Search Results (122)

Understanding ecosystem carbon processes relies heavily on the reliable assessment of gross primary productivity (GPP) yet remains challenging in the Inner Mongolia grasslands due to data scarcity and high uncertainty among existing products. We developed a ConvTransformer-based framework that exploits complementary information from satellite observations and meteorological datasets to enhance the representation of complex spatiotemporal dependencies in grassland ecosystems. Grounded in leave-one-site-out cross-validation across six eddy covariance sites, the model achieved average performance metrics of R² = 0.59, RMSE = 1.40 g C m⁻² d⁻¹, Bias = −0.31 g C m⁻² d⁻¹, and KGE = 0.46, outperforming traditional machine learning models (RF, GBRT, and SVR) as well as the light use efficiency model (EC-LUE) in both accuracy and robustness. Using this framework, we generated a daily GPP dataset at spatial granularity of 1 km for the Inner Mongolia grasslands from 2003 to 2018. The results reveal a clear spatial gradient, with GPP decreasing from southeast to northwest. Comparisons with established products, including FLUXCOM, BESS V2, and PML V2, show strong spatial consistency and reduced discrepancies, supporting the reliability of the estimates. Overall, the proposed framework provides an effective approach for characterizing regional carbon dynamics and supports long-term ecological monitoring in semi-arid regions. Full article

In controlled-environment agriculture (CEA), light serves both as an energy source for photosynthesis and as a regulatory factor. However, the light responses of underutilized leafy greens are still not fully characterized compared with model crops such as lettuce. This study evaluated the effects of lighting parameters on the growth, metabolism, antioxidant properties, and mineral composition of Chrysanthemum coronarium (shungiku) greens cultivated hydroponically in CEA. Three parallel experiments were conducted, aiming to explore the effects of (I) light spectrum using red (R, 660 nm), blue (B, 447 nm), and combined RB light; (II) photoperiod, using 12, 16, and 24 h photoperiods at equal daily light integral; and 150, 200, 250, and 300 µmol m⁻² s⁻¹ photosynthetic photon flux density (PPFD) at 16 h photoperiod. RB light promoted the highest biomass accumulation and light use efficiency (LUE), while monochromatic red and blue light limited growth and reduced Fe and Zn contents. A 12 h photoperiod yielded the best results for leaf area, fresh weight, and LUE compared with 16 and 24 h photoperiods. Higher PPFD increased biomass, soluble sugars, antioxidant capacity, organic acids, and micronutrients, with peak LUE at 200 µmol m⁻² s⁻¹ instead of the maximum yield at 300 µmol m⁻² s⁻¹. These findings emphasize the importance of crop-specific and trait-oriented light optimization for underutilized leafy vegetables. Full article

Solar-induced chlorophyll fluorescence (SIF) is an emerging predictor in the crop gross primary production (GPP) estimation for its close relationships with vegetation photosynthesis. Conventional crop GPP are estimated by data-driven models upscaled from eddy covariance flux observations, light-use efficiency (LUE) models, and process-based models, which are constrained by the limited availability of in-site experimental and simulated data. By using vegetation remote sensing data and meteorological data to simulate the combined impacts of changes in vegetation physiological factors and environmental factors on GPP estimation, we proposed a new method to estimate GPP for winter wheat over the North China Plain (NCP) based on the SIF-based mechanistic light response (MLR) model with bias correction. Results showed that (1) vegetation and meteorological factors could be used to fit the bias caused by the static input parameters of the MLR model for winter wheat GPP estimation, which solved the unavailability of the input parameters in the MLR models; (2) the MLR model with bias correction could quickly achieve large-scale crop GPP estimation at the regional scale during the vigorous period of winter wheat, whose performance was superior to that of a traditional statistical regression model with an increased R² of 6.4%. Full article

Gross primary production (GPP) is a key indicator to evaluating ecosystem carbon sinks. Southwest China is characterised by diverse ecosystems and abundant forest resources and represents one of the most important carbon reservoirs in China. Therefore, a quantitative assessment of the uncertainty of existing GPP products and their influencing factors is important. This study investigates GPP uncertainties and its influencing factors based on the three-cornered hat (TCH) and XGBoost and SHAP methods. Thirteen products were examined, including six products from the light use efficiency (LUE) model, two products from the process-based (Process) model, three products from the machine learning (ML) model and two products from satellite-based direct proxies (Proxies). The results reveal the following: (1) All products show similar spatial patterns, with Process products fluctuating notably in 2010, 2011, and 2014, while others remain stable. (2) Relative uncertainty is lowest annually, increasing monthly and daily; ML products exhibit greater stability. Among them, CEDAR has the least uncertainty and strongest agreement with flux observations (r = 0.82), whereas EC-LUE shows the highest uncertainty. (3) Vegetation index, elevation and radiation are more influential than other factors. These findings aid GPP product selection and uncertainty assessment in complex terrains with sparse ground data. Full article

The light environment is a key factor in regulating crop growth and quality in plant factories, where both light intensity and photoperiod strongly influence photosynthetic productivity and energy consumption. This study aimed to elucidate the interactive effects of light intensity and photoperiod on the growth, photosynthetic performance, and energy-use efficiency of Pakchoi in a controlled environment, thereby optimizing lighting strategies. Here, three levels of light intensity (PPFD: 100, 175, and 250 μmol·m⁻²·s⁻¹) and four photoperiods (8, 12, 16, and 20 h·d⁻¹) were combined, resulting in twelve treatments. Plant growth parameters, chlorophyll content, gas exchange indices, CO₂ response curves, and chlorophyll fluorescence characteristics were measured, along with analyses of light-use efficiency (LUE) and electrical energy-use efficiency (EUE). The highest biomass accumulation was observed under a 20 h·d⁻¹–250 μmol·m⁻²·s⁻¹ treatment. In contrast, the optimal LUE (9.69%) and EUE (4.98%) were observed under a 20 h·d⁻¹–175 μmol·m⁻²·s⁻¹ treatment. The best photosynthetic performance (A_max 32.61 μmol·m⁻²·s⁻¹) occurred under a 16 h·d⁻¹–250 μmol·m⁻²·s⁻¹ treatment. This study integrates growth, photosynthetic physiology, and energy-use efficiency, revealing a trade-off between biomass production and energy utilization in Pakchoi cultivation. It clarifies that “moderate light intensity + long photoperiod” is the optimal strategy to balance yield and energy consumption in plant factories. Full article

Light use efficiency (LUE) models are widely used to estimate gross primary productivity (GPP) because they provide strong accuracy while maintaining low complexity. The aim of this study is (a) to evaluate the performance of a LUE model (sCASE) and (b) to compare the performance of several alternative water scalars. The analyses are done using GPP measurements from thirteen eddy covariance sites across Europe, corresponding to different forest types. Daily GPP estimates produced by sCASE were highly accurate for most sites (average R² = 0.750 and average RMSE = 2.317 g C m⁻² d⁻¹), matching the performance of other widely used LUE models in the literature. All three scalars were essential for maintaining model accuracy, although their relative importance varied among sites. The developmental scalar, which is not incorporated in most productivity models, was particularly important for accurately estimating GPP in deciduous species. Among the ten water scalars tested, those based on simple water balance calculations performed best in water-limited sites, whereas the VPD-based scalar performed better in sites without water limitation. The EF (evaporative fraction) scalar showed high accuracy at some sites across both water status categories but very low accuracy at others. For large-scale applications, water scalars based on MODIS indices offer the advantage of global coverage, which can outweigh their lower accuracy relative to other scalars. Full article

Mangrove forests provide essential climate regulation and coastal protection, yet fine-scale quantification of carbon dynamics remains limited in the Sundarbans due to spatial heterogeneity and tidal influences. This study estimated canopy structural and photosynthetic dynamics from 2019 to 2023 by integrating 10 m spatial high-resolution remote sensing with a light use efficiency (LUE) modeling framework. Leaf Area Index (LAI) was retrieved at 10 m resolution using the PROSAIL radiative transfer model applied to Sentinel-2 data to characterize the canopy structure of the mangrove forest. LUE-based Gross Primary Productivity (GPP) was estimated using Sentinel-2 vegetation and water indices and MODIS fPAR with station observatory temperature data. Annual carbon uptake showed clear interannual variation, ranging from 1881 to 2862 g C m⁻² yr⁻¹ between 2019 and 2023. GPP estimates were strongly correlated with MODIS-GPP (R² = 0.86, p < 0.001), demonstrating the method’s reliability for monitoring mangrove carbon sequestration. LUE-based Solar-induced Chlorophyll Fluorescence (SIF) was derived at 10 m resolution and compared with TROPOMI-SIF observations to assess correspondence (R² = 0.88, p < 0.001) with photosynthetic activity. LAI, GPP and SIF exhibited pronounced seasonal and interannual variability on photosynthetic activity, with higher values during the monsoon growing season and lower values during dry periods. Mean NDVI declined from 2019 to 2023 and modeled annual carbon uptake ranged from approximately 43 to 65 Mt CO₂ eq, with lower sequestration in 2022–2023 associated with climatic stress. Strong correlations among LAI, NDVI, GPP, and SIF indicated consistent coupling between photosynthetic activity and carbon uptake in the mangrove ecosystem. These results provide a fine-scale assessment of mangrove carbon dynamics relevant to conservation and climate-mitigation planning in tropical regions. Full article

With advancements in solar-induced fluorescence (SIF) observation technology and the evolution of vegetation radiative transfer models, SIF signals can now be more effectively interpreted and leveraged from a mechanistic perspective. This, in turn, facilitates a deeper understanding of the mechanistic link between SIF and photosynthesis. Considering the impact of water stress on terrestrial ecosystems, this paper simulated SIF and gross primary productivity (GPP) values using the STEMMUS-SCOPE model at half-hour scales from 2017 to 2023 at the Daman site. The simulation results were compared and validated against flux tower observations and SCOPE model outputs. Taking advantage of irrigation events in the semi-arid irrigated farmland, we assessed the accuracy of STEMMUS-SCOPE in simulating SIF and GPP under drought stress, as well as its capability to quantitatively analyze the impacts of water stress on SIF and GPP. The results show that the accuracy of the SIF and GPP values simulated by the STEMMUS-SCOPE model is higher than that of the SCOPE model. The averaged R² and RMSE between the SIF simulated by STEMMUS-SCOPE model and the observed SIF values are 0.66 and 0.29 mW m⁻² nm⁻¹, and the averaged R² and RMSE between the GPP simulated by the STEMMUS-SCOPE model and the observed GPP values from 2017 to 2023 are 0.88 and 4.93 µmol CO₂ m⁻² s⁻¹, respectively. Especially under relatively drought conditions, the R² between the SIF simulated values and observed values is 0.84, and the R² between the GPP simulated values and observed values is 0.96. By further combining soil moisture content (SMC) and canopy conductance (Gs) analyses, we found that the response of the STEMMUS-SCOPE simulations under water stress was consistent with previous findings on the impacts of water deficits, thereby confirming the model’s reliability for drought conditions. Under drought stress, the decline in fluorescence emission efficiency (ΦF) with decreasing Gs and SMC was smaller than that of the light use efficiency (LUE). Therefore, the STEMMUS-SCOPE model is promising for investigating the SIF–GPP relationship under drought stress. Full article

Facing the significant challenges posed by global population growth and urbanization, plant factories, as an efficient closed cultivation system capable of precise environmental control, have become a key direction in the development of modern agriculture. However, high energy consumption, particularly lighting (which accounts for over 50%), remains a major bottleneck limiting their large-scale application. This study systematically explored the effects of dynamic light regulation strategies on lettuce (Lactuca sativa L.) growth, physiological and biochemical indicators (such as chlorophyll, photosynthetic, and fluorescence parameters), nutritional quality, energy utilization efficiency, and post-harvest shelf life. Four different light treatments were designed: a stepwise increasing photosynthetic photon flux density (PPFD) from 160 to 340 μmol·m⁻²·s⁻¹ (T1), a constant light intensity of 250 μmol·m⁻²·s⁻¹ (T2), a three-stage strategy with high light intensity in the middle phase (T3), and a three-stage strategy with sequentially increasing light (T4). The results showed that the T4 treatment exhibited the best overall performance. Compared with the T2 treatment, the T4 treatment increased biomass by 23.4%, significantly improved the net photosynthetic rate by 50.32% at the final measurement, and increased ascorbic acid (AsA) and protein content by 33.36% and 33.19%, respectively. Additionally, this treatment showed the highest energy use efficiency. On the 30th day of treatment, the light energy use efficiency (LUE) and electrical energy use efficiency (EUE) of the T4 treatment were significantly increased, by 23.41% and 23.9%, respectively, compared with the T2 treatment. In summary, dynamic light regulation can synergistically improve crop yield, chlorophyll content, photosynthetic efficiency, nutritional quality, and energy utilization efficiency, providing a theoretical basis and solution for precise light regulation and energy consumption reduction in plant factories. Full article

Meta-cresol (m-cresol) is highly corrosive and toxic, and is widely present in industrial wastewater. As a pollutant, it adversely affects various aspects of human production and daily life. To evaluate the feasibility of using sunflowers to remediate m-cresol-contaminated wastewater, this study used Helianthus annuus L. as the test subject to analyze its tolerance and the wastewater purification efficiency under different m-cresol concentrations. The results showed that the net photosynthetic rate (P_n), transpiration rate (T_r), stomatal conductance (G_s), and light energy utilization efficiency (LUE) of Helianthus annuus L. exhibited an overall decreasing trend, while the intercellular CO₂ concentration (Cᵢ) initially increased and subsequently decreased with increasing m-cresol concentration. When m-cresol concentration reached or exceeded 60 mg·L⁻¹, the net photosynthetic rate and intercellular CO₂ concentration in the leaves showed opposite trends with further increases in m-cresol stress. The inhibition of net photosynthesis in sunflowers by m-cresol was mainly attributed to non-stomatal factors. The maximum photochemical efficiency (F_v/F_m), actual photochemical efficiency (ΦPSII), photochemical quenching coefficient (q^P), PSII excitation energy partition coefficient (α), and the fraction of absorbed light energy used for photochemistry (P) all decreased with increasing m-cresol concentration. In contrast, non-photochemical quenching (NPQ), the quantum yield of regulated energy dissipation [Y(NPQ)], and the fraction of energy dissipated as heat through the antenna (D) first increased and then decreased. Under low-concentration m-cresol stress, sunflowers protected their photosynthetic system by dissipating excess light energy as heat as a stress response. However, high concentrations of m-cresol caused irreversible damage to Photosystem II (PSII) in sunflowers. Under m-cresol stress, chlorophyll a exhibited strong stability with minimal degradation. As the m-cresol concentration increased from 30 to 180 mg·L⁻¹, the removal rate decreased from 84.91% to 11.84%. In conclusion, sunflowers show good remediation potential for wastewater contaminated with low concentrations of m-cresol and can be used for treating m-cresol wastewater with concentrations ≤ 51.9 mg·L⁻¹. Full article

Gross primary productivity (GPP) is a key indicator for assessing carbon uptake capacity and photosynthetic productivity in agricultural ecosystems, playing a crucial role in regional carbon cycle evaluation and sustainable agriculture development. However, traditional mechanistic light use efficiency (LUE) models exhibit variable accuracy under different climatic conditions and crop types. Machine learning models, while demonstrating strong fitting capabilities, heavily depend on the selection of input features and data availability. This study focuses on winter wheat in the Guanzhong region, utilizing continuous field observation data from the 2020–2022 growing seasons to develop five machine learning models: Ridge Regression (Ridge), Random Forest (RF), Support Vector Regression (SVR), Gradient Boosting Regression (GB), and a stacking-based ensemble learning model (LSM). These models were compared with the LUE model under two scenarios, excluding and including solar-induced chlorophyll fluorescence (SIF), to evaluate the contribution of SIF to GPP estimation accuracy. The results indicate significant differences in GPP estimation performance among the machine learning models, with LSM outperforming others in both scenarios. Without SIF, LSM achieved an average R² of 0.87, surpassing individual models (0.72–0.83), demonstrating strong stability and generalization ability. With SIF inclusion, all machine learning models showed marked accuracy improvements, with LSM’s average R² rising to 0.91, highlighting SIF’s critical role in capturing photosynthetic dynamics. Although the LUE model approached machine learning model accuracy in some growth stages, its overall performance was limited by structural constraints. This study demonstrates that ensemble learning methods integrating multi-source observations offer significant advantages for high-precision winter wheat GPP estimation, and that incorporating SIF as a physiological indicator further enhances model robustness and predictive capacity. The findings validate the potential of combining ensemble learning and photosynthetic physiological parameters to improve GPP retrieval accuracy, providing a reliable technical pathway for agricultural ecosystem carbon flux estimation and informing strategies for climate change adaptation. Full article

Phosphorus (P) and silicon (Si) could profoundly affect the net primary productivity (ANPP) of grassland ecosystems. However, how ecosystem biomass will respond to different Si addition, especially under a concurrent increase in P fertilization, remains limited. With persistent demand for grassland utilization, there is a need to enhance and sustain the productivity of grasslands on the Qinghai–Tibet Plateau. Three P addition rates (0, 400, 800, and 1200 kg Ca(H₂PO₄)₂ ha⁻¹ yr⁻¹) without Si and with Si (14.36 kg H₄SiO₄ ha⁻¹ yr⁻¹) were applied to alpine grassland on the Qinghai–Tibet Plateau to evaluate the responses of aboveground biomass and the underlying mechanisms linking to structural carbon composition and physiological traits of grasses and forbs. Our results show that the application of Si significantly reduced the lignin, cellulose, hemicellulose, and total phenol contents of both grasses and forbs. Additionally, the addition of P, Si, and phosphorus and silicon (PSi) co-application significantly increased the net photosynthetic rate (Pn) and light use efficiency (LUE) of grasses and forbs. Moreover, Si promoted the absorption of N and P by plants, resulting in significant changes in the Si:C, Si:P, and Si:N ratios and increasing the aboveground biomass. Our findings suggest that Si can replace structural carbohydrates and regulate the absorption and utilization of N and P to optimize the photosynthetic process of leaves, thereby achieving greater biomass. In summary, Si supplementation improves ecosystem stability in alpine meadows by optimizing plant functions and increasing biomass accumulation. Full article

Indoor farming presents a sustainable response to urbanization and climate change, yet optimizing light use efficiency (LUE) remains vital for maximizing crop yield and minimizing energy use. This study introduces an IoT-based framework for adaptive light management in controlled environments, using lettuce (Lactuca sativa L.) as a model crop due to its rapid growth and sensitivity to light spectra. The system integrates advanced LED lighting, real-time sensors, and cloud-based analytics to enhance light distribution and automate adjustments based on growth stages. The key findings indicate a 20% increase in energy efficiency and a 15% improvement in lettuce growth compared to traditional static models. Novel metrics—Light Use Efficiency at Growth stage Canopy Level (LUEP) and Lamp Level (LUEL)—were developed to assess system performance comprehensively. Simulations identified optimal growth conditions, including a light intensity of 350–400 µmol/m²/s and photoperiods of 16–17 h/day. Spectral optimization showed that a balanced blue-red light mix benefits vegetative growth, while higher red content supports flowering. The framework’s feedback control ensures rapid (<2 s) and accurate (>97%) adjustments to environmental deviations, maintaining ideal conditions throughout growth stages. Comparative analysis confirms the adaptive system’s superiority over static models in responding to dynamic environmental conditions and improving performance metrics like LUEP and LUEL. Practical recommendations include stage-specific guidelines for light spectrum, intensity, and duration to enhance both energy efficiency and crop productivity. While tailored to lettuce, the modular system design allows for adaptation to a variety of leafy greens and other crops with species-specific calibration. This research demonstrates the potential of IoT-driven adaptive lighting systems to advance precision agriculture in indoor environments, offering scalable, energy-efficient solutions for sustainable food production. Full article

Agroecosystems play a key role in the global carbon cycle, with CO₂ exchange driven by photosynthesis and respiration. Indicators such as gross primary productivity (GPP), net primary productivity (NPP), and carbon, water, and light use efficiency (CUE, WUE, LUE) are essential for assessing resource use in agricultural systems. Conventional tillage depletes carbon, water, and nutrients, negatively impacting the environment, while conservation practices aim to improve soil health and biodiversity. This study evaluated the effects of a cover crop in a wheat–maize rotation on sloped arable land prone to water erosion. The experiment involved minimum contour tillage combined with cover cropping, and its impact on carbon balance components and resource use efficiency was assessed. The results demonstrated that the inclusion of a cover crop significantly improved GPP and NPP. Water and light use efficiency also increased, particularly in 2022 and 2023, which were characterized by summer drought. However, carbon use efficiency remained unchanged over the study period. These findings highlight the potential of conservation practices, such as cover cropping and reduced tillage, to enhance productivity and resource efficiency in sloped agricultural landscapes under water stress conditions. Full article

Vertical farming is gaining popularity as a sustainable solution to global food demand, particularly in urban areas where space is limited. However, optimizing key factors such as planting density remains a critical issue, as it directly affects light interception, energy efficiency, and crop yield. Lettuce and basil, the most commonly grown crops in vertical farms, were chosen for this study, with the aim of addressing the impact of planting density on light interception and overall productivity for improving the performance and sustainability of vertical farming systems. Plants were grown in an ebb-and-flow system of a fully controlled experimental vertical farm, where light was provided by light-emitting diode fixtures delivering a photoperiod of 16 h d⁻¹ and 200 µmol m⁻² s⁻¹ of photosynthetic photon flux density. Experimental treatments included three planting densities, namely 123 (low density, LD), 237 (medium density, MD), and 680 (high density, HD) plant m⁻². At the final harvest (29 days after sowing), the adoption of the highest planting density (680 plant m⁻²) resulted in greater fresh yield (kg FW m⁻²), leaf area index (LAI, m² m⁻²), light use efficiency (LUE, g DW mol⁻¹) and light energy use efficiency (L-EUE, g FW kWh⁻¹) for both lettuce (+207%, +227%, +142%, +206%, respectively), and basil (+312%, +316%, +291, +309%, respectively), as compared to the lowest density (123 plant m⁻²). However, the fresh and dry weights of the individual plants were lowered, probably as a result of the reduced light availability due to the highly dense plants’ canopy. Overall, these findings underscore the potential of increasing planting density in vertical farms to enhance yield and resource efficiency. Full article