Canopy Level Research Articles

Combining Multiangular, Polarimetric, and Hyperspectral Measurements to Estimate Leaf Nitrogen Concentration From Different Plant Species

Optical remote sensing is one of the most popular methods for estimating leaf nitrogen concentration (LNC). This nondestructive approach based on reflected intensity measurements has been applied to estimate the variation and distribution of nitrogen concentration in leaf and canopy levels in numerous studies. However, both intensity and polarization are necessary to describe the optical properties of light reflected from leaves and to estimate LNC estimation. In this study, based on the Stokes parameters, the total reflectance, the polarized reflectance, and the nonpolarized reflectance factors (NpRFs) were simultaneously obtained through polarimetric hyperspectral measurements under varied source-viewing geometries in both laboratory and field conditions. Several published hyperspectral indices based on the NpRF showed much better LNC estimation accuracy than those using the total reflectance factor. A clear improvement was found in the viewing directions dominated by specular reflection. Thus, using multi-angular polarimetric hyperspectral measurements not only improves the accuracy of hyperspectral indices on LNC estimation using the NpRF, but also enables the hyperspectral indices to be effective for a wide range of viewing angles. Moreover, polarimetric measurements deepen the understanding of the optical properties of light reflected from leaves. These results indicate that the combination of multiangular, polarimetric, and hyperspectral measurements may play a key role in the estimation of LNC.

In-Field Early Disease Recognition of Potato Late Blight Based on Deep Learning and Proximal Hyperspectral Imaging

Effective early detection of potato late blight (PLB) is an essential aspect of potato cultivation. However, it is a challenge to detect late blight at an early stage in fields with conventional imaging approaches because of the lack of visual cues displayed at the canopy level. Hyperspectral imaging can, capture spectral signals from a wide range of wavelengths also outside the visual wavelengths. In this context, we propose a deep learning classification architecture for hyperspectral images by combining 2D convolutional neural network (2D-CNN) and 3D-CNN with deep cooperative attention networks (PLB-2D-3D-A). First, 2D-CNN and 3D-CNN are used to extract rich spectral space features, and then the attention mechanism AttentionBlock and SE-ResNet are used to emphasize the salient features in the feature maps and increase the generalization ability of the model. The dataset is built with 15,360 images (64x64x204), cropped from 240 raw images captured in an experimental field with over 20 potato genotypes. The accuracy in the test dataset of 2000 images reached 0.739 in the full band and 0.790 in the specific bands (492nm, 519nm, 560nm, 592nm, 717nm and 765nm). This study shows an encouraging result for early detection of PLB with deep learning and proximal hyperspectral imaging.

Spectral responses at visible and near-infrared wavelengths of soybean plants to fungicides

ABSTRACT: The study evaluated the efficacy and soybean spectral responses to fifteen foliar fungicide mixtures labeled to control Asian soybean rust. Canopy level reflectance was measured using a multispectral camera onboard a multirotor drone before and two hours after each spray. The third application of fungicides improved control of soybean rust and increased yield. Nevertheless, up to three consecutive foliar fungicides applications did not affect the reflectance of soybean plants at visible and infrared wavelengths. Thus, drones can be a viable strategy for data acquisition regardless of the application of the fungicides.

Combined Radiometric Analysis Related to Guava Leaf Phenology in Response to Soil Application of Paclobutrazol (PBZ)

The leaf-response to three-soil applied treatments of Paclobutrazol (PBZ; 1000, 2000 and 0 ppm-control) was studied in a high-density plantation of eight guava (Psidium guajava) genotypes trees. All materials were prunned in vase form, with two to three major branches, yearly prunning for triggering the annual production cycle, and average height of 2.0 m. The dataset comprises fourth radiometric indices highly related to plant physiological activities. The dataset model took into account data collection dates, guava genotypes, and the positional effect of sun radiation on leaves based on their proximity to the canopy level and downward to the base of the woody seasonal-branch. Unexpectedly, there were no significant differences (NS) in PBZ treatments for genotypes, leaf position and radiometric indices. Analysis of the radiometric indices data revealed that anthocyanin (ARI index) and chlorophyll (PRI index) have a strong inverse relationship. Significant differences (P ≤ 0.05) were found between guava genotypes, and anthocyanin content; these results show that guava genotypes have varied responses, which could derive in their classification based-on drought resistance or low water requirements, however, it is important to note that additional research is required to determine the scope of these indications.

Foliar and root uptake of N deriving from simulated atmospheric N depositions in potted apple (Malus domestica) trees

A significant human-driven increment of the available reactive nitrogen (Nr) forms has occurred during the past century at the global scale, which in turn has increased the amount of Nr deposition. Grafted apple trees (Gala / M.9 strain T337) were used in a pot experiment conducted in semicontrolled conditions, where the 15N-labelling technique allowed to trace the fate of N from ammonium nitrate (15NH4 15NO3, isotopic enrichment: 10.3 atoms %) distributed at three increasing rates (N1, N2, N4, where N2 is the double of N1 and N4 is the double of N2) either to soil or to canopy (foliar application) to simulate atmospheric N depositions. At the end of the experiment, plants were destructively sampled, and N derived from depositions (Ndfd), total N, and biomass of above and belowground organs were determined. Uptake rates ranged from 21% to 57% and the Ndfd recovery was higher for soil than for foliar application. Foliar-supplied plants showed a higher Ndfd in leaves and shoots than soil-supplied ones, while the latter showed a higher Ndfd in roots than the former. Moreover, total N in trunk, shoot axes and leaves increased with the N rates up to the level N2, with no further increase in N4. Increasing tree N availability, regardless the supply mode, increased the shoot:root N content. The fact that the N uptake rate was rather stable at increasing N rates suggests that if N from atmospheric depositions becomes increasingly available at the canopy or soil level, it will actively contribute to apple tree nutrition and account for a significant fraction of the apple tree N needs.

Foliar Application of Protein Hydrolysates on Baby-Leaf Spinach Grown at Different N Levels

Surpluses of N are associated with environmental and health problems. To optimise N use and reduce nitrate accumulation in leafy species like spinach, the application of biostimulants is suggested. An experiment in controlled conditions (growth chamber/soilless) evaluated baby-spinach responses to two protein hydrolysates (PHs) from plant (legume, Trainer®) and animal (meat, Isabion®) sources, combined with three N rates: 2 (N2, deficient), 8 (N8, sub-optimal), and 14 (N14, optimal) mM of N. Biometrical and morphological traits of shoots and roots as well as the physio-metabolic (gas exchange, N assimilation, and NUtE), physical, mineral, and antioxidant profiles of leaves were assessed. The legume-PH boosts growth and yield only at the highest N conditions, while there was no effect at lower N rates. The legume-PH modulates root architecture and chlorophylls has positive responses only at optimal N availability, such as an increase in N uptake, leaf expansion, and photosynthetic activity at the canopy level. The PHs do not improve NUtE, leaf colour, consistency, cations, or antioxidants. Neither do PHs have any effect on reducing nitrate accumulation. Legume-PH improves N assimilation only at optimal N availability, while meat-PH does not, reaching the highest nitrate value at the highest N rate (2677 mg kg−1 fw), even if this value is under the EC limits for fresh spinach.

The Interrelationship Between Water Use Efficiency and Radiation Use Efficiency Under Progressive Soil Drying in Maize.

The maximizing of water use efficiency (WUE) and radiation use efficiency (RUE) is vital to improving crop production in dryland farming systems. However, the fundamental question as to the association of WUE with RUE and its underlying mechanism under limited-water availability remains contentious. Here, a two-year field trial for maize designed with five progressive soil drying regimes applied at two different growth stages (three-leaf stage and seven-leaf stage) was conducted during the 2013–2014 growing seasons. Both environmental variables and maize growth traits at the leaf and canopy levels were measured during the soil drying process. The results showed that leaf WUE increased with irrigation reduction at the early stage, while it decreased with irrigation reduction at the later stage. Leaf RUE thoroughly decreased with irrigation reduction during the progressive soil drying process. Aboveground biomass (AGB), leaf area index (LAI), a fraction of absorbed photosynthetically active radiation (fAPAR), and light extinction coefficient (k) of the maize canopy were significantly decreased by water deficits regardless of the growth stages when soil drying applied. The interrelationships between WUE and RUE were linear across the leaf and canopy scales under different soil drying patterns. Specifically, a positive linear relationship between WUE and RUE are unexpectedly found when soil drying was applied at the three-leaf stage, while it turned out to be negative when soil drying was applied at the seven-leaf stage. Moreover, the interaction between canopy WUE and RUE was more regulated by fAPAR than LAI under soil drying. Our findings suggest that more attention must be paid to fAPAR in evaluating the effect of drought on crops and may bring new insights into the interrelationships of water and radiation use processes in dryland agricultural ecosystems.

Monitoring nitrogen variability in two Mediterranean ornamental shrubs through proximal fluorescence-based sensors at leaf and canopy level

Over - fertilization is one of the most relevant issues affecting container-grown nursery plants.The massive application of chemicals results in severe pollution due to nitrogen (N) leaching, and leads to the production of unhealthy plants sensitive to fungal diseases and insect attacks that increase the use of pesticides. The ability of two fluorescence-based sensors (Dualex and Multiplex) for assessing the N-sufficiency of container grown Prunus laurocerasus L. and Ligustrum japonicum L. as test plants was investigated in a two-year long experiment under experimental and commercial growing conditions. An optimal N rate of 450 mg L−1 was identified for both species growing under different N fertilization levels (0 - 1350 mg N L−1). Fluorescence-based indices allowed for reliable estimation of both chlorophyll and nitrogen content at the leaf and canopy level. Absolute and relative sufficiency threshold values were provided and spatially compared under standard nursery conditions. The optimal shoot N concentration of 2.04% (P. laurocerasus) and 1.35% (L. japonicum), and the corresponding non-destructive nitrogen balance index (NBI) values were assessed and used for decision-making on N application and monitoring of the species considered.

Canopy height affects the allocation of photosynthetic carbon and nitrogen in two deciduous tree species under elevated CO2

Down-regulation of leaf N and Rubisco under elevated CO2 (eCO2) are accompanied by increased non-structural carbohydrates (NSC) due to the sink–source imbalance. Here, to investigate whether the canopy position affects the down-regulation of Rubisco, we measured leaf N, NSC and N allocation in two species with different heights at maturity [Fraxinus rhynchophylla (6.8 ± 0.3 m) and Sorbus alnifolia (3.6 ± 0.2 m)] from 2017 to 2019. Since 2009, both species were grown at three different CO2 concentrations in open-top chambers: ambient CO2 (400 ppm; aCO2); ambient CO2 × 1.4 (560 ppm; eCO21.4); and ambient CO2 × 1.8 (720 ppm; eCO21.8). Leaf N per unit mass (Nmass) decreased under eCO2, except under eCO21.8 in S. alnifolia and coincided with increased NSC. NSC increased under eCO2 in F. rhynchophylla, but the increment of NSC was greater in the upper canopy of S. alnifolia. Conversely, Rubisco content per unit area was reduced under eCO2 in S. alnifolia and there was no interaction between CO2 and canopy position. In contrast, the reduction of Rubisco content per unit area was greater in the upper canopy of F. rhynchophylla, with a significant interaction between CO2 and canopy position. Rubisco was negatively correlated with NSC only in the upper canopy of F. rhynchophylla, and at the same NSC, Rubisco was lower under eCO2 than under aCO2. Contrary to Rubisco, chlorophyll increased under eCO2 in both species, although there was no interaction between CO2 and canopy position. Finally, photosynthetic N content (Rubisco + chlorophyll + PSII) was reduced and consistent with down-regulation of Rubisco. Therefore, the observed Nmass reduction under eCO2 was associated with dilution due to NSC accumulation. Moreover, down-regulation of Rubisco under eCO2 was more sensitive to NSC accumulation in the upper canopy. Our findings emphasize the need for the modification of the canopy level model in the context of climate change.

Upscaling from leaf to canopy: Improved spectral indices for leaf biochemical traits estimation by minimizing the difference between leaf adaxial and abaxial surfaces

The knowledge of leaf biochemical traits is significant to understand the plant growth and physiological status. Spectral indices have been widely used to assess leaf biochemical traits, while the estimation accuracies at canopy level are frequently lower compared to those at leaf level in part due to the complexity of canopy structures and the variations in optical properties between leaf adaxial and abaxial surfaces. This study thus improved spectral indices with minimizing the effect of the difference between leaf adaxial and abaxial surfaces to assess leaf biochemical traits from leaf to canopy level. The datasets including leaf reflectance and canopy reflectance with corresponding leaf chlorophyll content ( C ab ), water content ( C w ), and dry matter content ( C m ) from a wide range of plant species were used. Results showed that there existed a significant difference between leaf abaxial and abaxial reflectance, causing the variation in the relationship between leaf biochemical traits and spectral indices. The published spectral indices exhibited the strong relationships with C ab , C w , and C m for either leaf adaxial or abaxial data, while the relationships of spectral indices with C ab , C w , and C m from leaf adaxial reflectance were inconsistent with those from leaf abaxial reflectance. The proposed adjusted ratio of difference spectral index (ARDSI) minimized the difference between leaf adaxial and abaxial reflectance for assessing C ab , C w , and C m at leaf level with the root mean square error of 9.32 μg cm -2 , 0.0050 g cm -2 , and 0.0053 g cm -2 , respectively. The application of the proposed ARDSI to the canopy level alleviated the effect of the variations of leaf adaxial and abaxial reflectance difference and canopy structures on the estimation of leaf biochemical traits, which thus improved the assessment of C ab , C w , and C m at canopy level in the simulated and measured datasets. The proposed approach would advance the applicability of spectral indices to monitor the physiological and functional traits of field crops at different scales. • Both leaf sides showed varied surface characteristics and optical properties. • Differences between both leaf sides affected the simulation of canopy reflectance. • Universal spectral indices were developed for leaf adaxial and abaxial surfaces. • The spectral indices well assessed C ab , C w , and C m at leaf and canopy levels.

A novel light interception trait of a hybrid rice ideotype indicative of leaf to panicle ratio

One of the major challenges facing rice breeding is to produce ideotype or ideal plant architecture (IPA) to improve canopy radiation use efficiency and hence grain yield. The hybrid japonica rice Jiayou Zhongke 6 (JYZK-6) is one of the benchmark cultivars with IPA characteristics, but the physio-ecological foundation of its high yield potential is still imperfectly understood. In this study, two-year field experiments were conducted using four japonica rice cultivars including JYZK-6 with contrasting canopy structures. A dynamic canopy light interception simulating device was constructed to capture canopy images representing the diurnal dynamics of solar angles throughout the grain-filling stage. Subsequently, leaf to panicle ratio (LPR), a newly developed physiological trait indicative of source-sink relations, was exploited to quantify the light distribution pattern within the rice canopy. The LPRs with high spatio-temporal resolution clearly showed the diurnal changes of light interception within the rice canopy. Genotypic differences in diurnal pattern of LPR were detected among the tested cultivars in both growing seasons, with the amplitude varying with grain-filling stages. Notably, LPR of the IPA cultivar JYZK-6 and its analogue WYJ-29, had a pattern of a V-shaped graph, peaking at sunrise and sunset while dropping to the lowest at noon during middle and late stages. Morphological measurement showed that the V-shaped pattern was associated with the height difference between flag leaf and panicle, panicle curvature, ratio of leaf area to panicle area, as well as the changing solar angles along with the progression of growth stage. This distinguishing feature of light pattern indicates that within the canopy of IPA cultivars, the leaf receives more light in the morning and afternoon whereas the panicle harvests more light at noon. It is inferred that such novel characteristics may provide the IPA cultivar with advantages of reducing midday suppression over the conventional cultivars, while more work is needed to verify this hypothesis. In summary, findings of this study offer a deeper insight into the physio-ecological processes underpinning radiation use efficiency at canopy level, and hence are valuable for breeding programs for ideotype cultivars.

Mapping canopy nitrogen-scapes to assess foraging habitat for a vulnerable arboreal folivore in mixed-species Eucalyptus forests.

Herbivore foraging decisions are closely related to plant nutritional quality. For arboreal folivores with specialized diets, such as the vulnerable greater glider (Petauroides volans), the abundance of suitable forage trees can influence habitat suitability and species occurrence. The ability to model and map foliar nitrogen would therefore enhance our understanding of folivore habitat use at finer scales. We tested whether high‐resolution multispectral imagery, collected by a lightweight and low‐cost commercial unoccupied aerial vehicle (UAV), could be used to predict total and digestible foliar nitrogen (N and digN) at the tree canopy level and forest stand‐scale from leaf‐scale chemistry measurements across a gradient of mixed‐species Eucalyptus forests in southeastern Australia. We surveyed temperate Eucalyptus forests across an elevational and topographic gradient from sea level to high elevation (50–1200 m a.s.l.) for forest structure, leaf chemistry, and greater glider occurrence. Using measures of multispectral leaf reflectance and spectral indices, we estimated N and digN and mapped N and favorable feeding habitat using machine learning algorithms. Our surveys covered 17 Eucalyptus species ranging in foliar N from 0.63% to 1.92% dry matter (DM) and digN from 0.45% to 1.73% DM. Both multispectral leaf reflectance and spectral indices were strong predictors for N and digN in model cross‐validation. At the tree level, 79% of variability between observed and predicted measures of nitrogen was explained. A spatial supervised classification model correctly identified 80% of canopy pixels associated with high N concentrations (≥1% DM). We developed a successful method for estimating foliar nitrogen of a range of temperate Eucalyptus species using UAV multispectral imagery at the tree canopy level and stand scale. The ability to spatially quantify feeding habitat using UAV imagery allows remote assessments of greater glider habitat at a scale relevant to support ground surveys, management, and conservation for the vulnerable greater glider across southeastern Australia.

Investigation of optimal vegetation indices for retrieval of leaf chlorophyll and leaf area index using enhanced learning algorithms

With the availability of high-resolution data due to sensor technology advancement, it is now easier for researchers and scientists to detect or view the spectral variability of different crops. For this study, Leaf chlorophyll content (LCC) and Leaf area index (LAI) of the crops Maize (Zea mays), Mustard (Brassica), and pink Lentils (Lens esculenta) under different irrigation and fertilizer treatments have been analyzed. In total, rigorous assessment of 25-hyperspectral vegetation indices (VIs) at both leaf and canopy level for chlorophyll content, whereas 7- hyperspectral VIs for LAI at canopy level were computed to investigate the robustness of these VIs for LCC and LAI assessment. Variable importance in projection (VIP) using Partial Least Square regression (PLSR) and coefficient of determination (R2) were computed for all the VIs to extract the most sensitive information for the retrieval of LCC and LAI. As a result, the VIs using the red-edge reflectance bands at 705 and 750 nm were found highly responsive to LAI compared to other wavebands. In contrast, the VIs indices made of green (550 nm), red (670, 690, and 700 nm), and red-edge (705, 750 nm) bands were found highly sensitive to the temporal LCC values of lentils and maize crop beds. In addition, the temporal LCC values of Mustard crop beds’ were found sensitive to the VIs made of green (550 nm), red (670, 690, and 700 nm), and NIR (800 nm) wavebands. The three VIs having high VIP and R2 values were selected as optimum sets of input to build support vector regression models using radial (SVR-Rad), linear (SVR-Li), polynomial (SVR-Poly), Random Forrest Regression (RFR), Partial least square regression (PLSR), and Hybrid neural fuzzy inference system (HyFIS). The analysis showed that the SVR-Rad model outperformed the SVR-Li, SVR-Poly, RFR, PLSR, and HyFIS models in terms of robustness for biophysical and biochemical parameters retrieval using hyperspectral data.

Hyperspectral Characteristics and Scale Effects of Leaf and Canopy of Summer Maize under Continuous Water Stresses

The scale effect problem is one of the most challenging issues in remote sensing studies. However, the research on the methodology and theory of the scale effect is scarcely applied in practice. To this end, in this study, 3 years of field experimental data of continuous water stresses on summer maize were used for this purpose. Furthermore, the Prospect and Sail models were employed to investigate the scale effects of reflectance characteristics and vegetation indexes. The results indicated that the spectral characteristics of canopy and leaf of summer maize were similar under continuous water stresses at various stages. The reflectance at the canopy level was distinct from that at the leaf level, considering the soil background differences. From leaf to canopy scales, with the increase in the leaf area index (LAI), the spectral reflectance of all treatments in the visible band decreased, but increased in the near-infrared band, and the reflectance was saturated when LAI increased to 5. The reflectance difference caused by LAI variation was enlarged as the drought stress intensified in the short-wave infrared band. The spectral reflectance in the near-infrared band was susceptible to leaf inclination angle (LIA) variation and changed significantly, especially in the closed canopy. With the increase in LAI, the difference vegetation index (DVI) and normalized difference vegetation index (NDVI) values under each treatment showed a gradually increasing trend. With the increase in LIA, the DVI value decreased gradually, and the DVI value under the saturated canopy was significantly higher than that under the unclosed canopy. However, the NDVI values of all treatments did not change with LIA, mostly under the closed canopy. Overall, the results demonstrated that LAI had a more significant influence on canopy reflectance than LIA. In addition, NDVI was not able to capture the LAI and LIA information when the canopy was closed, but DVI performed better.

Three-dimensional stratification pattern in an old-growth lowland forest: How does height in canopy and season influence temperate bat activity?

The study of animal–habitat interactions is of primary importance for the formulation of conservation recommendations. Flying, gliding, and climbing animals have the ability to exploit their habitat in a three‐dimensional way, and the vertical canopy structure in forests plays an essential role for habitat suitability. Forest bats as flying mammals may seasonally shift their microhabitat use due to differing energy demands or changing prey availability, but the patterns are not well understood. We investigated three‐dimensional and seasonal habitat use by insectivorous bats in a temperate lowland old‐growth forest, the Belovezhskaya Pushcha in Belarus. We acoustically sampled broadleaved and mixed coniferous plots in the forest interior and in gaps in three heights during two reproductive periods (pregnancy/lactation vs. postlactation). In canopy gaps, vertical stratification in bat activity was less pronounced than in the forest interior. Vertical activity patterns differed among species. The upper canopy levels were important foraging habitats for the open‐space forager guild and for some edge‐space foragers like the Barbastelle bat Barbastella barbastellus and the soprano pipistrelle Pipistrellus pygmaeus. Myotis species had highest activity levels near the ground in forest gaps. Moreover, we found species‐dependent seasonal microhabitat shifts. Generally, all species and species groups considered except Myotis species showed higher activity levels during postlactation. Myotis species tended toward higher activity in the forest interior during postlactation. P. pygmaeus switched from high activity levels in the upper canopy during pregnancy and lactation to high activity levels near the ground during postlactation. We conclude that a full comprehension of forest bat habitat use is only possible when height in canopy and seasonal patterns are considered.

Applied aerial spectroscopy: A case study on remote sensing of an ancient and semi-natural woodland.

An area of ancient and semi-natural woodland (ASNW) has been investigated by applied aerial spectroscopy using an unmanned aerial vehicle (UAV) with multispectral image (MSI) camera. A novel normalised difference spectral index (NDSI) algorithm was developed using principal component analysis (PCA). This novel NDSI was then combined with a simple segmentation method of thresholding and applied for the identification of native tree species as well as the overall health of the woodland. Using this new approach allowed the identification of trees at canopy level, across 7.4 hectares (73,934 m2) of ASNW, as oak (53%), silver birch (37%), empty space (9%) and dead trees (1%). This UAV derived data was corroborated, for its accuracy, by a statistically valid ground-level field study that identified oak (47%), silver birch (46%) and dead trees (7.4%). This simple innovative approach, using a low-cost multirotor UAV with MSI camera, is both rapid to deploy, was flown around 100 m above ground level, provides useable high resolution (5.3 cm / pixel) data within 22 mins that can be interrogated using readily available PC-based software to identify tree species. In addition, it provides an overall oversight of woodland health and has the potential to inform a future woodland regeneration strategy.

A new approach for modeling crop-weed interaction targeting management support in operational contexts: A case study on the rice weeds barnyardgrass and red rice

Despite their potential to support the optimization of weed management, available ecophysiological models for the simulation of crop-weed interaction are still not adopted in operational contexts. For some of them the reasons deal with the insufficient validation in farming conditions, whereas others are either too complex for being used in operational contexts or too empiric for being free from site- or context-specific effects. Here we present a new approach (WeedyCoSMo) to support strategic decisions on weed management, derived from the CoSMo process-based model for the simulation of phytocoenosis dynamics. The model dynamically reproduces on a yearly basis the interaction between crop and weeds at canopy level through the daily quantification of the suitability of each species to weather conditions and management practices, as well as to the simulated system state variables. Dynamically predicted outputs are the relative abundance of crop and weeds and state variables for each species like, e.g., aboveground biomass, biomass of different plant organs, grain yield, leaf area index, plant height. WeedyCoSMo was calibrated and validated using data from different sites (in the Jiangsu province, China, and in Arkansas, USA) and years (from 1982 to 2014), where different rice varieties and two major rice weeds–i.e., red rice (Oryza sativa L., var. sylvatica) and barnyardgrass (Echinocloa crus-galli L.) – were grown in monoculture or mixture. Model performances were satisfying: for rice crops grown in interaction with weeds, relative root mean square error never exceeded 25.2%, regardless of the variable considered, and Nash-Sutcliffe modeling efficiency was always higher than 0.63. Despite the low number of inputs and parameters needed to run the simulations, the degree of accuracy was similar to the ones achieved with other models for crop-weed interaction. This allows considering WeedyCoSMo as a promising approach in light of the possible integration in decision support systems targeting operational farming conditions.

Development of critical nitrogen dilution curves for different leaf layers within the rice canopy

Understanding the effect of nitrogen (N) vertical distribution within the rice canopy at different growth stages and development of critical nitrogen dilution curves for upper leaf layers will not only help accurate estimations of the spatial and temporal variation in N status, but will also provide a basis for analyses of N nutrition in rice. Based on a three-year field experiment with 2 rice varieties, 3 N levels and 4 planting densities, the effect of heterogeneity in N vertical distribution on the nitrogen nutrition index (NNI) was then determined, and for the first time, critical N dilution curves of upper leaf layers were constructed based on a Bayesian statistical model. The relationship for NNI entire canopy and its relationship with the other tested leaf layers was investigated. The leaf layer suitable for analyses of N nutrition and directly inversion of NNI by remote sensing is determined. The results revealed that the main contributor to plant N concentration (PNC) of entire canopy (PNCCanopy) was found to be the lower leaf layer of the rice canopy, and may lead to misdiagnosis under the non-N-limiting condition by using the canopy level based NNI. The PNC of top half leaf layer of the canopy was more stable in terms of short-term environmental changes, and more accurately representing the overall N status. The top half leaf layer was the main contributor to canopy reflectance. Thus, compared with the NNI of other upper leaf layers, the NNI of the top half leaf layer is more suitable for direct inversion by using CI (1113, 743) with the R2 = 0.70. Overall, these findings suggest that the NNI of the top half leaf layer is the optimal remote sensing indicator of N nutrition in rice.

Latent heat flux variability and response to drought stress of black poplar: A multi-platform multi-sensor remote and proximal sensing approach to relieve the data scarcity bottleneck

High-throughput mapping of latent heat flux (λET) is critical to efforts to optimize water resources management and to accelerate forest tree breeding for improved drought tolerance. Ideally, investigation of the energy response at the tree level may promote tailored irrigation strategies and, thus, maximize crop biomass productivity. However, data availability is limited and planning experimental campaigns in the field can be highly operationally complex. To this end, a multi-platform multi-sensor observational approach is herein developed to dissect the λET signature of a black poplar (Populus nigra) breeding population (“POP6”) at the canopy level. POP6 comprised more than 4600 trees representing 503 replicated genotypes, whose parents were derived from contrasting environmental conditions. Trees were trialed in two adjacent plots where different irrigation treatments (moderate drought [mDr] and well-watered [WW]) were applied. Data collected from satellite and unmanned aerial vehicles (UAVs) remote sensing as well as from ground-based proximal sensors were integrated at consistent spatial aggregation and combined to compute the surface energy balance of the trees through a modified Priestley-Taylor method. Here, we demonstrated that λET response was significantly different between WW and mDr trees, whereby genotypes in mDr conditions exhibited larger standard deviations. Importantly, genotypes classified as drought tolerant based on the stress susceptibility index (SSI) presented λET values significantly higher than the rest of the population. This study confirmed that water limitation in mDr settings led to reduced soil moisture in the tree root zone and, thus, to lower λET. These results pave the way to breeding poplar and other bioenergy crops with this underexploited trait for higher λET. Most notably, the illustrated work demonstrates a multi-platform multi-sensor data fusion approach to tackle the global challenge of monitoring landscape-scale ecosystem processes at fine resolution.

Experimental and producer-reported data quantify the value of foliar fungicide to winter wheat and its dependency on genotype and environment in the U.S. central Great Plains

• We quantified the yield benefits of fungicide to wheat using plot and survey data. • Fungicide yield benefits (7.8 ± 15 %) were greater in wetter growing seasons. • Susceptible genotypes benefited more from fungicides (17 %) than resistant ones (6 %). • Fungicides improved stability and modified environment-adaptability of genotypes. • Fungicide yield benefits were greater (31 %) in the survey data. Foliar fungicides can account for a large portion of the yield gap in winter wheat ( Triticum aestivum L.); however, their impacts on yield have been inconsistent in rainfed environments. We compiled a database of replicated field experiments and producer-reported fungicide and yield data from commercial fields to quantify the effects of fungicide application on winter wheat yield and yield stability. The database of field experiments (i.e., canopy level) included 56 non-inoculated environments spanning 12 growing seasons in eight Kansas locations, and was restricted to field experiments with direct comparisons between a foliar fungicide between Zadoks 40–55 and a side-by-side untreated control, resulting in 393 mean yield comparisons resulting from 3226 yield observations. The producer survey included genotype and fungicide management data from 654 commercial Kansas wheat fields cultivated across three growing seasons. Grain yield and weather conditions in the experimental and producer-reported database were similar, with seasonal precipitation ranging from ˜150 to 1035 mm and average grain yield of ˜3900 kg ha −1 with a ˜7000 kg ha −1 range. Foliar fungicide application resulted in 7.8 % average yield gain in the canopy-level data, ranging from −27 % to +97 %. Yield differences due to fungicide were strongly related to precipitation and to the ratio of precipitation and reference evapotranspiration (WS:WD) during the spring in the experimental data. Grain yield responsiveness to fungicide associated with the responsiveness of green canopy cover, kernel weight, biomass, and harvest index. Analysis of covariance suggested that grain yield usually decreased with increases in disease susceptibility in the absence of foliar fungicides; however, yield-disease relationships were either neutral or positive in the presence of fungicides. Average yield gain for resistant varieties was ˜166 kg ha −1 (5.6 %), which was lower than for intermediate (˜199 kg ha −1 ; 6.9 %) or susceptible genotypes (˜598 kg ha −1 ; 16.9 %). Foliar fungicides increased yield stability across genotypes at the canopy level and at the commercial field level. In the commercial-field level data, 53 % of the fields received foliar fungicide, with higher frequency in growing seasons with greater WS:WD (which were also higher yielding). The use of foliar fungicides was associated with improved yields and interacted with genotype’s resistance level to stripe rust and with growing seasons’ WS:WD. This work quantified and explained the yield benefits of foliar fungicide, and characterized its dependency on genotype-specific disease resistance and environmental conditions both at the canopy- and commercial field-levels.