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As one of the biggest agroproducts producers, China plays an important role in the global supply. Yet, climate change inevitably threatens their production and leads to tremendous losses. Furthermore, climatic and nonclimatic factors are likely to influence their producing behaviors and yields. Accordingly, this work aims to explore both the qualitative and quantitative nexus between climate change, nonclimatic drivers, and agroproduct yield in China. We choose lychee (Litchi chinensis Sonn.), with world's largest production in China and one of the most demanding agroproduct for climatic conditions, as the subject. A two‐way fixed‐effect Poisson model with robust standard error is developed based on county‐level panel data in 39 main producing counties in China along with climatic and cultural festival factors from 2014 to 2019. The main conclusions are as follows: (a) the lychee culture festival, a nonclimatic driver, has negative effect on lychee yield, and this is a novel effect pathway theoretically and we verify it empirically; (b) climate changes in various phenological phases are significantly correlated to lychee yield; precipitation during the exposure phase or flowering phase has negative effect, and minimum temperature during the heading phase has positive effect, which is the first paper in this field; and (c) a new method is developed to analyze nonnegative yield and production, which could also be applied in other industries. Core Ideas The negative relationship of lychee culture festival and lychee yield is a novel effect pathway. The climate effect in five phenological phases on lychee yield is the first reported in this field. A new method is developed could also be used in the other industries. Less precipitation during exposure and flowering phase produces higher yield. Higher minimum temperature during the heading phase tends to increase lychee yield.

The increasingly revealed effects of climate change threaten tropical fruit production. Considerable production loss and unstable yield has been observed in China and other regions with similar climate condition. Accordingly, this paper aims to explore the impacts of environmental conditions, phenological periods, and production endowments on a tropical fruit yield, namely lychee, by using the extended Cobb–Douglas model. Lychee (Litchi chinensis Sonn.) is chosen as the focus of this study given it is one of the most climate‐sensitive tropical fruits which have important contributions to the health, economy, and culture in South China. The growth‐span was divided into four phenological periods, namely: exposure period, heading period, flowering period and maturing period. A two‐way fixed effect model is employed for county‐level mixed‐frequency panel data. This data comprises lychee yield, environmental conditions (mean temperature, minimum temperature, and precipitation), production endowments (labor input, capital input, and technical input), and phenological periods in 39 main producing counties in China from 2011 to 2019. The key conclusions of interest are (1) higher minimum temperature results in larger lychee yield, (2) higher minimum temperature in the earlier phenological period has larger positive impact on lychee yield, and (3) higher growth in technical input causes lower lychee yield, and an explanation is provided. Heterogeneity analysis further implies various effects of the minimum temperature influencing lychee yield. The minimum temperature has a significant positive effect on lychee yield in non‐coastal and non‐western counties, especially in earlier phenological periods. Core Ideas This study develops a mixed‐frequency panel data model to estimate the impact of environmental conditions on yield. The results elaborate the surprisingly negative nexus between lychee yield and technical input growth. Higher minimum temperature results in larger lychee yield and optimal minimum temperature may exist.

WRKY proteins comprise a large family of transcription factors that play important roles in plant growth, development, and responses to stress, but little information is available about these genes in lychee (Litchi chinensis Sonn.). Despite being a popular fruit worldwide, lychee is highly vulnerable to lychee downy blight (LDB; caused by Peronophythora litchii), a major disease affecting lychee. Based on lychee whole‐genome data, a total of 50 LcWRKY genes were identified across 15 chromosomes. According to a conserved domain and phylogenetic analysis, these 50 LcWRKYs were classified into three distinct subfamilies. Gene structure analysis revealed the presence of 2–11 exons in the LcWRKYs. Cis‐acting regulatory element analysis identified many stress‐response elements in LcWRKY promoters. Moreover, a comprehensive 2‐year investigation involving the screening of a lychee natural population led to the identification of Guiwei and Yurong1 as displaying stable susceptibility and resistance against LDB, respectively. Furthermore, expression patterns indicated that LcWRKY13, LcWRKY17, and LcWRKY34 were significantly upregulated in response to LDB induction, while LDB suppressed the expression of LcWRKY11. These findings suggest that these four genes may play a crucial role in lychee resistance to LDB. The present study has provided a foundation for future investigations of LcWRKY gene function in lychee. Core Ideas The WRKY family was fully characterized by bioinformatics in lychee. Lychee downy blight (LDB) caused by Peronophythora litchii is one of the major diseases of lychee. One natural population consisting of 276 lychee landraces was screened for LDB resistance. Four WRKY genes that may be critical in lychee resistance to LDB were selected for further genomic studies.

Açaí, lychee, mamey, passion fruit and jackfruit are some lesser-consumed tropical fruits due to their low commercial production. In 2018, approximately 6.8 million tons of these fruits were harvested, representing about 6.35% of the total world production of tropical fruits. The present work reviews the nutritional content, profile of bioactive compounds, antioxidant and anti-inflammatory capacity of these fruits and their by-products, and their ability to modulate oxidative stress due to the content of phenolic compounds, carotenoids and dietary fiber. Açaí pulp is an excellent source of anthocyanins (587 mg cyanidin-3-glucoside equivalents/100 g dry weight, dw), mamey pulp is rich in carotenoids (36.12 mg β-carotene/100 g fresh weight, fw), passion fruit peel is rich in dietary fiber (61.16 g/100 dw). At the same time, jackfruit contains unique compounds such as moracin C, artocarpesin, norartocarpetin and oxyresveratrol. These molecules play an important role in the regulation of inflammation via activation of mitogen-activated protein kinases (including p38, ERK and JNK) and nuclear factor κB pathways. The properties of the bioactive compounds found in these fruits make them a good source for use as food ingredients for nutritional purposes or alternative therapies. Research is needed to confirm their health benefits that can increase their marketability, which can benefit the primary producers, processing industries (particularly smaller ones) and the final consumer, while an integral use of their by-products will allow their incorporation into the circular bioeconomy.

The performance of fruit surface defect detection is easily affected by factors such as noisy background and foliage occlusion. In this study, we choose lychee as a fruit type to investigate its surface quality. Lychees are hard to preserve and have to be stored at low temperatures to keep fresh. Additionally, the surface of lychees is subject to scratches and cracks during harvesting/processing. To explore the feasibility of the automation of defective surface detection for lychees, we build a dataset with 3743 samples divided into three categories, namely, mature, defects, and rot. The original dataset suffers an imbalanced distribution issue. To address it, we adopt a transformer-based generative adversarial network (GAN) as a means of data augmentation that can effectively enhance the original training set with more and diverse samples to rebalance the three categories. In addition, we investigate three deep convolutional neural network (DCNN) models, including SSD-MobileNet V2, Faster RCNN-ResNet50, and Faster RCNN-Inception-ResNet V2, trained under different settings for an extensive comparison study. The results show that all three models demonstrate consistent performance gains in mean average precision (mAP), with the application of GAN-based augmentation. The rebalanced dataset also reduces the inter-category discrepancy, allowing a DCNN model to be trained equally across categories. In addition, the qualitative results show that models trained under the augmented setting can better identify the critical regions and the object boundary, leading to gains in mAP. Lastly, we conclude that the most cost-effective model, SSD-MobileNet V2, presents a comparable mAP (91.81%) and a superior inference speed (102 FPS), suitable for real-time detection in industrial-level applications.

Rapid and accurate detection of fruits is crucial for estimating yields and making scientific decisions in litchi orchards. However, litchis grow in complex natural environments, characterized by variable lighting, severe occlusion from branches and leaves, small fruit sizes, and dense overlapping, all of which pose significant challenges for accurate detection. This paper addressed this problem by proposing a method that combines unmanned aerial vehicle (UAV) remote sensing and deep learning for litchi detection. A remote sensing image dataset comprising litchi fruit was first constructed. Subsequently, an improved algorithm, YOLOv7-MSRSF, was developed. Experimental results demonstrated that YOLOv7-MSRSF’s mean average precision (mAP) reached 96.1%, outperforming YOLOv7 and pure transformer algorithms by 3.7% and 20.6%, respectively. Tests on randomly selected 24 images demonstrated that integrating the Swin-transformer module into YOLOv7 improved litchi fruit detection accuracy under severe occlusion, dense overlapping, and variable lighting by 19.55%, 6.63%, and 13.94%, respectively. YOLOv7-MSRSF showed further improvements in these three complex conditions, with detection accuracy increasing by 26.99%, 9.82%, and 18.68%, respectively, reaching 89.16%, 97.79%, and 95.56%. Furthermore, the Real-ESRGAN algorithm significantly enhanced the YOLOv7-MSRSF model’s recognition accuracy of objects in low-resolution images captured by high-altitude drones. The average detected accuracy of three images collected at 27.5 m above the canopy reached a high value of 82.2%, which was improved by 70.6% compared with that (11.6%) before super-resolution processing. The proposed method offered valuable guidance for detecting small, dense agricultural objects in large-scale, complex natural environments.

In this study, lychee (Litchi chinensis Sonn.) pericarp powder was added to chitosan (CHS) matrix to develop active packaging films, and their structure, physicochemical, antibacterial, antioxidant, and functional properties were investigated. FT-IR results showed that intermolecular hydrogen bonds were formed between CHS and polyphenols in lychee pericarp powder (LPP), and the intermolecular interaction interfered with the assembly of CHS into semi-crystal structure, which reduced the crystallinity of CHS film. Incorporation of LPP significantly reduced water vapor permeability, water solubility, swelling degree, and elongation at break of CHS film (p < 0.05). However, UV-visible light barrier, tensile strength, and antibacterial and antioxidant properties of CHS films were increased by LPP incorporation. CHS-LPP film remarkably lowered the weight loss, firmness, titratable acidity, and total soluble solids of fresh-cut apple after five days storage. CHS-LPP film packaging effectively inhibited the browning of fresh-cut apple and the reduction of polyphenol content in apple juice caused by polyphenol oxidase (PPO)-mediated oxidation during storage. Therefore, CHS-LPP films have great potential as food packaging material to ensure the quality and extend the shelf life of food products.

While agroforestry is promoted in many regions worldwide, limited attention is paid to farmer-led transitions toward agroforestry. Agroforestry systems (AFS) are ubiquitous and structure the landscapes along the east coast of Madagascar. These systems are managed by smallholders and produce a variety of products, some for export (mainly clove products, vanilla, and lychees), others for self-consumption or for sale locally (including fruit, tubers, timber and fuelwood). We compared information in historical documents with data collected in household surveys in 2016 in one village to identify the socio-technical and economic determinants that guided farmers' strategies over a century and led to the current production systems and landscapes. The agricultural policy implemented by the French colonial power, required farmers to produce industrial crops for export. Reluctantly at first, farmers gradually abandoned shifting rice cultivation (tavy) to grow cash crops, mainly coffee, paving the way for an economy based on trade and monetization. Later, farmers replaced coffee by clove trees, gradually transforming shifting cultivation into AFS with cloves. Simultaneously, they extended paddy rice cultivation and diversified their AFS in response to economic and climatic disturbances and to more gradual changes, thereby demonstrating their continual responsiveness. Similar dynamics can be observed in many contexts where the colonial powers forced farmers to produce cash crops to supply industries in the metropolitan area, obliging them to diversify their production and cropping systems. Our results provide valuable insights into the multiple drivers of these transitions toward agroforestry and into the complexity of the processes involved.

This paper provides an overview of the research carried out over the last 25 years on the FAO56 single and basal crop coefficients of subtropical and tropical orchards and plantations of cactus pear, dragon fruit, fig, jujube, passion fruit, pomegranate, cape gooseberry, cherimoya, guava, longan, lychee, mango, papaya, acerola, carambola, cashew, cacao, coffee, jaboticaba, jatropha, macadamia, açai palm, coconut, date palm, guayule, oil palm, peach palm, ramie and rubber tree. The main objective of this review is to update standard single crop coefficients (Kc) and basal crop coefficients (Kcb) and complete the Kc and Kcb values tabulated in FAO56. Kc is the ratio between the non-stressed crop evapotranspiration (ETc) and the grass reference evapotranspiration (ETo), and Kcb is the ratio between the crop transpiration (Tc) and the ETo. When selecting and analysing the literature, only studies that used the FAO Penman–Monteith equation, or another equation well related to the former to compute ETo were considered, while ETc or Tc were obtained from accurate field measurements on crops under pristine (non-stress cropping conditions) or eustress (“good stress”) conditions. Articles meeting these conditions were selected to provide data for updating Kc and Kcb under standard conditions. The related description of orchards and plantations refers to crop cultivar and rootstock, irrigation systems and scheduling, planting spacing, fraction of ground cover (fc) by the crops, crop height (h), crop age and training systems, as Kc and Kcb values depend on these characteristics. To define the standard Kc and Kcb values of the selected crops, the values collected in the literature were compared with previously tabulated standard Kc and Kcb values. The updated tabulated values are transferable to other locations and climates and can be used to calculate and model crop water requirements, primarily for irrigation planning and scheduling, and thereby supporting of improved water use and savings, which is the overall aim of the current review.

Detecting litchis in a complex natural environment is important for yield estimation and provides reliable support to litchi-picking robots. This paper proposes an improved litchi detection model named YOLOv5-litchi for litchi detection in complex natural environments. First, we add a convolutional block attention module to each C3 module in the backbone of the network to enhance the ability of the network to extract important feature information. Second, we add a small-object detection layer to enable the model to locate smaller targets and enhance the detection performance of small targets. Third, the Mosaic-9 data augmentation in the network increases the diversity of datasets. Then, we accelerate the regression convergence process of the prediction box by replacing the target detection regression loss function with CIoU. Finally, we add weighted-boxes fusion to bring the prediction boxes closer to the target and reduce the missed detection. An experiment is carried out to verify the effectiveness of the improvement. The results of the study show that the mAP and recall of the YOLOv5-litchi model were improved by 12.9% and 15%, respectively, in comparison with those of the unimproved YOLOv5 network. The inference speed of the YOLOv5-litchi model to detect each picture is 25 ms, which is much better than that of Faster-RCNN and YOLOv4. Compared with the unimproved YOLOv5 network, the mAP of the YOLOv5-litchi model increased by 17.4% in the large visual scenes. The performance of the YOLOv5-litchi model for litchi detection is the best in five models. Therefore, YOLOv5-litchi achieved a good balance between speed, model size, and accuracy, which can meet the needs of litchi detection in agriculture and provides technical support for the yield estimation and litchi-picking robots.