Deep migration learning-based recognition of diseases and insect pests in Yunnan tea under complex environments.

BACKGROUND: The occurrence, development, and outbreak of tea diseases and pests pose a significant challenge to the quality and yield of tea, necessitating prompt identification and control measures. Given the vast array of tea diseases and pests, coupled with the intricacies of the tea planting environment, accurate and rapid diagnosis remains elusive. In addressing this issue, the present study investigates the utilization of transfer learning convolution neural networks for the identification of tea diseases and pests. Our objective is to facilitate the accurate and expeditious detection of diseases and pests affecting the Yunnan Big leaf kind of tea within its complex ecological niche. RESULTS: Initially, we gathered 1878 image data encompassing 10 prevalent types of tea diseases and pests from complex environments within tea plantations, compiling a comprehensive dataset. Additionally, we employed data augmentation techniques to enrich the sample diversity. Leveraging the ImageNet pre-trained model, we conducted a comprehensive evaluation and identified the Xception architecture as the most effective model. Notably, the integration of an attention mechanism within the Xeption model did not yield improvements in recognition performance. Subsequently, through transfer learning and the freezing core strategy, we achieved a test accuracy rate of 98.58% and a verification accuracy rate of 98.2310%. CONCLUSIONS: These outcomes signify a significant stride towards accurate and timely detection, holding promise for enhancing the sustainability and productivity of Yunnan tea. Our findings provide a theoretical foundation and technical guidance for the development of online detection technologies for tea diseases and pests in Yunnan.

Cover crop inclusion and residue retention improves soybean production and physiology in drought conditions.

Soybean (Glycine max (L.) Merr.) planting has increased in central and western North Dakota despite frequent drought occurrences that limit productivity. Soybean plants need high photosynthetic and transpiration rates to be productive, but they also need high water use efficiency when water is limited. Crop residues and cover crops in crop rotations may improve soybean drought tolerance in northern Great Plains. We aimed to examine how a management practice that included cover crops and residue retention impacts agronomic, ecosystem water and carbon dioxide flux, and canopy-scale physiological attributes of soybeans in the northern Great Plains under drought conditions. The experiment consisted of two soybean fields over two years with business-as-usual (no-cover crops and spring wheat residue removal) and aspirational management (cover crops and spring wheat residue retention) during a drought year. We compared yield; aboveground biomass; green chromatic coordinates, and CO2 and H2O fluxes from eddy covariance, Phenocam images, and ancillary micrometeorological measurements. These measurements were used to derive ecosystem-scale physical, and physiological attributes with the 'big leaf' framework to diagnose underlying processes. Soybean yields were 29 % higher under drought conditions in the field managed in a system that included cover crops and residue retention. This yield increase was associated with a 5 day increase in the green-chromatic-coordinate defined maturity phenophase, increasing agronomic and intrinsic water use efficiency by 27 % and 33 %, respectively, increasing water uptake, and increasing the rubisco-limited photosynthetic capacity (Vcmax25) by 42 %. The inclusion of cover crops and residue retention into a cropping system improved soybean productivity because of differences in water use, phenology timing, and photosynthetic capacity. These results suggest that farmers can improve soybean productivity and yield stability by incorporating cover crops and residue retention into their management suite because these practices to facilitate more aggressive water uptake.

El Niño-Southern Oscillation forcing on carbon and water cycling in a Bornean tropical rainforest.

Carbon dioxide and water vapor exchanges between tropical forest canopies and the atmosphere through photosynthesis, respiration, and evapotranspiration (ET) influence carbon and water cycling at the regional and global scales. Their inter- and intra-annual variations are sensitive to seasonal rhythms and longer-timescale tropical climatic events. In the present study, we assessed the El Niño-Southern Oscillation (ENSO) influence on ET and on the net ecosystem exchange (NEE), using eddy-covariance flux observations in a Bornean rainforest over a 10-y period (2010-2019) that included several El Niño and La Niña events. From flux model inversions, we inferred ecophysiological properties, notably the canopy stomatal conductance and "big-leaf" maximum carboxylation rate (Vcmax25_BL). Mean ET values were similar between ENSO phases (El Niño, La Niña, and neutral conditions). Conversely, the mean net ecosystem productivity was highest during La Niña events and lowest during El Niño events. Combining Shapley additive explanation calculations for nine controlling factors with a machine-learning algorithm, we determined that the primary factors for ET and NEE in the La Niña and neutral phases were incoming shortwave solar radiation and Vcmax25_BL, respectively, but that canopy stomatal conductance was the most significant factor for both ET and NEE in the El Niño phase. A combined stomatal-photosynthesis model approach further indicated that Vcmax25_BL differences between ENSO phases were the most significant controlling factor for canopy photosynthesis, emphasizing the strong need to account for ENSO-induced ecophysiological factor variations in model projections of the long-term carbon balance in Southeast Asian tropical rainforests.

CO2-assimilation, sequestration, and storage by urban woody species growing in parks and along streets in two climatic zones.

Using two cities, Rimini (Italy, Cfa climate) and Krakow (Poland, Cfb), as living laboratories, this research aimed at measuring in situ the capacity of 15 woody species to assimilate, sequester, and store CO2. About 1712 trees of the selected species were identified in parks or along streets of the two cities, and their age, DBH, height, and crown radius were measured. The volume of trunk and branches was measured using a terrestrial LiDAR. The true Leaf Area Index was calculated by correcting transmittance measurements conducted using a plant-canopy-analyser for leaf angle distribution, woody area index, and clumping. Dendrometric traits were fitted using age or DBH as independent variable to obtain site- and species-specific allometric equations. Instantaneous and daily net CO2-assimilation per unit leaf area was measured using an infra-red gas-analyser on full-sun and shaded leaves and upscaled to the unit crown-projection area and to the whole tree using both a big-leaf and a multilayer approach. Results showed that species differed for net CO2-assimilation per unit leaf area, leaf area index, and for the contribution of shaded leaves to overall canopy carbon gain, which yielded significant differences among species in net CO2-assimilation per unit crown-projection-area (AcpaML(d)). AcpaML(d) was underestimated by 6-30 % when calculated using the big-leaf, compared to the multilayer model. While maximizing AcpaML(d) can maximize CO2-assimilation for a given canopy cover, species which matched high AcpaML(d) and massive canopy spread, such as mature Platanus x acerifolia and Quercus robur, provided higher CO2-assimilation (Atree) at the individual tree scale. Land use (park or street), did not consistently affect CO2-assimilation per unit leaf or crown-projection area, although Atree can decline in response to specific management practices (e.g. heavy pruning). CO2-storage and sequestration, in general, showed a similar pattern as Atree, although the ratio between CO2-sequestration and CO2-assimilation decreased at increasing DBH.

Predicting ecosystem productivity based on plant community traits.

With the rapid accumulation of plant trait data, major opportunities have arisen for the integration of these data into predicting ecosystem primary productivity across a range of spatial extents. Traditionally, traits have been used to explain physiological productivity at cell, organ, or plant scales, but scaling up to the ecosystem scale has remained challenging. Here, we show the need to combine measures of community-level traits and environmental factors to predict ecosystem productivity at landscape or biogeographic scales. We show how theory can extend the production ecology equation to enormous potential for integrating traits into ecological models that estimate productivity-related ecosystem functions across ecological scales and to anticipate the response of terrestrial ecosystems to global change.

The phenology of leaf quality and its within-canopy variation is essential for accurate modeling of photosynthesis in tropical evergreen forests.

Leaf quantity (i.e., canopy leaf area index, LAI), quality (i.e., per-area photosynthetic capacity), and longevity all influence the photosynthetic seasonality of tropical evergreen forests. However, these components of tropical leaf phenology are poorly represented in most terrestrial biosphere models (TBMs). Here, we explored alternative options for the representation of leaf phenology effects in TBMs that employ the Farquahar, von Caemmerer & Berry (FvCB) representation of CO2 assimilation. We developed a two-fraction leaf (sun and shade), two-layer canopy (upper and lower) photosynthesis model to evaluate different modeling approaches and assessed three components of phenological variations (i.e., leaf quantity, quality, and within-canopy variation in leaf longevity). Our model was driven by the prescribed seasonality of leaf quantity and quality derived from ground-based measurements within an Amazonian evergreen forest. Modeled photosynthetic seasonality was not sensitive to leaf quantity, but was highly sensitive to leaf quality and its vertical distribution within the canopy, with markedly more sensitivity to upper canopy leaf quality. This is because light absorption in tropical canopies is near maximal for the entire year, implying that seasonal changes in LAI have little impact on total canopy light absorption; and because leaf quality has a greater effect on photosynthesis of sunlit leaves than light limited, shade leaves and sunlit foliage are more abundant in the upper canopy. Our two-fraction leaf, two-layer canopy model, which accounted for all three phenological components, was able to simulate photosynthetic seasonality, explaining ~90% of the average seasonal variation in eddy covariance-derived CO2 assimilation. This work identifies a parsimonious approach for representing tropical evergreen forest photosynthetic seasonality in TBMs that utilize the FvCB model of CO2 assimilation and highlights the importance of incorporating more realistic phenological mechanisms in models that seek to improve the projection of future carbon dynamics in tropical evergreen forests.

Protective effects of Big-leaf mulberry and physiological roles of nitric oxide synthases in the testis of mice following water immersion and restraint stress.

Big-leaf mulberry is a new hybrid plant from the application of cell engineering technology, but its effect in stress-induced testicular dysfunction is unknown. Nitric oxide (NO) is a tiny, highly reactive lipophilic molecule produced by nitric oxide synthases (NOS). Three isoforms of NOS (neuronal NOS, inducible NOS and endothelial NOS) have been identified. Our aim was to investigate the effect of water immersion and restraint stress (WIRS) on NOS in the testis, and the effect of Big-leaf mulberry to protect against WIRS. The activity and expression of NOS, and total antioxidant capacity (T-AOC) in the mouse testis of different treatment groups (non-WIRS, 3 h-WIRS, WIRS-recovery) were examined. Histological analysis of WIRS-induced testicular damage and immunohistochemical staining of NOS were also analyzed. Results demonstrated that WIRS-exposed mice produced several injuries and showed an increased iNOS and eNOS mRNA expression in testes, whereas pretreatment with Big-leaf mulberry down-regulated iNOS and eNOS mRNA expressions and up-regulated T-AOC activities. Immunohistochemical studies showed that both iNOS and eNOS were localized in germ cells, spermatozoa and blood vessels in addition to Leydig cells and Sertoli cells, but nNOS was not present in these areas. In conclusion, our results suggested that Big-leaf mulberry exerted a protective effect on WIRS-induced testicular dysfunction, and iNOS and eNOS appeared to exert an important action in mouse testes exposed to WIRS.