet season. Variability in leaf water status according to tree size and season is discussed as a possible determinant of both the within-stand and seasonal variations in SM. (C) 2009 Elsevier B.V. All rights reserved.

LEAF AREA REDUCTION IN CORN GROWN IN A TROPICAL REGION OF BRAZIL AND ITS EFFECTS ON AGRONOMIC TRAITS

Global climate change may reduce leaf area in crop plants due to factors such as increasing occurrence of pests and diseases. The aim of this work was to estimate the impact of leaf area reduction on agronomic traits in corn. An experiment simulating leaf area reduction was carried out in a tropical region of Brazil. The agronomic performance of corn plants was evaluated at different percentages of leaf loss. It was observed that leaf area reductions over 41.01% significantly harm yield, mass of 1000 grains, cob density, and stem and root quality Crop improvement programs should take into account the development of genotypes resistant to factors that cause leaf area reduction in tropical crops.

Improved methods for predicting individual leaf area and leaf senescence in maize (Zea mays)

The ability to predict leaf area and leaf area index is crucial in crop simulation models that predict crop growth and yield. Previous studies have shown existing methods of predicting leaf area to be inadequate when applied to a broad range of cultivars with different numbers of leaves. The objectives of the study were to (i) develop generalised methods of modelling individual and total plant leaf area, and leaf senescence, that do not require constants that are specific to environments and/or genotypes, (ii) re-examine the base, optimum, and maximum temperatures for calculation of thermal time for leaf senescence, and (iii) assess the method of calculation of individual leaf area from leaf length and leaf width in experimental work. Five cultivars of maize differing widely in maturity and adaptation were planted in October 1994 in south-eastern Queensland, and grown under non-limiting conditions of water and plant nutrient supplies. Additional data for maize plants with low total leaf number (12-17) grown at Katumani Research Centre, Kenya, were included to extend the range in the total leaf number per plant. The equation for the modified (slightly skewed) bell curve could be generalised for modelling individual leaf area, as all coefficients in it were related to total leaf number. Use of coefficients for individual genotypes can be avoided, and individual and total plant leaf area can be calculated from total leaf number. A single, logistic equation, relying on maximum plant leaf area and thermal time from emergence, was developed to predict leaf senescence. The base, optimum, and maximum temperatures for calculation of thermal time for leaf senescence were 8, 34, and 40 degrees C, and apply for the whole crop-cycle when used in modelling of leaf senescence. Thus, the modelling of leaf production and senescence is simplified, improved, and generalised. Consequently, the modelling of leaf area index (LAI) and variables that rely on LAI will be improved. For experimental purposes, we found that the calculation of leaf area from leaf length and leaf width remains appropriate, though the relationship differed slightly from previously published equations.

Does maintaining green leaf area in sorghum improve yield under drought? I. Leaf growth and senescence

Production of sorghum [Sorghum bicolor (L.) Moench], an important cereal crop in semiarid regions of the world, is often limited by drought. When water is limiting during the grain-filling period, hybrids possessing the stay-green trait maintain more photosynthetically active leaves than hybrids not possessing this trait. To improve yield under drought, knowledge of the extent of genetic variation in green leaf area retention is required. Field studies were undertaken in north-eastern Australia on a cracking and self-mulching gray clay to determine the effects of water regime and hybrid on the components of green leaf area at maturity (GLAM). Nine hybrids varying in stay-green were grown under a fully irrigated control, postflowering water deficit, and terminal (pre- and postflowering) water deficit. Water deficit reduced GLAM by 67% in the terminal drought treatment compared with the fully irrigated control. Under terminal water deficit, hybrids possessing the B35 and KS19 sources of stay-green retained more GLAM (1260 cm(2) plant(-1)) compared with intermediate (780 cm(2) plant(-1)) and senescent (670 cm(2) plant(-1)) hybrids. RQL12 hybrids (KS19 source of stay-green) displayed delayed onset and reduced rate of senescence; A35 hybrids displayed only delayed onset. Visual rating of green leaf retention was highly correlated with measured GLAM, although this procedure is constrained by an inability to distinguish among the functional mechanisms determining the phenotype. Linking functional rather than phenotypic differences to molecular markers may improve the efficiency of selecting for traits such as stay-green.

Does maintaining green leaf area in sorghum improve yield under drought? II. Dry matter production and yield

Retention of green leaf area at maturity (GLAM), known as stay-green, is used as an indicator of postanthesis drought resistance in sorghum [Sorghum bicolor (L.) Moench] breeding programs in the USA and Australia. The critical issue is whether maintaining green leaves under postanthesis drought increases grain yield in stay-green compared with senescent hybrids. Field studies were undertaken in northeastern Australia on a cracking and self-mulching gay clay. Nine closely related hybrids varying in rate of leaf senescence were grown under two water-limiting regimes, post-flowering water deficit and terminal (pre- and postflowering) water deficit, and a fully irrigated control. Under terminal water deficit, grain yield tvas correlated positively with GLAM (r = 0.75**) and negatively with rate of leaf senescence (r = -0.74**). Grain yield also increased by approximate to 0.35 Mg ha(-1) for every day that onset of leaf senescence was delayed beyond 76 DAE in the water-limited treatments. Stay-green hybrids produced 47% more postanthesis biomass than their senescent counterparts (920 vs. 624 g m(-2)) under the terminal water deficit regime. No differences in grain yield were found among eight of the nine hybrids under fully irrigated conditions, suggesting that the stay-green trait did not constrain yield in the well-watered control. The results indicate that sorghum hybrids possessing the stay-green trait have a significant yield advantage under postanthesis drought compared with hybrids not possessing this trait.

Predicting plant leaf area production: shoot assimilate accumulation and partitioning, and leaf area ratio, are stable for a wide range of sorghum population densities

Predicting plant leaf area production is required for modelling carbon balance and tiller dynamics in plant canopies. Plant leaf area production can be studied using a framework based on radiation intercepted, radiation use efficiency (RUE) and leaf area ratio (LAR) (ratio of leaf area to net above-ground biomass). The objective of this study was to test this framework for predicting leaf area production of sorghum during vegetative development by examining the stability of the contributing components over a large range of plant density. Four densities, varying from 2 to 16 plants m(-2), were implemented in a field experiment. Plants were either allowed to tiller or were maintained as uniculm by systematic tiller removal. In all cases, intercepted radiation was recorded daily and leaf area and shoot dry matter partitioning were quantified weekly at individual culm level. Up to anthesis, a unique relationship applied between fraction of intercepted radiation and leaf area index, and between shoot dry weight accumulation and amount of intercepted radiation, regardless of plant density. Partitioning of shoot assimilate between leaf, stem and head was also common across treatments up to anthesis, at both plant and culm levels. The relationship with thermal time (TT) from emergence of specific leaf area (SLA) and LAR of tillering plants did not change with plant density. In contrast, SLA of uniculm plants was appreciably lower under low-density conditions at any given TT from emergence. This was interpreted as a consequence of assimilate surplus arising from the inability of the plant to compensate by increasing the leaf area a culm could produce. It is argued that the stability of the extinction coefficient, RUE and plant LAR of tillering plants observed in these conditions provides a reliable way to predict leaf area production regardless of plant density. Crown Copyright (C) 2002 Published by Elsevier Science B.V. All rights reserved.