Canopy Development Research Articles

Active restoration increases tree species richness and recruitment of large-seeded taxa after 16-18 years.

Tropical forest restoration presents a potential lifeline to mitigate climate change and biodiversity crises in the Anthropocene. Yet, the extent to which human interventions, such as tree planting, accelerate the recovery of mature functioning ecosystems or redirect successional trajectories toward novel states remains uncertain due to a lack of long-term experiments. In 2004-2006, we established three 0.25-ha plots at 10 sites in southern Costa Rica to test three forest restoration approaches: natural regeneration (no planting), applied nucleation (planting in patches), and plantation (full planting). In a comprehensive survey after 16-18 years of recovery, we censused >80,000 seedlings, saplings, and trees from at least 255 species across 26 restoration plots (nine natural regeneration, nine applied nucleation, eight plantation) and six adjacent reference forests to evaluate treatment effects on recruitment patterns and community composition. Both applied nucleation and plantation treatments resulted in significantly elevated seedling and sapling establishment and more predictable community composition compared with natural regeneration. Similarity of vegetation composition to reference forest tended to scale positively with treatment planting intensity. Later-successional species with seeds ≥5 mm had significantly greater seedling and sapling abundance in the two planted treatments, and plantation showed similar recruitment densities of large-seeded (≥10 mm) species to reference forest. Plantation tended toward a lower abundance of early-successional recruits than applied nucleation. Trees (≥5 cm dbh) in all restoration treatments continued to be dominated by a few early-successional species and originally transplanted individuals. Seedling recruits of planted taxa were more abundant in applied nucleation than the other treatments though few transitioned into the sapling layer. Overall, our findings show that active tree planting accelerates the establishment of later-successional trees compared with natural regeneration after nearly two decades. While the apparent advantages of higher density tree planting on dispersal and understory establishment of larger seeded, later-successional species recruitment is notable, more time is needed to assess whether these differences will persist and transition to the more rapid development of a mature later-successional canopy. Our results underscore the need for ecological restoration planning and monitoring that targets biodiversity recovery over multiple decades.

Rootstock effect on growth, branch morphology and flowering precocity of young monoaxis and biaxis trees of ‘Scilate’ and ‘Fuji supreme’ apple cultivars

ABSTRACT The New Zealand Institute for Plant and Food Research has developed three semi-dwarfing apple rootstocks: PFR2, 4 and 5 with pest and disease resistance/tolerance. Nursery performance and flowering of ‘Scilate' and ‘Fuji Supreme' grafted onto PFR rootstocks and grown as monoaxis or biaxis trees were evaluated in comparison with ‘Malling 9' (dwarf) and ‘G.202' (semi-dwarf) at Hawkes' Bay research centre. For monoaxis ‘Scilate' trees, PFR rootstocks promoted primary, axillary and total shoot length/scion by (11–15%), (54–76%) and (22–37%), respectively compared with ‘M.9', but the same were lower by (4–11%), (25–62%) and (17–42%) compared with ‘G.202'. On PFR2 and PFR5, ‘Scilate' produced 6 axillary shoots/tree, compared with ‘M.9' (4) and ‘G.202' (7). ‘Scilate' on PFR2, 4 and 5 produced 74%, 62% and 93% of axillary shoots with moderate (30°–45°) crotch angle compared with ‘M.9' (30%) and ‘G.202' (79%). On biaxis trees, the total shoot length was higher on PFR2, 4 and 5 compared with ‘M.9', but not different from ‘G.202'. Flowering precocity was not affected by rootstocks. Overall, PFR2, 4, 5 and ‘G.202’ produced high-grade young trees expressing greater potential for early canopy development.

An in situ approach for validation of canopy chlorophyll fluorescence radiative transfer models using the full emission spectrum

The intensity and spectral properties of solar-induced chlorophyll fluorescence (SIF) carry valuable information on plant photosynthesis and productivity, but are also influenced by leaf and canopy structure. Physically based models provide a quantitative means to investigate how SIF intensity and spectra propagate and scale from the photosystem to the leaf and to the canopy levels. However, the validation of canopy SIF models is limited by the lack of methods that combine direct, independent, and complementary measurements of the full fluorescence spectrum at the leaf and canopy levels. Here, we propose a novel validation approach that combines in situ measurements of leaf and canopy fluorescence spectra. The approach is demonstrated with measurements in a rice crop at two contrasting stages of canopy development. We measured leaf reflectance, transmittance, and fluorescence spectra in situ, and subsequently inverted leaf structural and biochemical parameters and determined the leaf fluorescence quantum efficiency (FQE) using the Fluspect-Cx model. Two FQE inversion methods (Inversion-IIA and Inversion-IIB) were tested for the forward simulation of leaf fluorescence spectra. Leaf fluorescence spectra were then scaled up to the canopy level using 1D, 2D, and 3D radiative transfer schemes (SCOPE, mSCOPE, and DART), and compared with the direct canopy fluorescence spectral observations measured under red, green, blue, and white illumination. The validation results demonstrate that accounting for 3D canopy structure, as in the DART model, is critical to successfully scale the fluorescence spectrum from the leaf to the canopy level, whereas 1D SCOPE or even 2D mSCOPE were unable to fully reproduce the canopy fluorescence spectra. The results also demonstrate that the Inversion-IIB method matches relatively well the measurements with mean relative absolute errors (MRAE) of 20 %, 37 %, and 43 % versus Inversion-IIA with mean relative absolute errors (MRAE) of 62 %, 100 %, and 108 % for DART, mSCOPE, and SCOPE, respectively. We suggest that our validation approach is transferable to other plant species and canopy geometries, providing a means to standardize and evaluate the performance of canopy SIF models and improve our understanding of canopy SIF observations.

Vegetation community recovery on restored bottomland hardwood forests in northeast Indiana, USA.

Vegetation communities in restored bottomland hardwood forests in northeast Indiana were studied 6-21 years after restoration to assess progress toward restoration objectives. The study focused on four sites that were restored to compensate for resource injuries after contaminant releases. The restored sites were compared with four reference-site conditions, including crops (prerestoration condition), old field communities representing a no-management alternative, locally sampled second-growth mature forests, and forest community types described by the US National Vegetation Classification (USNVC), which represent ideal or defining conditions of recognized vegetation communities. Fixed-area plots provided data on field-sampled environmental variables, vegetation, soil, and hydrological conditions for crops, old fields, restored areas, and mature forests. The USNVC database provided quantitative data for three historically and geographically relevant reference forest community types for comparison with the sampled communities. Results of nonmetric multidimensional scaling based on species cover revealed clear gradients relating to site age and canopy development. Along those gradients, restored areas demonstrated increasing similarity to mature forest reference communities in terms of floristic composition. Specifically, the floristic quality of restored areas was significantly greater than that of crops and old fields. Furthermore, soil health measurements of physical, chemical, and hydrological conditions indicated significant improvements in restored site soils compared with prerestoration conditions represented by cropland soils. Descriptions and data from the USNVC provided ecological context for restoration target conditions and facilitated the assessment of restoration recovery along a trajectory from starting conditions to those target conditions. Descriptions by USNVC also helped identify deviations from the intended restoration objectives (e.g., invasive species recruitment) and potential adaptive management actions to return sites to their intended trajectories. Integr Environ Assess Manag 2024;20:1917-1938. Published 2024. This article is a U.S. Government work and is in the public domain in the USA. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Reduced canopy cover and development compromises weed suppression by buckwheat in cover crop mixtures

Buckwheat (Fagopyrum esculentum Moench) is a common cover crop typically grown for its rapid establishment and superior weed suppression. One challenge of incorporating buckwheat into crop rotations, however, is its short generation time, which requires in-season management to prevent viable seed production and volunteers in subsequent crops. Mixing buckwheat at reduced seeding rates with other cover crop species is one way for producers to reduce volunteer potential; however, this may compromise weed suppression. To investigate mechanisms behind buckwheat weed suppression and how using buckwheat in mixtures can affect suppression, we collected a series of morphological measurements in buckwheat monocultures and in three, two-way and four, three-way cover crop species mixtures and modeled their response over the growing season. We found that while buckwheat height and relative growth rate were unaffected with increasing species richness, there was a significant reduction in maximum leaf area index (3.75 vs. 2.85 vs. 2.82 cm2 cm-2) in mixture. Buckwheat monocultures provided superior season-long weed suppression compared to mixtures (85% vs. 53% vs. 50%). Principal component analysis showed that increased height and leaf area index values were associated with greater weed suppression. The link between reduced leaf area index and weed suppression in mixtures compared to buckwheat monocultures suggests that the rapid leaf development and canopy cover associated with buckwheat is a key mechanism behind weed suppression. In practice, producers should expect a dilution effect and weed suppression to be compromised when the buckwheat seeding rate is reduced in mixtures.

Divergent apparent temperature sensitivity of forest-floor respiration across a managed boreal forest landscape

Understanding the apparent temperature sensitivity (Q10) of forest floor respiration (Q10Rff) and its plant autotrophic (Q10Ra) and soil heterotrophic (Q10Rh) components is critical for determining the strength and direction of forest carbon cycle-climate feedbacks. However, the spatial variability of Q10Rff and its controlling factors across the spatially heterogeneous boreal forest landscape remains poorly understood. In this study, we used chamber-based respiration measurements conducted on paired natural and trenching plots over three growing seasons (2016–2018) to determine Q10Rff, Q10Rh and Q10Ra to soil temperature across 50 diverse forest stands (ranging 5–211 years old) in a managed landscape in northern Sweden. Additionally, we estimated ecosystem- and landscape-scale Q10 based on conventional and tall tower eddy covariance measurements, respectively, conducted over the same landscape. Our results suggest manifold variations (i.e., 10th to 90th percentiles) of Q10Rff (1.7 to 6.6), with Q10Ra (0.9 to 7.4) showing a greater range than Q10Rh (1.6 to 5.3). Forest-floor understory biomass was the main control of variations in Q10Ra, whereas soil properties explained best those of Q10Rh. Furthermore, a seasonal hysteresis of Q10Rff was observed, with higher values mostly occurring during the late growing season. The magnitude of this hysteresis scaled with stand age in response to concurrent changes in net primary production. The Q10Rff to air temperature declined with increasing stand age, which was attributed to the increased decoupling of air and soil temperatures with canopy development. Despite the considerable stand-level variations, when averaged over the 50 forest stands, Q10Rff to air temperature converged with that of both ecosystem- and landscape-scale respiration. In conclusion, our study highlights the complexity of Q10Rff, which needs to be considered in predictions of the interactions of climate change and management with the carbon cycle of managed boreal forests.

Effect of Tree Density on Yield and Fruit Quality of the Grafted Hazelnut Cultivar 'Tonda Francescana®'.

Optimizing planting density is crucial for balancing resource competition, light penetration, and tree productivity in orchard systems. This study investigateed the impact of planting density on the yield and fruit quality of the hazelnut cultivar 'Tonda Francescana®' grafted onto Corylus colurna L. rootstocks. The research aimed to assess how different planting densities influenced light penetration, canopy volume, yield, and the nutritional profile of hazelnuts during their sixth growing season. Three planting densities were tested: 625, 1250, and 2500 trees per hectare (low, medium, and high density, respectively). The results show that medium-density planting provided the best balance between light availability, canopy development, and yield efficiency. The synthesis of monounsaturated fatty acids (MUFA) and α-tocopherol (vitamin E) was more prominent in the medium-density system (80.2% and 10.3%, respectively), suggesting a favorable metabolic response to moderate competition for resources. In contrast, high-density planting yielded the most per hectare (2898 kg/ha) but exhibited lower individual tree productivity (1.16 kg). Low-density planting had the highest light penetration (53%) but lower overall yield (822 kg/ha) and quality, with greater starch accumulation in the fruit. In general, medium-density planting optimized both yield and kernel quality, with potential implications for orchard management and breeding strategies to enhance hazelnut production and nutritional value.

Exploring the Interplay of Bud Load and Pruning Type in Shaping ‘Xinomavro’ (Vitis vinifera L.) Vine Growth, Yield, and Berry Composition

‘Xinomavro’ (V. vinifera L.) is an important native red wine grape variety in Northern Greece, particularly in PDO (protected designation of origin) regions. Despite its significance, there is limited research on the effects of pruning type and severity on ‘Xinomavro’ vine physiology, yield, and berry quality across diverse environmental conditions. This study aimed to address this knowledge gap and provide growers with crucial information for optimizing vineyard management practices. The study was conducted over two consecutive years (2016 and 2017) in a vineyard in Thessaloniki, Northern Greece. Four treatments (B12: 12 buds on 6 spurs, B24: 24 buds on 12 spurs, M12: 12 buds on 2 canes, and M24: 24 buds on 4 canes) combining two bud load levels (12 or 24 count nodes) and two pruning types (short spurs or long canes) were applied to ‘Xinomavro’ vines in a complete block randomized design. The vine water status, gas exchange, canopy characteristics, yield components, and berry composition were measured. Bud load and pruning type significantly influenced vine canopy development, microclimate, and yield components. Short pruning with high bud load (B24) resulted in denser canopies and higher yields, whereas cane pruning (M12 and M24) led to more open canopies and improved berry quality indicators. Treatment effects on berry composition were inconsistent across years but showed a tendency for higher anthocyanin and total phenol content in cane-pruned vines. This study demonstrates that pruning type (short or long fruiting units) may have a greater impact on vine growth, yield, and berry composition than bud load alone in ‘Xinomavro’ vines. Cane pruning appears to be a more effective strategy for achieving vine balance and potentially improving grape quality under given experimental conditions.

Identification of potential auxin response candidate genes for soybean rapid canopy coverage through comparative evolution and expression analysis.

Throughout domestication, crop plants have gone through strong genetic bottlenecks, dramatically reducing the genetic diversity in today's available germplasm. This has also reduced the diversity in traits necessary for breeders to develop improved varieties. Many strategies have been developed to improve both genetic and trait diversity in crops, from backcrossing with wild relatives, to chemical/radiation mutagenesis, to genetic engineering. However, even with recent advances in genetic engineering we still face the rate limiting step of identifying which genes and mutations we should target to generate diversity in specific traits. Here, we apply a comparative evolutionary approach, pairing phylogenetic and expression analyses to identify potential candidate genes for diversifying soybean (Glycine max) canopy cover development via the nuclear auxin signaling gene families, while minimizing pleiotropic effects in other tissues. In soybean, rapid canopy cover development is correlated with yield and also suppresses weeds in organic cultivation. We identified genes most specifically expressed during early canopy development from the TIR1/AFB auxin receptor, Aux/IAA auxin co-receptor, and ARF auxin response factor gene families in soybean, using principal component analysis. We defined Arabidopsis thaliana and model legume species orthologs for each soybean gene in these families allowing us to speculate potential soybean phenotypes based on well-characterized mutants in these model species. In future work, we aim to connect genetic and functional diversity in these candidate genes with phenotypic diversity in planta allowing for improvements in soybean rapid canopy cover, yield, and weed suppression. Further development of this and similar algorithms for defining and quantifying tissue- and phenotype-specificity in gene expression may allow expansion of diversity in valuable phenotypes in important crops.

Optimizing transplanting densities for lowland rice production under low-yielding environments in the Madagascar highlands

ContextRice yield is low at 2.1 t ha−1 in sub-Saharan Africa. Increased yield is a critical challenge to food security and environmental conservation in this region. However, smallholder farmers have limited access to irrigation, mineral fertilizers, and improved crop varieties. One approach that even resource-limited farmers can easily manipulate is to optimize planting densities. However, there is limited empirical evidence to provide technical recommendations under such low-yielding conditions. ObjectiveThis study aimed to identify the effect of dense transplanting on lowland rice yields under low-yielding conditions, with a target range below 5 t ha−1. MethodsMulti-field trials were implemented with transplanting densities of a regular rate at 25–26.7 hills m−2, a doubled rate at 50–53.3 hills m−2, and a tripled rate at 88.9 hills m−2 in the central highlands of Madagascar, where rice yields are limited by nutrient deficiency and low temperature. Canopy coverage and cumulative intercepted radiation (CIR) were monitored from transplantation to maturity using digital imagery analysis. Field observations (n=306) and four-year household surveys (n=356) were combined to calculate the costs and benefits of changing transplanting densities. ResultsDoubling densities from 25.0–26.7 hills m−2 to 50.0–53.3 hills m−2 had a consistent yield advantage by approximately 0.4 t ha−1 across a yield range of 1.8 t ha−1–4.4 t ha−1. The yield was further increased by tripling the transplanting densities to 88.9 hills m−2 when the yield range was 1.9–2.3 t ha−1. The yield advantage of higher transplanting densities was attributed to a greater CIR at the initial growth stages and a significantly greater panicle number. Household surveys and field observations indicated that the benefit of yield gain was more than three times greater than the additional cost of doubling the seed amounts. No significant yield differences were observed by changing the transplanting densities when the yield level was higher than 5 t ha−1 or lower than 1.3 t ha−1 where substantial reductions in grain fertility occurred owing to low-temperature stress. ConclusionsA relatively high transplanting density of 50–53.3 hills m−2 or even higher is recommended to ensure initial canopy development and panicle number in low-yielding conditions where individual plant growth is stagnant, except in fields with high risks of grain set failure. ImplicationsThis study provides an easy-to-use opportunity for smallholder farmers to increase their rice yield. Further studies are required to determine whether these findings apply to warmer climatic conditions.

Monitoring and Optimization of Potato Growth Dynamics under Different Nitrogen Forms and Rates Using UAV RGB Imagery

The temporal dynamics of canopy growth are closely related to the accumulation and distribution of plant dry matter. Recently, unmanned aerial vehicles (UAVs) equipped with various sensors have been increasingly adopted in crop growth monitoring. In this study, two potato varieties were used as materials, and treated with different combinations of nitrogen forms (nitrate and ammonium) and application rates (0, 150, and 300 kg ha−1). A canopy development model was then constructed using low-cost time-series RGB imagery acquired by UAV. The objectives of this study were to quantify the variation in canopy development parameters under different nitrogen treatments and to explore the model parameters that represent the dynamics of plant dry matter accumulation, as well as those that contribute significantly to yield. The results showed that, except for the thermal time to canopy senescence (t2), other parameters of the potato canopy development model exhibited varying degrees of variation under different nitrogen treatments. The model parameters were more sensitive to nitrogen forms, such as ammonium and nitrate, than to application rates. The integral area (At) under the canopy development curve had a direct effect on plant dry matter accumulation (path coefficient of 0.78), and the two were significantly positively correlated (Pearson correlation coefficient of 0.93). Integral area at peak flowering (AtII) was significantly correlated with yield for both single and mixed potato varieties, having the greatest effect on yield (total effect of 1.717). In conclusion, UAV-acquired time-series RGB imagery could effectively quantify the variation of potato canopy development parameters under different nitrogen treatments and monitor the dynamic changes in plant dry matter accumulation. The regulation of canopy development parameters is of great importance and practical value for optimizing nitrogen management strategies and improving yield.

RGB‐based indices for estimating cover crop biomass, nitrogen content, and carbon:nitrogen ratio

AbstractPlant cover and biochemical composition are essential parameters for evaluating cover crop management. Destructive sampling or estimates with aerial imagery require substantial labor, time, expertise, or instrumentation cost. Using low‐cost consumer and mobile phone cameras to estimate plant canopy coverage and biochemical composition could broaden the use of high‐throughput technologies in research and crop management. Here, we estimated canopy development, tissue nitrogen, and biomass of medium red clover (Trifolium pratense L.), a perennial forage legume and common cover crop, using red‐green‐blue (RGB) indices collected with standard settings in non‐standardized field conditions. Pixels were classified as plant or background using combinations of four RGB indices with both unsupervised machine learning and preset thresholds. The excess green minus red (ExGR) index with a preset threshold of zero was the best index and threshold combination. It correctly identified pixels as plant or background 86.25% of the time. This combination also provided accurate estimates of crop growth and quality: Canopy coverage correlated with red clover biomass (R2 = 0.554, root mean square error [RMSE] = 219.29 kg ha−1), and ExGR index values of vegetation pixels were highly correlated with clover nitrogen content (R2 = 0.573, RMSE = 3.5 g kg−1) and carbon:nitrogen ratio (R2 = 0.574, RMSE = 1.29 g g−1). Data collection were simple to implement and stable across imaging conditions. Pending testing across different sensors, sites, and crop species, this method contributes to a growing and open set of decision support tools for agricultural research and management.

Bed, ridge and planting configurations influence crop performance in field-transplanted hybrid potato crops

Current cultivation practices for field transplanted potato crops grown from nursery-raised hybrid potato seedlings are mostly borrowed from the tuber-based conventional system. Most studies on field performance of field transplanted seedling crops have largely reported the use of ridged rows and in exceptional cases, the use of beds. It is therefore critical to assess the feasibility of the use of alternative ridge or bed systems for cultivation of field-transplanted nursery-raised potato seedlings considering the differences in physiological behaviour of crops grown from different starting materials. This study assessed the effects of six systems which included bed and ridge systems of different dimensions and planting configurations for field transplanted seedling crops. Field crop establishment, canopy growth and development as well as yield and yield components were assessed. In general, systems that boasted high plant densities resulted in faster canopy development and higher number of tubers and tuber yield. Bed systems (raised and flat beds; 8.0 plants m−2) therefore gave the highest numbers of tubers and tuber yield across all treatments. These systems also produced the most tubers in all tuber size classes resulting in the highest yields in all classes. Standard ridge systems (full- and half ridges; 0.75 m row distance), had the lowest plant populations (5.3 plants m−2) which resulted almost always in fewer tubers and lower yield. Other ridge systems (0.9-m and 0.5-m ridge systems), although having higher plant densities than the standard ridge systems (8.9 and 8.0 plants m−2, respectively) still performed poorer than the bed systems. The small and compact ridges in the 0.5-m ridge system and the compact arrangement of plants in the 0.9-m ridge system caused these effects. Conclusively, based on this study, productivity in field transplanting systems is highly influenced by plant density. Further, cultivation systems boasting higher planting densities should be recommended when the goal for production is to produce large quantities of seed tubers (> 35; ≤ 50 mm).

Inversion of Cotton Soil and Plant Analytical Development Based on Unmanned Aerial Vehicle Multispectral Imagery and Mixed Pixel Decomposition

In order to improve the accuracy of multispectral image inversion of soil and plant analytical development (SPAD) of the cotton canopy, image segmentation methods were utilized to remove the background interference, such as soil and shadow in UAV multispectral images. UAV multispectral images of cotton bud stage canopies at three different heights (30 m, 50 m, and 80 m) were acquired. Four methods, namely vegetation index thresholding (VIT), supervised classification by support vector machine (SVM), spectral mixture analysis (SMA), and multiple endmember spectral mixture analysis (MESMA), were used to segment cotton, soil, and shadows in the multispectral images of cotton. The segmented UAV multispectral images were used to extract the spectral information of the cotton canopy, and eight vegetation indices were calculated to construct the dataset. Partial least squares regression (PLSR), Random forest (FR), and support vector regression (SVR) algorithms were used to construct the inversion model of cotton SPAD. This study analyzed the effects of different image segmentation methods on the extraction accuracy of spectral information and the accuracy of SPAD modeling in the cotton canopy. The results showed that (1) The accuracy of spectral information extraction can be improved by removing background interference such as soil and shadows using four image segmentation methods. The correlation between the vegetation indices calculated from MESMA segmented images and the SPAD of the cotton canopy was improved the most; (2) At three different flight altitudes, the vegetation indices calculated by the MESMA segmentation method were used as the input variable, and the SVR model had the best accuracy in the inversion of cotton SPAD, with R2 of 0.810, 0.778, and 0.697, respectively; (3) At a flight altitude of 80 m, the R2 of the SVR models constructed using vegetation indices calculated from images segmented by VIT, SVM, SMA, and MESMA methods were improved by 2.2%, 5.8%, 13.7%, and 17.9%, respectively, compared to the original images. Therefore, the MESMA mixed pixel decomposition method can effectively remove soil and shadows in multispectral images, especially to provide a reference for improving the inversion accuracy of crop physiological parameters in low-resolution images with more mixed pixels.

Implementation of a slope stability method in the CRITERIA-1D agro-hydrological modeling scheme

AbstractThis paper presents the implementation of a slope stability method for rainfall-induced shallow landslides in CRITERIA-1D, which is an agro-hydrological model based on Richards’ equation for transient infiltration and redistribution processes. CRITERIA-1D can simulate the presence and development of roots and canopies over space and time, the regulation of transpiration activity based on real meteorological data, and the evaporation reduction caused by canopies. The slope can be considered composed of a multi-layered soil, leading to the possibility of simulating the bedrock and of setting an initial water table level. CRITERIA-1D can consider different soil horizons characterized by different hydraulic conductivities and soil water retention curves, thus allowing the simulation of capillarity barriers. The validation of the proposed physically based slope stability model was conducted through the simulation of the collected water content and water potential data of an experimental slope. The monitored slope is located close to Montuè, in the north-eastern sector of Oltrepò Pavese (northern Apennines—Italy). Just close to the monitoring station, a shallow landslide occurred in 2014 at a depth of around 100 cm. The results show the utility of agro-hydrological modeling schemes in modeling the antecedent soil moisture condition and in reducing the overestimation of landslides events detection, which is an issue for early warning systems and slope management related to rainfall-induced shallow landslides. The presented model can be used also to test different bioengineering solutions for slope stabilization, especially when data about rooting systems and plant physiology are known.

Leaf area estimation based on ANFIS using embedded system and PV panel

Leaf area is one of the important parameters for plant canopy development. It is used as an indicator closely related to plant growth in several studies on plant production. However, most leaf area meters used today are costly and rely on human observations. This situation may be limiting for researchers in terms of having proper leaf area measuring devices. The reliance on human-focused measurements leads to human errors. Digital scanners and cameras, digital image processing-based estimation methods, paper weighing, grid counting, regression equations, width and height correlation models, planimeters, laser optics, and handheld scanners can be used to determine leaf area. However, some of these methods are expensive and unnecessary for simple studies. Therefore, this study aims to design and implement an embedded system with a simpler, cheaper alternative to the currently used methods and devices, minimizing human errors. The proposed embedded system serves as a tool for measuring leaf area using a photovoltaic panel (PV) and an Adaptive Neuro-Fuzzy Inference System (ANFIS). In the study, geometric shapes with known areas are used as the learning data, and real plant leaves with known areas are used in the testing process. As a result, the prediction made by ANFIS is observed to have an accuracy of R2 = 0.99.

Improving the estimation of rice above-ground biomass based on spatio-temporal UAV imagery and phenological stages.

Unmanned aerial vehicles (UAVs) equipped with visible and multispectral cameras provide reliable and efficient methods for remote crop monitoring and above-ground biomass (AGB) estimation in rice fields. However, existing research predominantly focuses on AGB estimation based on canopy spectral features or by incorporating plant height (PH) as a parameter. Insufficient consideration has been given to the spatial structure and the phenological stages of rice in these studies. In this study, a novel method was introduced by fully considering the three-dimensional growth dynamics of rice, integrating both horizontal (canopy cover, CC) and vertical (PH) aspects of canopy development, and accounting for the growing days of rice. To investigate the synergistic effects of combining spectral, spatial and temporal parameters, both small-scale plot experiments and large-scale field testing were conducted in Jiangsu Province, China from 2021 to 2022. Twenty vegetation indices (VIs) were used as spectral features, PH and CC as spatial parameters, and days after transplanting (DAT) as a temporal parameter. AGB estimation models were built with five regression methods (MSR, ENet, PLSR, RF and SVR), using the derived data from six feature combinations (VIs, PH+CC, PH+CC+DAT, VIs+PH +CC, VIs+DAT, VIs+PH+CC+DAT). The results showed a strong correlation between extracted and ground-measured PH (R2 = 0.89, RMSE=5.08 cm). Furthermore, VIs, PH and CC exhibit strong correlations with AGB during the mid-tillering to flowering stages. The optimal AGB estimation results during the mid-tillering to flowering stages on plot data were from the PLSR model with VIs and DAT as inputs (R 2 = 0.88, RMSE=1111kg/ha, NRMSE=9.76%), and with VIs, PH, CC, and DAT all as inputs (R 2 = 0.88, RMSE=1131 kg/ha, NRMSE=9.94%). For the field sampling data, the ENet model combined with different feature inputs had the best estimation results (%error=0.6%-13.5%), demonstrating excellent practical applicability. Model evaluation and feature importance ranking demonstrated that augmenting VIs with temporal and spatial parameters significantly enhanced the AGB estimation accuracy. In summary, the fusion of spectral and spatio-temporal features enhanced the actual physical significance of the AGB estimation models and showed great potential for accurate rice AGB estimation during the main phenological stages.

Evaluation of chemical attributes of soils: definition of management zones in silvipastoral system

Soil chemical analysis is a fundamental to achieve optimal cultivation conditions in a productive silvopastoral system, essential for the planning and management of this activity. The objective of this research is to map the spatial distribution of nutrients in the soil in a 5-year-old silvopastoral system composed of brachiaria-brizantha and jacarandá-da-bahia, and to analyze the effect of tree density and canopy on nutrient levels. The study was conducted at the experimental area of the Capixaba Institute for Research, Technical Assistance, and Rural Extension (INCAPER) in the district of Pacotuba, ES. The delimitation of the work area was done using geographic information systems, to create a regular grid of points to enable systematic soil sampling. A total of 75 disturbed soil samples were collected at depths of 0-0.10 m to obtain quantitative soil nutrient data. High-quality images obtained using a remotely piloted aircraft (RPA) allowed for the overlay of soil nutrient data, pasture data, forest inventory data, and sample plot data to assist in comparative analyses. This enabled identification of the areas with lower canopy development, particularly in the northeast portion. By cross-referencing the spatialized soil nutrient data with the data obtained through RPA, it was possible to correlate them with the lower or higher development of the system. The use of RPA, together with a georeferenced database, proved to be an innovative and powerful tool to support decision-making, allowing for the monitoring of system evolution and serving as a basis for evaluating the relationship between chemical elements and tree canopies. Keywords: geostatistics, precision agriculture, soil nutrients.

How global sensitive is the AquaCrop model to input parameters? A case study of silage maize yield on a regional scale

IntroductionAquaCrop is a water-driven crop growth model that simulates aboveground biomass production in croplands. This study aimed to identify the driving parameters of the AquaCrop model for the model calibration and simplification to fill the research gap in intermediate environmental conditions between sub-tropical sub-humid and temperate sub-humid climates for silage maize.MethodsTo this end, we applied global sensitivity analysis (GSA) by combining the Morris method and the Extended Fourier Amplitude Sensitivity Test (EFAST) on crop yield output. The process involved a field sampling of soil and crop of silage maize carried out in the agricultural fields of Ghale-Nou, southern Tehran, Iran, in the summer of 2019 in order to measure certain model parameters.Results and discussionIn compliance with the Morris method, 30 parameters were identified as the least sensitive, while results from the EFAST test showed 9 parameters as contributing to the highest sensitivities in the model. The results clearly point to the capacity of employing a combination of both methods to attain a more efficient model calibration. Particular root, soil, canopy development, and biomass production parameters were influential and merit attention during calibration. Instead, parameters describing crop responses to water stress were acting rather insensitive in this study condition. The insights gained from this study, i.e., assessing parameter ranges and distinguishing between less sensitive and more sensitive parameters based on environmental and crop conditions, have the potential to be applied to other crop growth models with caution.

A satellite-field phenological bridging framework for characterizing community-level spring forest phenology using multi-scale satellite imagery

Forest phenology, as a sensitive indicator of a forest’s response to climate change and variability, has long been monitored using remote sensing, yet has seldom been interpreted or validated with spatially compatible, community-level field phenological observations. In temperate deciduous forests, multiple spring phenological phases are critical for modeling carbon storage and biogeochemical cycles. The simultaneous detection of all these critical phenological phases at the community level remains a long-standing challenge. To tackle these challenges, the objective of this study is to develop a novel satellite-field phenological bridging framework for characterizing all key spring phenological phases of a fragmented deciduous forest using multi-scale satellite time series. The framework consists of four key components: deep learning-based spatiotemporal image fusion, satellite-based forest phenology modeling, satellite-based forest phenological metric extraction, and field-based forest community phenological characterization. With the devised framework, we extract a total of 24 satellite phenological metrics of Trelease Woods, a forest fragment near Urbana, IL, USA. From weekly field phenological observations of 148 canopy trees of 15 common species of the forest community, we devise three summative field phenological indices to characterize community-level phenological states in spring. Under the satellite-field bridging framework, events during each key spring forest phenological phase (i.e., bud swell, budburst/leafing out, leaf expansion, and leaf maturity) are successfully detected using the fusion imagery (MAE from 1.1 to 2.9 days and bias from -2.4 to 1.5 days). However, the satellite detection of the earliest field events may be influenced by understory plants, soil background, and snow. The subsequent multi-scale, satellite phenological analysis underscores the importance of taking into account spatial scale and representation from both satellite and field phenological perspectives in building corresponding bridging relationships. Among the extracted pheno-metrics, bridging the threshold-based metrics to field phenological indices results in the highest accuracy (MAE less than 3 days and bias less than 2 days). The strong agreement among the field indices demonstrates the effectiveness of our field phenological surveying approach in generating community-wide forest phenological representation. Our study innovatively scales up the field phenological observations from the individual trees to the species to the community level, and the devised framework enables accurate retrieval of all key phenological events of community-wide, spring canopy development of the forest fragment.