Precise crop yield prediction provides valuable information for agricultural planning and decision-making processes. However, timely predicting crop yields remains challenging as crop growth is sensitive to growing season weather variation and climate change. In this work, we develop a deep learning-based solution, namely Multi-Modal Spatial-Temporal Vision Transformer (MMST-ViT), for predicting crop yields at the county level across the United States, by considering the effects of short-term meteorological variations during the growing season and the long-term climate change on crops. Specifically, our MMST-ViT consists of a Multi-Modal Transformer, a Spatial Transformer, and a Temporal Transformer. The Multi-Modal Transformer leverages both visual remote sensing data and short-term meteorological data for modeling the effect of growing season weather variations on crop growth. The Spatial Transformer learns the high-resolution spatial dependency among counties for accurate agricultural tracking. The Temporal Transformer captures the long-range temporal dependency for learning the impact of long-term climate change on crops. Meanwhile, we also devise a novel multi-modal contrastive learning technique to pre-train our model without extensive human supervision. Hence, our MMST-ViT captures the impacts of both short-term weather variations and long-term climate change on crops by leveraging both satellite images and meteorological data. We have conducted extensive experiments on over 200 counties in the United States, with the experimental results exhibiting that our MMST-ViT outperforms its counterparts under three performance metrics of interest. Our dataset and code are available at https://github.com/fudong03/MMST-ViT.

In Bangladesh, the poultry industry is an economically and socially important sector, but it is persistently threatened by the effects of H5N1 highly pathogenic avian influenza. Thus, identifying the optimal control policy in response to an emerging disease outbreak is a key challenge for policy-makers. To inform this aim, a common approach is to carry out simulation studies comparing plausible strategies, while accounting for known capacity restrictions. In this study we perform simulations of a previously developed H5N1 influenza transmission model framework, fitted to two separate historical outbreaks, to assess specific control objectives related to the burden or duration of H5N1 outbreaks among poultry farms in the Dhaka division of Bangladesh. In particular, we explore the optimal implementation of ring culling, ring vaccination and active surveillance measures when presuming disease transmission predominately occurs from premises-to-premises, versus a setting requiring the inclusion of external factors. Additionally, we determine the sensitivity of the management actions under consideration to differing levels of capacity constraints and outbreaks with disparate transmission dynamics. While we find that reactive culling and vaccination policies should pay close attention to these factors to ensure intervention targeting is optimised, across multiple settings the top performing control action amongst those under consideration were targeted proactive surveillance schemes. Our findings may advise the type of control measure, plus its intensity, that could potentially be applied in the event of a developing outbreak of H5N1 amongst originally H5N1 virus-free commercially-reared poultry in the Dhaka division of Bangladesh.

Previous work suggests domestic poultry are important contributors to the emergence and transmission of highly pathogenic avian influenza throughout Asia. In Poyang Lake, China, domestic duck production cycles are synchronized with arrival and departure of thousands of migratory wild birds in the area. During these periods, high densities of juvenile domestic ducks are in close proximity to migratory wild ducks, increasing the potential for the virus to be transmitted and subsequently disseminated via migration. In this paper, we use GPS dataloggers and dynamic Brownian bridge models to describe movements and habitat use of free-grazing domestic ducks in the Poyang Lake basin and identify specific areas that may have the highest risk of H5N1 transmission between domestic and wild birds. Specifically, we determine relative use by free-grazing domestic ducks of natural wetlands, which are the most heavily used areas by migratory wild ducks, and of rice paddies, which provide habitat for resident wild ducks and lower densities of migratory wild ducks. To our knowledge, this is the first movement study on domestic ducks, and our data show potential for free-grazing domestic ducks from farms located near natural wetlands to come in contact with wild waterfowl, thereby increasing the risk for disease transmission. This study provides an example of the importance of movement ecology studies in understanding dynamics such as disease transmission on a complicated landscape.

Advanced satellite tracking technologies enable biologists to track animal movements at fine spatial and temporal scales. The resultant data present opportunities and challenges for understanding animal behavioral mechanisms. In this paper, we develop a new method to elucidate animal movement patterns from tracking data. Here, we propose the notion of continuous behavior patterns as a concise representation of popular migration routes and underlying sequential behaviors during migration. Each stage in the pattern is characterized in terms of space (i.e., the places traversed during movements) and time (i.e. the time spent in those places); that is, the behavioral state corresponding to a stage is inferred according to the spatiotemporal and sequential context. Hence, the pattern may be interpreted predictably. We develop a candidate generation and refinement framework to derive all continuous behavior patterns from raw trajectories. In the framework, we first define the representative spots to denote the underlying potential behavioral states that are extracted from individual trajectories according to the similarity of relaxed continuous locations in certain distinct time intervals. We determine the common behaviors of multiple individuals according to the spatiotemporal proximity of representative spots and apply a projection-based extension approach to generate candidate sequential behavior sequences as candidate patterns. Finally, the candidate generation procedure is combined with a refinement procedure to derive continuous behavior patterns. We apply an ordered processing strategy to accelerate candidate refinement. The proposed patterns and discovery framework are evaluated through conceptual experiments on both real GPS-tracking and large synthetic datasets.

The MODIS Land Science Team has produced a standard product of gross primary production (GPP) for the global land biosphere, using a satellite-based Production Efficiency Model. In this special issue for the validation studies, two papers have examined the accuracy, error, and uncertainty of the GPP product. Here, we present a user's perspective on accuracy assessment of the data products and discuss two large sources of uncertainty in the context of model variables and parameters: 1) light absorption by chlorophyll versus light absorption by canopy and 2) maximum light use efficiency.