Former Arizona gubernatorial candidate Kari Lake weighs in as Democratic Gov. Katie Hobbs vetoes legislation limiting Chinese land ownership near U.S. military bases and strategic assets and warns how the move puts national security at risk. Texas has become the latest state to cement a ban on land and property purchases by individuals or entities from adversarial nations. Republican Gov. Greg Abbott signed Senate Bill 17 into law over the weekend, prohibiting countries identified as security threats in the intelligence community's 2025 Annual Threat Assessment, from acquiring "real property" in the state. The countries include China, Russia, Iran and North Korea, and the bill identified "real property" as agricultural land, commercial or industrial properties, residential properties and land used for mining or water use. Amid heightened global tensions, there has been an increased appetite for protecting foreign asset acquisitions in the United States.

Sen. Pete Ricketts, R-Neb., spoke with Fox News Digital about his bipartisan bill to codify oversight of foreign adversaries, including China, buying American farmland. EXCLUSIVE: Republican Sen. Pete Ricketts is leading the charge with Democrat Sen. John Fetterman to codify oversight on foreign countries buying American farmland. The bipartisan Agricultural Foreign Investment Disclosure (AFIDA) Improvements Act seeks to implement recommendations published by the Government Accountability Office (GAO) in January 2024, which found the AFIDA was ill-equipped to combat foreign ownership of American agricultural land. "Communist China is our greatest geopolitical threat," Ricketts told Fox News Digital in an exclusive interview, adding, "This is a way for us to improve the disclosure that's going on with regard to the purchase of this agricultural land, so we can take other action if necessary to make sure we're not giving Communist China the opportunity to buy agricultural land." The bill's proposal comes as two Chinese nationals – a University of Michigan post-doctoral research fellow, Yunqing Jian, and Huazhong University of Science and Technology student Chengxuan Han – were held in federal custody after they were accused of smuggling biological materials into the United States.

Water quality is foundational to environmental sustainability, ecosystem resilience, and public health. Deep learning models, particularly Long Short-Term Memory (LSTM) networks, offer transformative potential for large-scale water quality prediction and scientific insights generation. However, their widespread adoption in high-stakes decision-making, such as pollution mitigation and equitable resource allocation, is prevented by unresolved trustworthiness challenges including fairness, uncertainty, interpretability, robustness, generalizability, and reproducibility. In this work, we present the first comprehensive evaluation of trustworthiness in a continental-scale multi-task LSTM model predicting 20 water quality variables (encompassing physical/chemical processes, geochemical weathering, and nutrient cycling) across 482 U.S. basins. Our investigation uncovers systematic patterns of model performance disparities linked to basin characteristics, the inherent complexity of biogeochemical processes, and variable predictability, emphasizing critical performance fairness concerns. We further propose methodological frameworks for quantitatively evaluating critical aspects of trustworthiness, including uncertainty, interpretability, and robustness, identifying key limitations that could challenge reliable real-world deployment. This work serves as a timely call to action for advancing trustworthy data-driven methods for water resources management and provides a pathway to offering critical insights for researchers, decision-makers, and practitioners seeking to leverage artificial intelligence (AI) responsibly in environmental management.

In the agricultural sector, the COVID-19 threatens to lead to a severe food security crisis in the region, with disruptions in the food supply chain and agricultural production expected to contract between 2.6% and 7%. From the food crop production side, the travel bans and border closures, the late reception and the use of agricultural inputs such as imported seeds, fertilizers, and pesticides could lead to poor food crop production performances. Another layer of disruption introduced by the mobility restriction measures is the scarcity of agricultural workers, mainly seasonal workers. The lockdown measures and border closures limit seasonal workers' availability to get to the farm on time for planting and harvesting activities. Moreover, most of the imported agricultural inputs travel by air, which the pandemic has heavily impacted. Such transportation disruptions can also negatively affect the food crop production system. This chapter assesses food crop production levels in 2020 -- before the harvesting period -- in all African regions and four staples such as maize, cassava, rice, and wheat. The production levels are predicted using the combination of biogeophysical remote sensing data retrieved from satellite images and machine learning artificial neural networks (ANNs) technique. The remote sensing products are used as input variables and the ANNs as the predictive modeling framework. The input remote sensing products are the Normalized Difference Vegetation Index (NDVI), the daytime Land Surface Temperature (LST), rainfall data, and agricultural lands' Evapotranspiration (ET). The output maps and data are made publicly available on a web-based platform, AAgWa (Africa Agriculture Watch, www.aagwa.org), to facilitate access to such information to policymakers, deciders, and other stakeholders.

As I noted recently, organizations often find the biggest success through small steps with artificial intelligence. There are many examples of this at work, but Linux offers a great one. Linux started out as a student desktop experiment before it creeped slowly into companies as a reliable print server before eventually taking over the data center and the cloud (and Mars--it's on both the Chinese and U.S. rovers there). Incremental steps can add up to big things. In the area of food production, it needs to.

WE'VE long had eyes in the sky. But now a handful of start-ups are using these satellites to monitor everything from flood damage to crop yield with greater frequency and detail than ever before. Efforts to keep tabs on Earth from above began with NASA's Landsat programme, which started in 1973. It currently has two satellites in orbit imaging the whole of Earth's surface every 16 days. The resolution is high enough to capture major roads, but not individual houses.

The problem with this type of method is that it does not really take advantage of Land use and land cover classification (LUCC) maps from high resolution images. We believe that pixel based spectral remote sensor data are of great interest since they allow to information is not enough to characterize land use and track issues like deforestation/reforestation, water sources land cover classes. For this reason, our goal is to design a reduction, urban growth, or to calculate indicators like a methodology that models classes as areas of correlated pixels.