Pub Date : 2025-01-17DOI: 10.1016/j.agrformet.2025.110402
Tomas Poblete , Michael S. Watt , Henning Buddenbaum , Pablo J. Zarco-Tejada
<div><div>Radiata pine (<em>Pinus radiata</em> D. Don) is a widely planted tree species. Fertilizers, especially those containing leaf nitrogen (N) and phosphorous (P), are essential for maximizing growth. Nutrient deficiencies and excessive fertilization can limit growth, so monitoring is crucial. Leaf pigments such as chlorophyll <em>a</em>+<em>b</em> (C<sub>a+b</sub>) can be used to assess plant nutrition, specifically leaf N. Remote sensing approaches can be used to monitor forest condition by estimating C<sub>a+b</sub> content as a proxy for leaf N. Conventional methods for C<sub>a+b</sub> estimation are based on empirical relationships using sensitive spectral indices or inversions of Radiative Transfer Models (RTMs). However, the structural complexity of tree crowns composed of multiple layers of clumped leaves/needles and background and shadow effects challenge the use of the indices proposed for both leaf C<sub>a+b</sub> and leaf nitrogen assessment. This study compares the accuracy of methods for C<sub>a+b</sub> estimation in radiata pine using hyperspectral data collected from a greenhouse experiment over the growing season and from a field trial representing a stand with a complex structure. The methods used to predict needle C<sub>a+b</sub> from tree-crown spectra included: 1) empirical relationships between C<sub>a+b</sub> measurements and hyperspectral indices; 2) scaling-up of hyperspectral index-based C<sub>a+b</sub> predictive relationships through RTM simulations; and 3) RTM inversions of C<sub>a+b</sub> content. These methods were tested over two different segmentation strategies, including sunlit-vegetation and full-crown spectra, to assess the effects of the increased structural complexity.</div><div>Predictions of C<sub>a+b</sub> from the greenhouse experiment were generally higher for empirical models that used TCARI/OSAVI (Transformed Chlorophyll Absorption in Reflectance Index normalized by the Optimized Soil-Adjusted Vegetation Index) and CI (Chlorophyll index) hyperspectral indices when looking at full-crown rather than sunlit-vegetation pixels. RMSE measurements for full-crown models based on TCARI/OSAVI and CI across the three seasons ranged between 3.60 and 8.71 µg/cm<sup>2</sup> and between 3.70 and 7.86 µg/cm<sup>2</sup>, respectively. Using the scaling-up methodology, the TCARI-OSAVI-derived models were more stable across different methods of pixel extraction than the CI-derived models were, showing the smallest variations across measurement dates. Predictions of C<sub>a+b</sub> in the field trial showed that PRO4SAIL2, which combines the PROSPECT-D model with the 4SAIL2 model and accounts for clumping and a more complex tree structure, was more accurate than PRO4SAIL, which couples PROSPECT-D with the original 4SAIL model, across both crown segmentation methods. Using PRO4SAIL2, predictions were more accurate for the full-crown spectra (R² = 0.82; RMSE = 3.35 µg/cm²) than for the sunlit-vegetation pixels (R² = 0
{"title":"Chlorophyll content estimation in radiata pine using hyperspectral imagery: A comparison between empirical models, scaling-up algorithms, and radiative transfer inversions","authors":"Tomas Poblete , Michael S. Watt , Henning Buddenbaum , Pablo J. Zarco-Tejada","doi":"10.1016/j.agrformet.2025.110402","DOIUrl":"10.1016/j.agrformet.2025.110402","url":null,"abstract":"<div><div>Radiata pine (<em>Pinus radiata</em> D. Don) is a widely planted tree species. Fertilizers, especially those containing leaf nitrogen (N) and phosphorous (P), are essential for maximizing growth. Nutrient deficiencies and excessive fertilization can limit growth, so monitoring is crucial. Leaf pigments such as chlorophyll <em>a</em>+<em>b</em> (C<sub>a+b</sub>) can be used to assess plant nutrition, specifically leaf N. Remote sensing approaches can be used to monitor forest condition by estimating C<sub>a+b</sub> content as a proxy for leaf N. Conventional methods for C<sub>a+b</sub> estimation are based on empirical relationships using sensitive spectral indices or inversions of Radiative Transfer Models (RTMs). However, the structural complexity of tree crowns composed of multiple layers of clumped leaves/needles and background and shadow effects challenge the use of the indices proposed for both leaf C<sub>a+b</sub> and leaf nitrogen assessment. This study compares the accuracy of methods for C<sub>a+b</sub> estimation in radiata pine using hyperspectral data collected from a greenhouse experiment over the growing season and from a field trial representing a stand with a complex structure. The methods used to predict needle C<sub>a+b</sub> from tree-crown spectra included: 1) empirical relationships between C<sub>a+b</sub> measurements and hyperspectral indices; 2) scaling-up of hyperspectral index-based C<sub>a+b</sub> predictive relationships through RTM simulations; and 3) RTM inversions of C<sub>a+b</sub> content. These methods were tested over two different segmentation strategies, including sunlit-vegetation and full-crown spectra, to assess the effects of the increased structural complexity.</div><div>Predictions of C<sub>a+b</sub> from the greenhouse experiment were generally higher for empirical models that used TCARI/OSAVI (Transformed Chlorophyll Absorption in Reflectance Index normalized by the Optimized Soil-Adjusted Vegetation Index) and CI (Chlorophyll index) hyperspectral indices when looking at full-crown rather than sunlit-vegetation pixels. RMSE measurements for full-crown models based on TCARI/OSAVI and CI across the three seasons ranged between 3.60 and 8.71 µg/cm<sup>2</sup> and between 3.70 and 7.86 µg/cm<sup>2</sup>, respectively. Using the scaling-up methodology, the TCARI-OSAVI-derived models were more stable across different methods of pixel extraction than the CI-derived models were, showing the smallest variations across measurement dates. Predictions of C<sub>a+b</sub> in the field trial showed that PRO4SAIL2, which combines the PROSPECT-D model with the 4SAIL2 model and accounts for clumping and a more complex tree structure, was more accurate than PRO4SAIL, which couples PROSPECT-D with the original 4SAIL model, across both crown segmentation methods. Using PRO4SAIL2, predictions were more accurate for the full-crown spectra (R² = 0.82; RMSE = 3.35 µg/cm²) than for the sunlit-vegetation pixels (R² = 0","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110402"},"PeriodicalIF":5.6,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-17DOI: 10.1016/j.agrformet.2025.110397
Wenqiang Zhang , Geping Luo , Rafiq Hamdi , Xiumei Ma , Piet Termonia , Philippe De Maeyer , Anping Chen
The scarcity and uneven global distribution of eddy covariance (EC) towers are the key factors that contribute to significant uncertainties in carbon cycle studies of terrestrial ecosystems. To address this limitation of EC towers, Zhang et al. (2023b) developed a meteorological station-based net ecosystem exchange (NEE) dataset. This dataset includes 4674 global meteorological stations, representing a 22-fold increase compared to the 212 existing EC towers and covering a broader range of ecosystem types. Here, we propose a systematic framework for the comprehensive assessment of spatio-temporal representativeness and global uncertainty of the meteorological station-based carbon flux dataset. Meteorological stations effectively enhance the spatial representativeness of the EC towers and reduce the latitudinal variability of the spatial representativeness. In most regions, the temporal trends of carbon flux data from meteorological stations did not significantly differ from those observed by EC towers (p < 0.001). The global uncertainty of carbon fluxes from meteorological station is 0.37, followed by the VISIT and FLUXCOM products with uncertainties of 0.44 and 0.45, respectively. Overall, the carbon fluxes from meteorological stations exhibit higher spatial representativeness and better temporal representativeness compared to the EC tower observations and possess lower global uncertainties than the existing carbon flux gridded products. Consequently, the carbon flux data derived from meteorological stations is a trade-off dataset that addresses the low spatial representativeness of the EC towers and the high uncertainty of the gridded products. It effectively complements the existing EC tower data while ensuring accuracy. The development of this dataset will play an important role in reducing the uncertainty of global carbon sink-related studies.
{"title":"Bridging the gap in carbon cycle studies: Meteorological station-based carbon flux dataset as a complement to EC towers","authors":"Wenqiang Zhang , Geping Luo , Rafiq Hamdi , Xiumei Ma , Piet Termonia , Philippe De Maeyer , Anping Chen","doi":"10.1016/j.agrformet.2025.110397","DOIUrl":"10.1016/j.agrformet.2025.110397","url":null,"abstract":"<div><div>The scarcity and uneven global distribution of eddy covariance (EC) towers are the key factors that contribute to significant uncertainties in carbon cycle studies of terrestrial ecosystems. To address this limitation of EC towers, Zhang et al. (2023b) developed a meteorological station-based net ecosystem exchange (NEE) dataset. This dataset includes 4674 global meteorological stations, representing a 22-fold increase compared to the 212 existing EC towers and covering a broader range of ecosystem types. Here, we propose a systematic framework for the comprehensive assessment of spatio-temporal representativeness and global uncertainty of the meteorological station-based carbon flux dataset. Meteorological stations effectively enhance the spatial representativeness of the EC towers and reduce the latitudinal variability of the spatial representativeness. In most regions, the temporal trends of carbon flux data from meteorological stations did not significantly differ from those observed by EC towers (p < 0.001). The global uncertainty of carbon fluxes from meteorological station is 0.37, followed by the VISIT and FLUXCOM products with uncertainties of 0.44 and 0.45, respectively. Overall, the carbon fluxes from meteorological stations exhibit higher spatial representativeness and better temporal representativeness compared to the EC tower observations and possess lower global uncertainties than the existing carbon flux gridded products. Consequently, the carbon flux data derived from meteorological stations is a trade-off dataset that addresses the low spatial representativeness of the EC towers and the high uncertainty of the gridded products. It effectively complements the existing EC tower data while ensuring accuracy. The development of this dataset will play an important role in reducing the uncertainty of global carbon sink-related studies.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110397"},"PeriodicalIF":5.6,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142989111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-16DOI: 10.1016/j.agrformet.2025.110393
Paulina A. Asante , Eric Rahn , Niels P.R. Anten , Pieter A. Zuidema , Alejandro Morales , Danaё M.A. Rozendaal
Climate change is expected to negatively impact cocoa production in West and Central Africa, where over 70 % of cocoa is grown. However, effects of temperature, precipitation and atmospheric carbon dioxide concentration [CO2] on cocoa tree physiology and productivity are poorly understood. Consequently, climate-change implications have not been adequately considered. The objective was to improve understanding of potential cocoa productivity responses to climate change by mid-century (2060).
Using a crop model, we simulated potential water-limited cocoa yields (Yw) to evaluate effects of warming and precipitation changes based on five plausible general circulation models (GCMs) climate-change scenarios, with and without elevated CO2. We examined how variation in Yw was associated with that of climate using mixed-effects models and estimated total cocoa production on current plantation area under current low-input and high-input scenarios.
With notable exceptions, by mid-century, Yw and suitable area were projected to increase, particularly when assuming full elevated [CO2] effects and under wetter climate-change scenarios. We identified a (south) east - west gradient with higher yield increases (∼39–60 %) in Cameroon and Nigeria compared to Ghana and Côte d'Ivoire (∼30–45 %). Larger yield reductions (∼12 %) were identified in Côte d'Ivoire and Ghana than in Nigeria (∼10 %) and Cameroon (∼2 %). Additionally, gains in suitable area were projected for Nigeria (∼17–20 Mha), Cameroon (∼11–12 Mha), and Ghana (∼2 Mha) while Côte d'Ivoire could lose ∼6–11 Mha (i.e., ∼27–50 % of current suitable area). Inter-annual yield variability was higher in areas with low yields. Based on the mid climate-change scenario, country-level production on current plantation area in Côte d'Ivoire and Ghana could be maintained. Projected increases and shorter length in dry season precipitation strongly determined increases in Yw and reductions in Yw variability, respectively. Thus, despite projected warming and precipitation changes, many current cocoa-growing areas may maintain or increase their productivity, particularly if full effects of elevated [CO2] are assumed.
{"title":"Climate change impacts on cocoa production in the major producing countries of West and Central Africa by mid-century","authors":"Paulina A. Asante , Eric Rahn , Niels P.R. Anten , Pieter A. Zuidema , Alejandro Morales , Danaё M.A. Rozendaal","doi":"10.1016/j.agrformet.2025.110393","DOIUrl":"10.1016/j.agrformet.2025.110393","url":null,"abstract":"<div><div>Climate change is expected to negatively impact cocoa production in West and Central Africa, where over 70 % of cocoa is grown. However, effects of temperature, precipitation and atmospheric carbon dioxide concentration [CO<sub>2</sub>] on cocoa tree physiology and productivity are poorly understood. Consequently, climate-change implications have not been adequately considered. The objective was to improve understanding of potential cocoa productivity responses to climate change by mid-century (2060).</div><div>Using a crop model, we simulated potential water-limited cocoa yields (Yw) to evaluate effects of warming and precipitation changes based on five plausible general circulation models (GCMs) climate-change scenarios, with and without elevated CO<sub>2</sub>. We examined how variation in Yw was associated with that of climate using mixed-effects models and estimated total cocoa production on current plantation area under current low-input and high-input scenarios.</div><div>With notable exceptions, by mid-century, Yw and suitable area were projected to increase, particularly when assuming full elevated [CO<sub>2</sub>] effects and under wetter climate-change scenarios. We identified a (south) east - west gradient with higher yield increases (∼39–60 %) in Cameroon and Nigeria compared to Ghana and Côte d'Ivoire (∼30–45 %). Larger yield reductions (∼12 %) were identified in Côte d'Ivoire and Ghana than in Nigeria (∼10 %) and Cameroon (∼2 %). Additionally, gains in suitable area were projected for Nigeria (∼17–20 Mha), Cameroon (∼11–12 Mha), and Ghana (∼2 Mha) while Côte d'Ivoire could lose ∼6–11 Mha (i.e., ∼27–50 % of current suitable area). Inter-annual yield variability was higher in areas with low yields. Based on the mid climate-change scenario, country-level production on current plantation area in Côte d'Ivoire and Ghana could be maintained. Projected increases and shorter length in dry season precipitation strongly determined increases in Yw and reductions in Yw variability, respectively. Thus, despite projected warming and precipitation changes, many current cocoa-growing areas may maintain or increase their productivity, particularly if full effects of elevated [CO<sub>2</sub>] are assumed.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110393"},"PeriodicalIF":5.6,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142986890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-16DOI: 10.1016/j.agrformet.2025.110396
Junliang Zou , Yun Zhang , Brian Tobin , Matthew Saunders , Erica Cacciotti , Giuseppi Benanti , Bruce Osborne
Climate change is expected to increase the frequency and intensity of water deficits and extreme rainfall events in temperate regions, with significant effects on greenhouse gas (GHG) emissions. In this study, we investigated the impact of water deficits and drying and rewetting events on GHG fluxes in two Irish sites with adjacent forest and grassland ecosystems. We deployed rain-out shelters to simulate drought and applied water to mimic the extreme precipitation events. The effects of warming on these events were also examined using soil cores collected from the field. Water deficits increased carbon dioxide (CO2) emissions at the evergreen coniferous forest site but decreased it at the broadleaf deciduous forest site, likely due to differences in the prevailing soil moisture contents and the availability of oxygen for microbial activity. Rewetting triggered pulses of CO2 (1.1 – 7.2 fold), methane (CH4) (> 20 fold), and nitrous oxide (N2O) (3.3 – 71.7 fold) emissions in both ecosystems. Warming amplified the effects of water additions, leading to a 1.9 – 3.4-fold increase in CO2 and N2O fluxes, compared to the pre-wetting levels and a 1.2 – 1.5-fold increase compared to the controls. Cumulative CO2 emissions over 24 hours showed a negative response to increasing soil moisture and a positive response to the changes in soil moisture (difference between the initial value before water addition and the final soil moisture after water addition). CH4 fluxes exhibited an opposite trend. Multiple linear regression revealed that at higher soil carbon concentrations CO2 emissions were reduced but CH4 emissions increased, for the same change in soil moisture. Given that future climate scenarios predict an increase in extreme rainfall events a better understanding of the influence of soil drying-rewetting events on GHG emissions is required that accounts for multiple influencing factors, including differences in regional and site characteristics.
{"title":"Contrasting effects of water deficits and rewetting on greenhouse gas emissions in two grassland and forest ecosystems","authors":"Junliang Zou , Yun Zhang , Brian Tobin , Matthew Saunders , Erica Cacciotti , Giuseppi Benanti , Bruce Osborne","doi":"10.1016/j.agrformet.2025.110396","DOIUrl":"10.1016/j.agrformet.2025.110396","url":null,"abstract":"<div><div>Climate change is expected to increase the frequency and intensity of water deficits and extreme rainfall events in temperate regions, with significant effects on greenhouse gas (GHG) emissions. In this study, we investigated the impact of water deficits and drying and rewetting events on GHG fluxes in two Irish sites with adjacent forest and grassland ecosystems. We deployed rain-out shelters to simulate drought and applied water to mimic the extreme precipitation events. The effects of warming on these events were also examined using soil cores collected from the field. Water deficits increased carbon dioxide (CO<sub>2</sub>) emissions at the evergreen coniferous forest site but decreased it at the broadleaf deciduous forest site, likely due to differences in the prevailing soil moisture contents and the availability of oxygen for microbial activity. Rewetting triggered pulses of CO<sub>2</sub> (1.1 – 7.2 fold), methane (CH<sub>4</sub>) (> 20 fold), and nitrous oxide (N<sub>2</sub>O) (3.3 – 71.7 fold) emissions in both ecosystems. Warming amplified the effects of water additions, leading to a 1.9 – 3.4-fold increase in CO<sub>2</sub> and N<sub>2</sub>O fluxes, compared to the pre-wetting levels and a 1.2 – 1.5-fold increase compared to the controls. Cumulative CO<sub>2</sub> emissions over 24 hours showed a negative response to increasing soil moisture and a positive response to the changes in soil moisture (difference between the initial value before water addition and the final soil moisture after water addition). CH<sub>4</sub> fluxes exhibited an opposite trend. Multiple linear regression revealed that at higher soil carbon concentrations CO<sub>2</sub> emissions were reduced but CH<sub>4</sub> emissions increased, for the same change in soil moisture. Given that future climate scenarios predict an increase in extreme rainfall events a better understanding of the influence of soil drying-rewetting events on GHG emissions is required that accounts for multiple influencing factors, including differences in regional and site characteristics.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110396"},"PeriodicalIF":5.6,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142986889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-15DOI: 10.1016/j.agrformet.2025.110389
Mengliang Ma , Qiang Li , Yaping Wang , Jin Liang , Jiangyao Wang , Jinliang Liu , Mingfang Zhang
Rainfall canopy interception plays a crucial role in rainfall redistribution and hydrological processes in forests. While previous studies have often focused on monthly or yearly time scales, the responses of forest canopy interception to different rainfall magnitudes, frequencies and intensities, particularly under changing climate conditions have been less explored. In addition, the performance of canopy interception models that capture the dynamics of rainfall interception under changing climate remains largely unknown. In this study, we conducted field observations across various tree species and used the Revised Gash model to evaluate the canopy interception under different rainfall intensities. Our findings revealed that the observed interception loss of gross precipitation were 26.1 %, 42.1 %, and 41.6 % for Pinus tabuliformis (PT), Quercus wutaishanica (QW), and Betula platyphylla (BP), respectively. The Revised Gash model accurately estimated canopy interception, with percentage errors of 0.4 %, 5.6 %, and 22.3 % for PT, QW, and BP, respectively. Interestingly, the model performed better for PT, especially under light to moderate rain, while its applicability for QW and BP were diminished under moderate to heavy rain. Overall, the Revised Gash model underestimated interception loss across different rainfall intensities, with more pronounced underestimations observed at higher rainfall intensities. Evaporation during and after rainfall contributed significantly to over 85.3 % of interception loss across three tree species. Sensitivity analysis highlighted that parameters including mean rainfall intensity, mean wet canopy evaporation rate, and canopy storage capacity were critical in influencing canopy interception simulation. These findings highlight the influence of rainfall intensity on the model's reliability in simulating interception loss and provide insights for forest hydrology research in semi-arid regions.
{"title":"Rainfall intensities determine accuracy of canopy interception simulation using the Revised Gash model","authors":"Mengliang Ma , Qiang Li , Yaping Wang , Jin Liang , Jiangyao Wang , Jinliang Liu , Mingfang Zhang","doi":"10.1016/j.agrformet.2025.110389","DOIUrl":"10.1016/j.agrformet.2025.110389","url":null,"abstract":"<div><div>Rainfall canopy interception plays a crucial role in rainfall redistribution and hydrological processes in forests. While previous studies have often focused on monthly or yearly time scales, the responses of forest canopy interception to different rainfall magnitudes, frequencies and intensities, particularly under changing climate conditions have been less explored. In addition, the performance of canopy interception models that capture the dynamics of rainfall interception under changing climate remains largely unknown. In this study, we conducted field observations across various tree species and used the Revised Gash model to evaluate the canopy interception under different rainfall intensities. Our findings revealed that the observed interception loss of gross precipitation were 26.1 %, 42.1 %, and 41.6 % for <em>Pinus tabuliformis</em> (<em>PT</em>), <em>Quercus wutaishanica</em> (<em>QW</em>), and <em>Betula platyphylla</em> (<em>BP</em>), respectively. The Revised Gash model accurately estimated canopy interception, with percentage errors of 0.4 %, 5.6 %, and 22.3 % for <em>PT, QW</em>, and <em>BP</em>, respectively. Interestingly, the model performed better for <em>PT</em>, especially under light to moderate rain, while its applicability for <em>QW</em> and <em>BP</em> were diminished under moderate to heavy rain. Overall, the Revised Gash model underestimated interception loss across different rainfall intensities, with more pronounced underestimations observed at higher rainfall intensities. Evaporation during and after rainfall contributed significantly to over 85.3 % of interception loss across three tree species. Sensitivity analysis highlighted that parameters including mean rainfall intensity, mean wet canopy evaporation rate, and canopy storage capacity were critical in influencing canopy interception simulation. These findings highlight the influence of rainfall intensity on the model's reliability in simulating interception loss and provide insights for forest hydrology research in semi-arid regions.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110389"},"PeriodicalIF":5.6,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142981536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-13DOI: 10.1016/j.agrformet.2024.110378
Jasper Schreiber , Václav Pouska , Petr Macek , Dominik Thom , Claus Bässler
Deadwood is a crucial component of forest ecosystems, supporting numerous forest-dwelling species and ecosystem functions, such as water and nutrient cycling. Temperature is a major driver of processes, affecting, inter alia, metabolic rates within deadwood. Deadwood temperature is determined by factors at both the forest stand-scale and individual deadwood object-scale. Yet, the contribution of individual factors within the complex hierarchy of scales that drive temperature in deadwood remains poorly understood. We conducted a real-world experiment to analyze the effects of forest stand canopy cover (open vs. closed canopies), surrounding deadwood amount (high vs. low), deadwood tree species (beech vs. fir), position (soil contact vs. uplifted) and diameter (range: 19-47 cm) of coarse woody debris on within-deadwood daily mean, minimum and maximum temperature at monthly and seasonal level. Stand-scale factors were more important than object-scale factors for explaining the variance in temperature. Canopy cover exhibited the strongest relationship with temperature. Daily mean and maximum temperature were higher and daily minimum temperature was lower in open than in closed canopies during the growing season (May-October). Further, daily minimum was lower in open canopies during winter (November-April). Annual daily mean and maximum temperature were about 1 °C and 5 °C warmer, respectively, and minimum temperature about 2 °C colder in open compared to closed canopies. Effects of deadwood amount, object diameter, position, and tree species on temperature were less important and statistically significant in only a few months. We conclude that canopy cover is more important than deadwood characteristics in determining internal deadwood temperature. An increase of canopy disturbance will hence elevate the temperature in deadwood, which might have important consequences on deadwood-dwelling species and ecological processes, such as heterotrophic respiration. To diversify habitat conditions for multiple species, we recommend enriching deadwood under various canopy conditions.
{"title":"Effects of canopy-mediated microclimate and object characteristics on deadwood temperature","authors":"Jasper Schreiber , Václav Pouska , Petr Macek , Dominik Thom , Claus Bässler","doi":"10.1016/j.agrformet.2024.110378","DOIUrl":"10.1016/j.agrformet.2024.110378","url":null,"abstract":"<div><div>Deadwood is a crucial component of forest ecosystems, supporting numerous forest-dwelling species and ecosystem functions, such as water and nutrient cycling. Temperature is a major driver of processes, affecting, <em>inter alia</em>, metabolic rates within deadwood. Deadwood temperature is determined by factors at both the forest stand-scale and individual deadwood object-scale. Yet, the contribution of individual factors within the complex hierarchy of scales that drive temperature in deadwood remains poorly understood. We conducted a real-world experiment to analyze the effects of forest stand canopy cover (open vs. closed canopies), surrounding deadwood amount (high vs. low), deadwood tree species (beech vs. fir), position (soil contact vs. uplifted) and diameter (range: 19-47 cm) of coarse woody debris on within-deadwood daily mean, minimum and maximum temperature at monthly and seasonal level. Stand-scale factors were more important than object-scale factors for explaining the variance in temperature. Canopy cover exhibited the strongest relationship with temperature. Daily mean and maximum temperature were higher and daily minimum temperature was lower in open than in closed canopies during the growing season (May-October). Further, daily minimum was lower in open canopies during winter (November-April). Annual daily mean and maximum temperature were about 1 °C and 5 °C warmer, respectively, and minimum temperature about 2 °C colder in open compared to closed canopies. Effects of deadwood amount, object diameter, position, and tree species on temperature were less important and statistically significant in only a few months. We conclude that canopy cover is more important than deadwood characteristics in determining internal deadwood temperature. An increase of canopy disturbance will hence elevate the temperature in deadwood, which might have important consequences on deadwood-dwelling species and ecological processes, such as heterotrophic respiration. To diversify habitat conditions for multiple species, we recommend enriching deadwood under various canopy conditions.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110378"},"PeriodicalIF":5.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-13DOI: 10.1016/j.agrformet.2025.110386
Huijun Qin , Yuanshang Guo , Chengyi Li , Chunming Xin , Rui Hu , Mingzhu He
Climate change has significantly altered precipitation patterns worldwide, resulting in more frequent and intense droughts and heavy rainstorms, particularly in vulnerable ecosystems such as arid deserts. This study investigated how dominant desert shrubs, the C3 plant Kalidium gracile and the C4 plant Salsola passerina, respond to varying precipitation regimes. A six-year controlled experiment (2016–2021) employing a five-level precipitation gradient, ranging from extreme drought to increased water availability, was conducted to elucidate changes in leaves carbon content and its components under these conditions. Results indicated a substantial increase in starch (ST) content in S. passerina under heightened rainfall conditions (P < 0.05), whereas K. gracile showed a propensity tendency to accumulate ST content under moderate drought condition. These findings indicated distinct adaptive strategies between the two species in response to water availability. Additionally, both shrubs maintained a relatively stable ratio of non-structural carbohydrates (NSC) to structural carbohydrates (SC) (P > 0.05), suggesting an active regulation of carbon balance within plant structures, independent of precipitation changes. Notably, S. passerina demonstrated greater responsiveness to precipitation alterations compared to K. gracile, highlighting species-specific differences in carbon allocation strategies. This study provides mechanistic insights into plant carbon dynamics in response to precipitation changes in desert ecosystems, contributing to a deeper understanding of carbon cycling processes and ecosystem functioning in arid landscapes.
{"title":"Carbohydrate allocation strategies in leaves of dominant desert shrubs in response to precipitation variability","authors":"Huijun Qin , Yuanshang Guo , Chengyi Li , Chunming Xin , Rui Hu , Mingzhu He","doi":"10.1016/j.agrformet.2025.110386","DOIUrl":"10.1016/j.agrformet.2025.110386","url":null,"abstract":"<div><div>Climate change has significantly altered precipitation patterns worldwide, resulting in more frequent and intense droughts and heavy rainstorms, particularly in vulnerable ecosystems such as arid deserts. This study investigated how dominant desert shrubs, the C<sub>3</sub> plant <em>Kalidium gracile</em> and the C<sub>4</sub> plant <em>Salsola passerina</em>, respond to varying precipitation regimes. A six-year controlled experiment (2016–2021) employing a five-level precipitation gradient, ranging from extreme drought to increased water availability, was conducted to elucidate changes in leaves carbon content and its components under these conditions. Results indicated a substantial increase in starch (ST) content in <em>S. passerina</em> under heightened rainfall conditions (<em>P</em> < 0.05), whereas <em>K. gracile</em> showed a propensity tendency to accumulate ST content under moderate drought condition. These findings indicated distinct adaptive strategies between the two species in response to water availability. Additionally, both shrubs maintained a relatively stable ratio of non-structural carbohydrates (NSC) to structural carbohydrates (SC) (<em>P</em> > 0.05), suggesting an active regulation of carbon balance within plant structures, independent of precipitation changes. Notably, <em>S. passerina</em> demonstrated greater responsiveness to precipitation alterations compared to <em>K. gracile</em>, highlighting species-specific differences in carbon allocation strategies. This study provides mechanistic insights into plant carbon dynamics in response to precipitation changes in desert ecosystems, contributing to a deeper understanding of carbon cycling processes and ecosystem functioning in arid landscapes.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110386"},"PeriodicalIF":5.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142968310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-13DOI: 10.1016/j.agrformet.2025.110392
Jon Detka , Mohammad Jafari , Marcella Gomez , Gregory S. Gilbert
This study presents the development and application of models to estimate leaf wetness duration and their integration with drone-based imagery to analyze plant disease patterns across a coastal gradient. By comparing machine learning algorithms with empirical models, we identified that both approaches effectively predict leaf wetness, particularly in a temperate maritime ecosystem. The models were applied to study two manzanita species (Arctostaphylos tomentosa and A. pumila), revealing a strong correlation between leaf wetness and disease prevalence. This work highlights the role of microclimate conditions in shaping plant health and disease distribution in coastal shrublands. We compared nine popular machine learning algorithms and four empirical threshold models to characterize leaf wetness patterns in a spatially diverse temperate maritime wildland ecosystem. We suggest that simple empirical leaf wetness models based on dew point depression or relative humidity thresholds perform as well as machine learning techniques and should not be overlooked. The relationship between leaf wetness duration and the spatial distribution of plant disease along a coastal-to-inland climate gradient offers valuable insights into disease dynamics.
本研究介绍了估算叶片湿润度持续时间的模型的开发和应用，以及这些模型与无人机图像的整合，以分析沿海梯度的植物病害模式。通过比较机器学习算法和经验模型，我们发现这两种方法都能有效预测叶片湿润度，尤其是在温带海洋生态系统中。这些模型被应用于研究两种芒草（Arctostaphylos tomentosa 和 A. pumila），发现叶片湿度与疾病流行之间存在很强的相关性。这项工作凸显了小气候条件在塑造沿海灌木林植物健康和病害分布中的作用。我们比较了九种流行的机器学习算法和四种经验阈值模型，以描述一个空间多样的温带海洋野生生态系统的叶片湿度模式。我们认为，基于露点降低或相对湿度阈值的简单经验叶片湿度模型与机器学习技术的表现一样好，不应被忽视。沿着沿海到内陆的气候梯度，叶片湿度持续时间与植物病害空间分布之间的关系为了解病害动态提供了宝贵的信息。
{"title":"Machine learning vs. empirical models: Estimating leaf wetness patterns in a wildland landscape for plant disease management","authors":"Jon Detka , Mohammad Jafari , Marcella Gomez , Gregory S. Gilbert","doi":"10.1016/j.agrformet.2025.110392","DOIUrl":"10.1016/j.agrformet.2025.110392","url":null,"abstract":"<div><div>This study presents the development and application of models to estimate leaf wetness duration and their integration with drone-based imagery to analyze plant disease patterns across a coastal gradient. By comparing machine learning algorithms with empirical models, we identified that both approaches effectively predict leaf wetness, particularly in a temperate maritime ecosystem. The models were applied to study two manzanita species (<em>Arctostaphylos tomentosa</em> and <em>A. pumila</em>), revealing a strong correlation between leaf wetness and disease prevalence. This work highlights the role of microclimate conditions in shaping plant health and disease distribution in coastal shrublands. We compared nine popular machine learning algorithms and four empirical threshold models to characterize leaf wetness patterns in a spatially diverse temperate maritime wildland ecosystem. We suggest that simple empirical leaf wetness models based on dew point depression or relative humidity thresholds perform as well as machine learning techniques and should not be overlooked. The relationship between leaf wetness duration and the spatial distribution of plant disease along a coastal-to-inland climate gradient offers valuable insights into disease dynamics.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110392"},"PeriodicalIF":5.6,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142974719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-12DOI: 10.1016/j.agrformet.2025.110391
Ching-Hung Shih , Ray G. Anderson , Todd. H. Skaggs , Jehn-Yih Juang , Yi-Ying Chen , Yi-Shin Jang , Rong-Yu Gu , Cho-Ying Huang , Min-Hui Lo
Partitioning evapotranspiration components is crucial for an in-depth understanding of energy, water, and carbon cycles in agricultural and forest ecosystems. In this study, the Flux Variance Similarity (FVS) method, lauded for its capability to segregate eddy covariance datasets' evapotranspiration, was applied in Taiwan's Chi-Lan montane cloud forest and the Lien-Hua-Chih forest. However, we discovered a biased early peak of transpiration using the FVS method in the Chi-Lan montane cloud forest that did not align with the diurnal cycle of transpiration obtained from the Community Land Model, observed sap flow velocity, and net radiation. This bias is attributed to the rapid increase in specific humidity, caused by additional water vapor sources from valley wind. This factor violates the FVS method's assumptions and leads to an early peak in CO2 fluxes describing the net primary production (NPP). Furthermore, the high relative humidity conditions from afternoon to evening contribute to a larger magnitude of leaf-level water use efficiency, primarily due to minimal gradients between intercellular and ambient water vapor concentrations. The early peak of net primary production and water use efficiency skew the diurnal course of estimated transpiration. Additionally, the substantial canopy evaporation in the morning and the uncertainty in water use efficiency during periods of high relative humidity contribute to the overall uncertainty in transpiration values. Consequently, the application of the FVS method in environments akin to the Chi-Lan montane cloud forest warrants caution due to the intrinsic uncertainty. Our research emphasizes the imperative to explore different evapotranspiration partitioning techniques, especially in topographies like mountainous regions where diurnal water vapor accumulation is swift and places that are consistently subjected to high relative humidity.
{"title":"Challenges and limitations of applying the flux variance similarity (FVS) method to partition evapotranspiration in a montane cloud forest","authors":"Ching-Hung Shih , Ray G. Anderson , Todd. H. Skaggs , Jehn-Yih Juang , Yi-Ying Chen , Yi-Shin Jang , Rong-Yu Gu , Cho-Ying Huang , Min-Hui Lo","doi":"10.1016/j.agrformet.2025.110391","DOIUrl":"10.1016/j.agrformet.2025.110391","url":null,"abstract":"<div><div>Partitioning evapotranspiration components is crucial for an in-depth understanding of energy, water, and carbon cycles in agricultural and forest ecosystems. In this study, the Flux Variance Similarity (FVS) method, lauded for its capability to segregate eddy covariance datasets' evapotranspiration, was applied in Taiwan's Chi-Lan montane cloud forest and the Lien-Hua-Chih forest. However, we discovered a biased early peak of transpiration using the FVS method in the Chi-Lan montane cloud forest that did not align with the diurnal cycle of transpiration obtained from the Community Land Model, observed sap flow velocity, and net radiation. This bias is attributed to the rapid increase in specific humidity, caused by additional water vapor sources from valley wind. This factor violates the FVS method's assumptions and leads to an early peak in CO<sub>2</sub> fluxes describing the net primary production (NPP). Furthermore, the high relative humidity conditions from afternoon to evening contribute to a larger magnitude of leaf-level water use efficiency, primarily due to minimal gradients between intercellular and ambient water vapor concentrations. The early peak of net primary production and water use efficiency skew the diurnal course of estimated transpiration. Additionally, the substantial canopy evaporation in the morning and the uncertainty in water use efficiency during periods of high relative humidity contribute to the overall uncertainty in transpiration values. Consequently, the application of the FVS method in environments akin to the Chi-Lan montane cloud forest warrants caution due to the intrinsic uncertainty. Our research emphasizes the imperative to explore different evapotranspiration partitioning techniques, especially in topographies like mountainous regions where diurnal water vapor accumulation is swift and places that are consistently subjected to high relative humidity.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110391"},"PeriodicalIF":5.6,"publicationDate":"2025-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-11DOI: 10.1016/j.agrformet.2025.110387
Wenqiang Gao , Jianfeng Liu , Wenquan Bao , Fujun Duan , Xiao He , Dongli Gao , Xiangdong Lei
Impending climate change is anticipated to exacerbate the frequency and severity of extreme droughts, significantly affecting tree growth and distribution ranges. A critical endeavor in predicting how tree species will respond to more frequent and intense severe droughts is assessing the drought sensitivity and resilience of tree growth across a species' different range. However, the variation in tree growth resistance and resilience to extreme droughts across different distribution range edges have received little attention. In this study, we analyzed tree ring width data from 596 trees across 19 sites, encompassing the northernmost and southernmost distribution limits of Juniperus rigida in China. Our objectives were to delineate patterns of growth resistance, recovery and resilience to extreme droughts between northern and southern populations, and to assess their driving factors. Our findings revealed that the drought events significantly reduced the tree growth. Specifically, the tree growth has exhibited a decreasing trend in the northern distribution range limit, but an increasing trend at southern range limit since 1996, due to the more frequent and severe droughts in the northern region than in the southern. Furthermore, although the tree growth resistance and resilience were significantly higher in the northern limits than those in the southern, more frequent droughts will reduce their resistance and resilience. In addition, the growth resistance and resilience were also affected by factors such as tree age, pre-drought growth (e.g. mean growth rate and variability), and the interaction between drought characteristics and pre-drought growth. We conclude that J. rigida trees exhibit greater resistance and resilience to drought at their northern range limits compared to their southern counterparts. However, the increasing frequency and severity of droughts in the northern expose these trees to more persistent drought conditions, which could ultimately result in a decline in resilience and growth.
{"title":"Extreme droughts decrease the growth and resilience of Juniperus rigida in the northern edge but not in the southern","authors":"Wenqiang Gao , Jianfeng Liu , Wenquan Bao , Fujun Duan , Xiao He , Dongli Gao , Xiangdong Lei","doi":"10.1016/j.agrformet.2025.110387","DOIUrl":"10.1016/j.agrformet.2025.110387","url":null,"abstract":"<div><div>Impending climate change is anticipated to exacerbate the frequency and severity of extreme droughts, significantly affecting tree growth and distribution ranges. A critical endeavor in predicting how tree species will respond to more frequent and intense severe droughts is assessing the drought sensitivity and resilience of tree growth across a species' different range. However, the variation in tree growth resistance and resilience to extreme droughts across different distribution range edges have received little attention. In this study, we analyzed tree ring width data from 596 trees across 19 sites, encompassing the northernmost and southernmost distribution limits of <em>Juniperus rigida</em> in China. Our objectives were to delineate patterns of growth resistance, recovery and resilience to extreme droughts between northern and southern populations, and to assess their driving factors. Our findings revealed that the drought events significantly reduced the tree growth. Specifically, the tree growth has exhibited a decreasing trend in the northern distribution range limit, but an increasing trend at southern range limit since 1996, due to the more frequent and severe droughts in the northern region than in the southern. Furthermore, although the tree growth resistance and resilience were significantly higher in the northern limits than those in the southern, more frequent droughts will reduce their resistance and resilience. In addition, the growth resistance and resilience were also affected by factors such as tree age, pre-drought growth (e.g. mean growth rate and variability), and the interaction between drought characteristics and pre-drought growth. We conclude that <em>J. rigida</em> trees exhibit greater resistance and resilience to drought at their northern range limits compared to their southern counterparts. However, the increasing frequency and severity of droughts in the northern expose these trees to more persistent drought conditions, which could ultimately result in a decline in resilience and growth.</div></div>","PeriodicalId":50839,"journal":{"name":"Agricultural and Forest Meteorology","volume":"362 ","pages":"Article 110387"},"PeriodicalIF":5.6,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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