Phenological State Research Articles

Assessing the phenological state of evergreen conifers using hyperspectral imaging time series

Phenology is a reliable indicator of vegetation condition and ecological changes in the environment. Plant Spectral Phenology (PSP) offers the potential for the development of automated, rapid, and wide-area vegetation monitoring systems. The spectral characteristics of plants (vegetation) are employed as metrics of PSP, which can be sensed both proximally and remotely. A key objective is to undertake a comparative analysis of the results of PSP versus those of phenology based on visual observations. The resolution of this issue is of paramount importance for the coordination of phenological studies at diverse levels (ground, surface, and remote), thus ensuring the continuity of phenological studies conducted prior to the advent of PSP. This issue is particularly pronounced in the case of evergreen conifers. The present study focuses on four evergreen conifers: Thuja occidentalis, Platycladus orientalis, Pinus sylvestris and P. nigra subsp. pallasiana. Hyperspectral imaging was performed under laboratory conditions using a Cubert UHD-185 hyperspectral camera. Concomitantly, phenological observations were conducted. The spectral time series yielded 79 chlorophyll-sensitive and carotenoid-sensitive Vegetation Indices (VIs), which were then used to construct double logistic functions. A significant proportion of the VIs exhibited a high degree of correctness with regard to the aforementioned functions, as indicated by the value of R2 exceeding 0.7. The values of the principal stages of seasonal development of evergreen conifers, namely the Start of Season (SOS), End of Season (EOS), Position of Peak value (POP) and Length of Season (LOS), were calculated using double logistic functions. These stages were matched to the phenological phases of development of the experimental plants. The values of SOS, EOS, POP and LOS varied significantly depending on the VIs used as a metric as well as the evergreen conifers. The lowest variability by metrics is observed in SOS, while the maximum is observed in EOS and POP. The results obtained may be of importance for the choice of criterion for the comparison of PSP with phenology based on visual observations and the most suitable VIs for these purposes.

Nitrogen availability and summer drought, but not N:P imbalance, drive carbon use efficiency of a Mediterranean tree-grass ecosystem.

All ecosystems contain both sources and sinks for atmospheric carbon (C). A change in their balance of net and gross ecosystem carbon uptake, ecosystem-scale carbon use efficiency (CUEECO), is a change in their ability to buffer climate change. However, anthropogenic nitrogen (N) deposition is increasing N availability, potentially shifting terrestrial ecosystem stoichiometry towards phosphorus (P) limitation. Depending on how gross primary production (GPP, plants alone) and ecosystem respiration (RECO, plants and heterotrophs) are limited by N, P or associated changes in other biogeochemical cycles, CUEECO may change. Seasonally, CUEECO also varies as the multiple processes that control GPP and respiration and their limitations shift in time. We worked in a Mediterranean tree-grass ecosystem (locally called 'dehesa') characterized by mild, wet winters and summer droughts. We examined CUEECO from eddy covariance fluxes over 6 years under control, +N and + NP fertilized treatments on three timescales: annual, seasonal (determined by vegetation phenological phases) and 14-day aggregations. Finer aggregation allowed consideration of responses to specific patterns in vegetation activity and meteorological conditions. We predicted that CUEECO should be increased by wetter conditions, and successively by N and NP fertilization. Milder and wetter years with proportionally longer growing seasons increased CUEECO, as did N fertilization, regardless of whether P was added. Using a generalized additive model, whole ecosystem phenological status and water deficit indicators, which both varied with treatment, were the main determinants of 14-day differences in CUEECO. The direction of water effects depended on the timescale considered and occurred alongside treatment-dependent water depletion. Overall, future regional trends of longer dry summers may push these systems towards lower CUEECO.

It is good to be average: Ecological correlates of breeding phenology in an Arctic seabird, Alle alle (Dovekie)

Abstract Recognizing the deviation in an animal’s behavior from a population mean is not only necessary to understand the evolution and stability of the whole system but also to predict the future of a population in a changing environment. Arctic seabirds are expected to exhibit high synchronization in timing of breeding at the population level, due to highly seasonal and harsh environmental conditions. Nevertheless, even in such a highly synchronized system, there are always some earlier and later breeders, and the drivers of inter-pair variation remain largely unknown. Using an 8-year dataset on Alle alle (Dovekie), a small Arctic seabird, we examined potential drivers of breeding phenology. We found that A. alle pairs were quite repeatable in their phenology, and preserved their phenological status, with their chicks hatching consistently before, during, or after the population median date for hatching, despite that the calendar position of the median shifted between years. This would suggest that timing of breeding is associated with some pair characteristics, either via properties of the nest and/or some partner’s traits. However, breeding phenology of the pair was not dependent on nest location, pair bond duration or morphological similarity of the partners. Importantly, timing of breeding was negatively associated with chick growth rate, indicating fitness consequences of phenology. A simulation in our study further suggests that the chance of fledgling survival in the context of predation may be the highest for the chicks that hatched during the peak of the hatching period. While our results suggest that the timing of the breeding is important for the reproductive outcome, further research is required to determine the drivers of the consistent breeding phenology within A. alle pairs.

Remote Sensing Applied to Pasture Monitoring: A Review

Objective: The goal of this study was to review the results obtained by various authors for large-scale pasture monitoring via remote sensing; to monitor the development and degradation conditions of pastures according to case studies; and to analyze data, images, and Geographic Information System (GIS) procedures in relevant works. Theoretical Framework: In this topic, the principles that guided the literature review are presented, including a discussion of the search for remote sensing results related to pastures in large geographical areas, and case studies in works that denote the technological potential from field sampling and results worldwide are also presented. Method: Research methods associated with different themes, keywords, and regions were adopted to identify results from research in the area of remote sensing applied to surveying the conditions of pastures in the different typologies in the studied regions. Results and Discussion: The literature review in this study highlights the advances and methodologies developed for the evaluation of pasture conditions and mapping, from which it was possible to observe the stage of degradation of this vegetation, which is essential for agricultural production. Research Implications: The potential of using remote sensing materials and mechanisms for pasture assessment, mapping areas in stages of degradation and identifying vegetation vigor is highly valuable in large-scale pasture management. The presented literature indicates that pasture vegetation varies greatly according to geographic region, indicating great complexity in deriving its phenological conditions and showing that field campaigns are essential for the description and establishment of a relationship between the foliar canopy and sensor response. Originality/Value: This literature review addresses large-scale remote sensing surveys of pastures, contributing to research on the conditions of this vegetation. The bibliography presented in this review can support studies on mapping the pasture degradation process through remote sensing images and data.

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.

Large-Scale Monitoring of Bird Communities along an Altitudinal Gradient in Two Central High Atlas Valleys

Altitude is one of the determinant factors that influence the spatial distribution of birds and their species richness. However, few investigations studied its influence on African bird communities over a wide gradient in North Africa. Considering this knowledge gap, 194 sampling points were carried out along altitudinal gradient (773 to 2595 m) in two adjacent mountainous valleys in the Central High Atlas of Beni Mellal in Morocco, Aït Bouguemmaz (AB), and Ahançal (OA). Each valley was divided into three altitudinal intervals (low, medium, and high zones). For each spatial subdivision, we studied the phenological cycle, in the sense that each valley underwent a four-season monitoring, during 2018 and 2019. The results showed 131 species in the study area in 43 families; both valleys had similar community richness, but they differed by the phenological status of their species. Indeed, the increase in altitude led to a decrease in bird species richness, while it does not show any correlation with abundance. In addition, richness and abundance increased during the high-productivity seasons (i.e., spring and summer). Although these results were expected, they provide an opportunity to compare these results with higher latitude communities in Europe and to better focus future large-scale investigations on the combined influence of latitude and altitude on birds.

Vegetation and Dormancy States Identification in Coniferous Plants Based on Hyperspectral Imaging Data

Conifers are a common type of plant used in ornamental horticulture. The prompt diagnosis of the phenological state of coniferous plants using remote sensing is crucial for forecasting the consequences of extreme weather events. This is the first study to identify the “Vegetation” and “Dormancy” states in coniferous plants by analyzing their annual time series of spectral characteristics. The study analyzed Platycladus orientalis, Thuja occidentalis and T. plicata using time series values of 81 vegetation indices and 125 spectral bands. Linear discriminant analysis (LDA) was used to identify “Vegetation” and “Dormancy” states. The model contained three to four independent variables and achieved a high level of correctness (92.3 to 96.1%) and test accuracy (92.1 to 96.0%). The LDA model assigns the highest weight to vegetation indices that are sensitive to photosynthetic pigments, such as the photochemical reflectance index (PRI), normalized PRI (PRI_norm), the ratio of PRI to coloration index 2 (PRI/CI2), and derivative index 2 (D2). The random forest method also diagnoses the “Vegetation” and “Dormancy” states with high accuracy (97.3%). The vegetation indices chlorophyll/carotenoid index (CCI), PRI, PRI_norm and PRI/CI2 contribute the most to the mean decrease accuracy and mean decrease Gini. Diagnosing the phenological state of conifers throughout the annual cycle will allow for the effective planning of management measures in conifer plantations.

Remote Sensing Evaluation Drone Herbicide Application Effectiveness for Controlling Echinochloa spp. in Rice Crop in Valencia (Spain).

Rice (Oryza sativa L.) is a staple cereal in the diet of more than half of the world's population. Within the European Union, Spain is a leader in rice production due to its climate and tradition, accounting for 26% of total EU production in 2020. The Valencian rice area covers around 15,000 hectares and is strongly influenced by biotic and abiotic factors. An important biotic factor affecting rice production is weeds, which compete with rice for sunlight, water and nutrients. The dominant weed in Spain is Echinochloa spp., although wild rice is becoming increasingly important. Rice cultivation in Valencia takes place in the area of L'Albufera de Valencia, which is a natural park, i.e., a special protection area. In this natural area, the use of phytosanitary products is limited, so it is necessary to use the minimum amount possible. Therefore, the objective of this work is to evaluate the possibility of using remote sensing effectively to determine the effectiveness of the application of the herbicide cyhalofop-butyl by drone for the control of Echinochloa spp. in rice crops in Valencia. The results will be compared with those obtained by using sterilisation machines (electric backpack sprayers) to apply the herbicide. To evaluate the effectiveness of the application, the reflectance obtained by the satellite sensors in the red and near infrared (NIR) wavelengths, as well as the normalised difference vegetation index (NDVI), were used. The remote sensing results were analysed and complemented by the number of rice plants and weeds per area, plant dry weight, leaf area, BBCH phenological state, SPAD index values, chlorophyll content and relative growth rate. Remote sensing is validated as an effective tool for determining the efficacy of an herbicide in controlling weeds applied by both the drone and the electric backpack sprayer. The weeds slowed down their development after the treatment. Depending on the phenological state of the crop and the active ingredient of the herbicide, these results are applicable to other areas with different climatic and environmental conditions.

A Novel Method for Mapping Moso Bamboo Forests Using Remote Sensing Data With the Consideration of Phenological Status

A Novel Method for Mapping Moso Bamboo Forests Using Remote Sensing Data With the Consideration of Phenological Status

An ecological survey of Laro-limicolae in Northeastern Algeria

We conducted an inventory and characterization of Laro-limicolae settlement at three Ramsar sites located in Eastern Numidia (Lake Tonga, Lake Oubeira and El Mellah lagoon) between 2014 and 2016. We identified 27 species belonging to six families, dominated by Scolopacidae (10 sp.) and Laridae (9 sp.) followed by Charadriidae (4 sp.), Recurvirostridae (2 sp.) and for Glareolidae and Haematopodidae families (1 sp.) Inventoried species exhibited different phenological status: resident (26%), wintering (44%), migratory breeders (19%) and wintering/breeding (11%). Population analysis showed the presence of protected species such as black-tailed godwit and Northern lapwing. Bird counts showed monthly variations in total abundance and richness, peaking during winter. Highest numbers were recorded in January and lowest numbers in August. Analysis of diversity and equitability indices indicated that maximum values (H’ = 3.01 and E = 0.94) were noted at El Mellah lagoon in November and at Lake Oubeira in June. Our study provides recent and unpublished data on a group of birds that are little-documented for North Africa, particularly for Algeria. Our findings open the way for further, more detailed work on the subject, and may serve to develop action plans for the management and conservation of these bird populations and their habitats.

Alternative method for determining leaf CO2 assimilation without gas exchange measurements: Performance, comparison and sensitivity analysis.

We present an alternative method to determine leaf CO2 assimilation rate (An ), eliminating the need for gas exchange measurements in proximal and remote sensing. This method combines the Farquhar-von Caemmerer-Berry photosynthesis model with mechanistic light reaction (MLR) theory and leaf energy balance (EB) analysis. The MLR theory estimates the actual electron transport rate (J) by leveraging chlorophyll fluorescence via pulse amplitude-modulated fluorometry for proximal sensing or sun-induced chlorophyll fluorescence measurements for remote sensing, along with spectral reflectance. The EB equation is used to directly estimate stomatal conductance from leaf temperature. In wheat and soybean, the MLR-EB model successfully estimated An variations, including midday depression, under various environmental and phenological conditions. Sensitivity analysis revealed that the leaf boundary layer conductance (gb ) played an equal, if not more, crucial role compared to the variables for J. This was primarily caused by the indirect influence of gb through the EB equation rather than its direct impact on convective CO2 exchange on the leaf. Although the MLR-EB model requires an accurate estimation of gb , it can potentially reduce uncertainties and enhance applicability in photosynthesis assessment when gas exchange measurements are unavailable.

Determination of critical nitrogen curve in cauliflower (Brassica oleracea var. Botrytis) with the NBI index of multiplex®

The concentration of nitrogen in plants is usually determined through chemical analysis, although there are alternatives such as the measurement of chlorophyll as an indirect estimate of the nitrogen content. Currently, the reflectance, transmittance and fluorescence methods in specific regions of the spectrum are used to diagnose the nutritional status of plants in nitrogen. The objective has been to evaluate the use of sensors based on the principles of fluorescence and transmittance in the same test plots. The tests were carried out at the Valdegón Farm, at the Agro-food Technological Research and Development Center of the Government of La Rioja, at Agoncillo (La Rioja), Spain, in the years 2012, 2013 and 2014. Periodic sampling of nitrogen in soil, plant biomass and sheet measurements with Multiplex® were carried out. For the NBI index of the Multiplex high correlations have been found with the nitrogen content of the plant, which has served, depending on the phenological state of the crop, to adjust some functions that relate the measurements made with the biomass of the crop in the case of the NBI index. In the critical curves obtained for the sensors, it is observed that from a biomass of approximately 1 Mg/ha (50% of covered ground) the measurement stabilizes and it is from this moment when deficit treatments start to be distinguished from the non-deficit ones. Therefore, these models could be used to determine a nitrogen-based nutritional deficit and be able to correct it by fertilization.

Development and validation of phenological models for eight varieties of sweet cherry (Prunus avium L.) growing under Mediterranean climate condition

The projected increase in temperatures caused by climate change will modify the phenological stages of sweet cherry in Mediterranean-type climate regions. This study aimed to develop and validate phenology models for eight sweet cherry varieties (Sam, Lapins, Rainier, Royal D, Santina, Regina, Kordia, Sweet Heart) in the Maule Region, Chile. The models were based on growing degree days (GDD), and the stochastic monomolecular model was employed to account for the randomness caused by measurement errors affecting the prediction of phenological states. A dataset of phenological stages was compiled over four growing seasons: 2017-2018, 2018-2019, 2019-2020, and 2020-2021. The results revealed a significant correlation (with R2 values between 0.96 and 0.98) the modified phenological (PS) scale of Biologische Bundesanstalt, Bundessortenamt, and Chemical Industry (BBCH) and GDD for all eight varieties. The validation of the model demonstrated root mean square error (RMSE), mean absolute error (MAE), and model efficiency (FE) values ranging from 1 to 1.3, 0.79 to 1.1, and 0.82 to 0.92, respectively. Finally, monomolecular model estimated the modified scale of BBCH with errors less than 2 %.

Monitoring the effects of weed management strategies on tree canopy structure and growth using UAV-LiDAR in a young almond orchard

The primary objective of this study was to assess the potential effect of integrated weed management (IWM) on canopy structure and growth in a young almond orchard using unmanned aerial vehicle (UAV) LiDAR point cloud data. The experiment took place in the Neve Ya’ar Model Farm, with four IWM strategies tested: (1) standard herbicide, (2) physical-mechanical, (3) cover crops, and (4) integrated management combining herbicide and mowing. One pre-treatment sessions in 2019 and two experiment sessions in 2020–2021 were conducted. During these three experimental sessions, manual measurements, including trunk diameter (TD), were taken for all trees in the orchard. Six canopy growth parameters (e.g., tree height and volume), and functionality parameters (e.g., leaf density, gap fraction profile (GFP) and entropy) were calculated for individual trees using UAV-LiDAR data and were compared to manual measurement (i.e., TD). The results indicated that the herbicide treatment enabled effectively maintained weed coverage at approximately 5–10 % throughout the experiment, demonstrating successful control over weed growth. In contrast, the integrated weed management strategy yielded the highest weed cover (∼50 %). With the view of replacing ground manual measurements with UAV-LiDAR-derived measurements, we explored the relationships between TD and UAV-LiDAR-derived volume and GFP. The relationships between TD and GFP were with R2 values of 0.43 and 0.57 for 2021, respectively. The relationship between GFP and volume showed an R2 value of 0.65 for both years. We observed significant differences between IWM for TD, volume, and GFP in both years, indicating on higher tree development in the herbicide and integrated management treatments. The standard herbicide-based management yielded the highest crop yield, while the cover crop strategy yielded the lowest per plot. Finally, random forest (RF) model was used to identify key factors that significantly influence tree volume, TD, and GFP. The RF results showed that IWM explained about 30 % of tree structural changes, while the rest were attributed to environmental factors (i.e., topographical and climatic). The RF model R2 values ranged between 0.79 and 0.86, representing the topographical and climatic effects on tree structural parameters and growth, reflecting the natural field variability. Our study designed and successfully implemented a framework for estimating canopy characterization in young almond orchards subjected to varied IWM treatments. Ensuring the precise calibration of the LiDAR system and validating data, especially in complex canopy structures, can be challenging since the accuracy of ground-truth data has its errors. For future study, the fusion of LiDAR data with multispectral imagery or terrestrial LiDAR data can be considered, as well as more frequent UAV-LiDAR re-visits, offering insights into phenological states and higher resolution of canopy characteristics in orchards. This research enhances our understanding of the advantages and challenges of using UAV-LiDAR point cloud data analysis to assess the impact of IWM on young almond tree growth.

Seasonal variation of essential oil from Murraya koenigii (Rutaceae) and insecticidal potential against Aedes aegypti (Diptera: Culicidae)

Murraya koenigii is a plant with exploited pharmacological potential and wide traditional use in Asian culture. This research investigated the chemical composition and seasonal variation of essential oil constituents of M. koenigii cultivated in the city of Goiânia, Goiás, region under climatic influence of the Cerrado domain. The larvicidal potential of these essential oils against Aedes aegypti was investigated. The constituents were evaluated by Gas Chromatography coupled to Mass Spectrometry (GC/MS). Climatological information and phenological conditions were also considered. Samples grouped into chemically similar sets were used in bioassays to investigate larvicidal activity. The results show the essential oil characterized by the majority presence of β-phellandrene, z-caryophyllene, α-pinene, sabinene and isodaucene. The phenological stage had more influence on the chemical composition of the essential oil than climatic factors. All essential oil samples were effective against Ae. aegypti, with no significant change in lethal concentrations.

Springing forward: Migrating songbirds catch up with the start of spring in North America.

In temperate regions, the annual pattern of spring onset can be envisioned as a 'green wave' of emerging vegetation that moves across continents from low to high latitudes, signifying increasing food availability for consumers. Many herbivorous migrants 'surf' such resource waves, timing their movements to exploit peak vegetation resources in early spring. Although less well studied at the individual level, secondary consumers such as insectivorous songbirds can track vegetation phenology during migration as well. We hypothesized that four species of ground-foraging songbirds in eastern North America-two warblers and two thrushes-time their spring migrations to coincide with later phases of vegetation phenology, corresponding to increased arthropod prey, and predicted they would match their migration rate to the green wave but trail behind it rather than surfing its leading edge. We further hypothesized that the rate at which spring onset progresses across the continent influences bird migration rates, such that individuals adjust migration timing within North America to phenological conditions they experience en route. To test our hypotheses, we used a continent-wide automated radio telemetry network to track individual songbirds on spring migration between the U.S. Gulf Coast region and northern locations closer to their breeding grounds. We measured vegetation phenology using two metrics of spring onset, the spring index first leaf date and the normalized difference vegetation index (NDVI), then calculated the rate and timing of spring onset relative to bird detections. All individuals arrived in the southeastern United States well after local spring onset. Counter to our expectations, we found that songbirds exhibited a 'catching up' pattern: Individuals migrated faster than the green wave of spring onset, effectively closing in on the start of spring as they approached breeding areas. While surfing of resource waves is a well-documented migration strategy for herbivorous waterfowl and ungulates, individual songbirds in our study migrated faster than the green wave and increasingly caught up to its leading edge en route. Consequently, songbirds experience a range of vegetation phenophases while migrating through North America, suggesting flexibility in their capacity to exploit variable resources in spring.

Evaluation and zoning of thermal and phenological conditions of grape cultivation in Hamadan province

Evaluation and zoning of thermal and phenological conditions of grape cultivation in Hamadan province

Long-term trends of local bird populations based on monitoring schemes: are they suitable for justifying management measures?

Local biodiversity monitoring is important to assess the effects of global change, but also to evaluate the performance of landscape and wildlife protection, since large-scale assessments may buffer local fluctuations, rare species tend to be underrepresented, and management actions are usually implemented on local scales. We estimated population trends of 58 bird species using open-population N-mixture models based on count data in two localities in southeastern Spain, which have been collected according to a citizen science monitoring program (SACRE, Monitoring Common Breeding Birds in Spain) over 21 and 15 years, respectively. We performed different abundance models for each species and study area, accounting for imperfect detection of individuals in replicated counts. After selecting the best models for each species and study area, empirical Bayes methods were used for estimating abundances, which allowed us to calculate population growth rates (λ) and finally population trends. We also compared the two local population trends and related them with national and European trends, and species functional traits (phenological status, dietary, and habitat specialization characteristics). Our results showed increasing trends for most species, but a weak correlation between populations of the same species from both study areas. In general, local population trends were consistent with the trends observed at national and continental scales, although contrasting patterns exist for several species, mainly with increasing local trends and decreasing Spanish and European trends. Moreover, we found no evidence of a relationship between population trends and species traits. We conclude that using open-population N-mixture models is an appropriate method to estimate population trends, and that citizen science-based monitoring schemes can be a source of data for such analyses. This modeling approach can help managers to assess the effectiveness of their actions at the local level in the context of global change.

Effect of Regulated Deficit Irrigation (RDI) on the Growth and Development of Pear Fruit (Pyrus communis L.), var. Triunfo de Viena

The irrigation of crops represents 70% of the world’s water use. For crops grown in high-altitude tropical climates, due to the decrease in rainfall with increasing altitude, along with the effects of global warming, it is necessary to propose alternatives for sustainable fruit production with reduced water consumption. This study was carried out in Sesquilé, Colombia (South America), at an altitude of 2595 m a.s.l. for two successive years with pear trees, var. Triunfo de Viena. The objective of this work was to determine the effect of regulated deficit irrigation (RDI) on the absolute and relative growth rates of the equatorial diameter of the fruits, the fresh and dry weights, the number of fruits, the yield per tree and the water potential of the fruits. In 2014, during the rapid growth phase of the fruit, one group of plants was irrigated at 100% of the crop evapotranspiration (ETc) (control), another at 74% and a third group at 60%. In 2015, the three groups were irrigated at 100%, 48% and 27% of the ETc. The use of RDI did not significantly affect the growth of the fruits. This study showed that the ‘Triunfo de Viena’ pear tree not only has sufficient adaptive reserves, but also has good ecological plasticity under water stress conditions in high-altitude tropical climates. In cases where water is a limiting factor for pear tree production, RDI can obtain production rates similar to those of a regularly irrigated crop, as long as this technique is used and implemented in phenological states of low sensitivity to water stress without exceeding the tolerance limits of the plants to the stressor.

Estimation of Above-Ground Biomass for Pinus densata Using Multi-Source Time Series in Shangri-La Considering Seasonal Effects

Forest above-ground biomass (AGB) is the basis of terrestrial carbon storage estimation, and making full use of the seasonal characteristics of remote sensing imagery can improve the estimation accuracy. In this study, we used multi-source time series and sample plots with the Random Forest (RF) model to estimate the AGB. The sources included Sentinel-1 (S-1), Sentinel-2 (S-2), and the S-1 and S-2 combination (S-1S-2). Time series included single season, annual, and multi-season. This study aims to (1) explore the optimal image acquisition season to estimate AGB; (2) determine whether the ability to estimate the AGB of multi-seasonal imagery exceeded that of annual and single-season imagery; (3) discover the sensitivity of different data to AGB according to phenological conditions. The results showed that: (1) images acquired in autumn were more useful for AGB estimation than spring, summer, and winter; (2) the S-1 multi-seasonal AGB model had higher accuracy than the annual or single-season one; (3) in autumn and spring, S-1 had higher estimation accuracy than S-2, and in autumn and spring, estimation accuracy from S-1S-2 was higher than that from S-1 and S-2; (4) in 16 AGB estimation models, the best estimation accuracy was achieved by the autumn AGB model from S-1S-2 (R2 = 0.90, RMSE = 16.26 t/ha, p = 0.82, and rRMSE = 18.97). This study could be useful to identify the optimal image acquisition season for AGB estimation, thus reducing the economic cost of image acquisition and improving the estimation accuracy.