Brief history of agricultural systems modeling.

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the "next generation" models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

Towards a new generation of agricultural system data, models and knowledge products: Design and improvement.

This paper presents ideas for a new generation of agricultural system models that could meet the needs of a growing community of end-users exemplified by a set of Use Cases. We envision new data, models and knowledge products that could accelerate the innovation process that is needed to achieve the goal of achieving sustainable local, regional and global food security. We identify desirable features for models, and describe some of the potential advances that we envisage for model components and their integration. We propose an implementation strategy that would link a "pre-competitive" space for model development to a "competitive space" for knowledge product development and through private-public partnerships for new data infrastructure. Specific model improvements would be based on further testing and evaluation of existing models, the development and testing of modular model components and integration, and linkages of model integration platforms to new data management and visualization tools.

Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science.

We review the current state of agricultural systems science, focusing in particular on the capabilities and limitations of agricultural systems models. We discuss the state of models relative to five different Use Cases spanning field, farm, landscape, regional, and global spatial scales and engaging questions in past, current, and future time periods. Contributions from multiple disciplines have made major advances relevant to a wide range of agricultural system model applications at various spatial and temporal scales. Although current agricultural systems models have features that are needed for the Use Cases, we found that all of them have limitations and need to be improved. We identified common limitations across all Use Cases, namely 1) a scarcity of data for developing, evaluating, and applying agricultural system models and 2) inadequate knowledge systems that effectively communicate model results to society. We argue that these limitations are greater obstacles to progress than gaps in conceptual theory or available methods for using system models. New initiatives on open data show promise for addressing the data problem, but there also needs to be a cultural change among agricultural researchers to ensure that data for addressing the range of Use Cases are available for future model improvements and applications. We conclude that multiple platforms and multiple models are needed for model applications for different purposes. The Use Cases provide a useful framework for considering capabilities and limitations of existing models and data.

Implementing wastewater surveillance for SARS-CoV-2 on a university campus: Lessons learned.

Wastewater surveillance, also known as wastewater-based epidemiology (WBE), has been successfully used to detect SARS-CoV-2 and other viruses in sewage in many locations in the United States and globally. This includes implementation of the surveillance on college and university campuses. A two-phase study was conducted during the 2020-2021 academic year to test the feasibility of a WBE system on campus and to supplement the clinical COVID-19 testing performed for the student, staff, and faculty body. The primary objective during the Fall 2020 semester was to monitor a large portion of the on-campus population and to obtain an understanding of the spreading of the SARS-CoV-2 virus. The Spring 2021 objective was focused on selected residence halls and groups of residents on campus, as this was more efficient and relevant for an effective follow-up response. Logistical problems and planning oversights initially occurred but were corrected with improved communication and experience. Many lessons were learned, including effective mapping, site planning, communication, personnel organization, and equipment management, and obtained along the way, thereby paving an opportune guide for future planning efforts. PRACTITIONER POINTS: WBE was successful in the detection of many SARS-CoV-2 variants incl. Alpha, Beta, Gamma, Delta, Lambda, Mu, and Omicron. Careful planning and contingencies were essential for a successful implementation of a SARS-CoV-2 monitoring program. A surveillance program may be important for detection and monitoring of other public health relevant targets in wastewater incl. bacteria, viruses, fungi and viruses. Diverse lessons were learned incl. effective mapping, site planning, communication, personnel organization, and equipment management, thereby providing a guide for future planning efforts.

Convergent evolution of a symbiotic duet: the case of the lichen genus Polychidium (Peltigerales, Ascomycota).

PREMISE OF THE STUDY: Thallus architecture has long been a powerful guide for classifying lichens and has often trumped photobiont association and ascomatal type, but the reliability of these characters to predict phylogenetic affinity has seldom been tested. The cyanolichen genus Polychidium unites species that have strikingly similar gross morphology but consort with different photobiont genera. If Polychidium were found to be monophyletic, photobiont switching among closely related species would be suggested. If, however, species were found to arise in different lineages, a convergent body plan and ascomatal type evolution would be inferred. METHODS: We tested the monophyly of Polychidium with a multilocus phylogeny based on nuclear and mitochondrial sequence data from all known Peltigeralean families and reconstructed ancestral states for specific thallus architecture and ascomatal ontogeny types relative to Polychidium and other clades. KEY RESULTS: We found that Polychidium consists of two species groups that arose independently in different suborders within the Peltigerales, associated with Nostoc and Scytonema photobionts, respectively. We infer from ancestral character state reconstruction that dendroid thallus architecture evolved independently in these two lineages. CONCLUSIONS: The independent development of similar dendroid thallus architecture in different fungal suborders with different photobionts represents a clear and previously overlooked example of convergent evolution in lichens. Our results also suggest a pattern of character state conservation, loss, and reversion in ascomatal ontogeny types, hitherto considered conserved traits useful for higher level ascomycete systematics.

A novel transcriptomic approach to identify candidate genes for grain quality traits in wheat.

A novel methodology is described in which transcriptomics is combined with the measurement of bread-making quality and other agronomic traits for wheat genotypes grown in different environments (wet and cool or hot and dry conditions) to identify transcripts associated with these traits. Seven doubled haploid lines from the Spark x Rialto mapping population were selected to be matched for development and known alleles affecting quality. These were grown in polytunnels with different environments applied 14 days post-anthesis, and the whole experiment was repeated over 2 years. Transcriptomics using the wheat Affymetrix chip was carried out on whole caryopsis samples at two stages during grain filling. Transcript abundance was correlated with the traits for approximately 400 transcripts. About 30 of these were selected as being of most interest, and markers were derived from them and mapped using the population. Expression was identified as being under cis control for 11 of these and under trans control for 18. These transcripts are candidates for involvement in the biological processes which underlie genotypic variation in these traits.

Climate change impacts on global food security.

Climate change could potentially interrupt progress toward a world without hunger. A robust and coherent global pattern is discernible of the impacts of climate change on crop productivity that could have consequences for food availability. The stability of whole food systems may be at risk under climate change because of short-term variability in supply. However, the potential impact is less clear at regional scales, but it is likely that climate variability and change will exacerbate food insecurity in areas currently vulnerable to hunger and undernutrition. Likewise, it can be anticipated that food access and utilization will be affected indirectly via collateral effects on household and individual incomes, and food utilization could be impaired by loss of access to drinking water and damage to health. The evidence supports the need for considerable investment in adaptation and mitigation actions toward a "climate-smart food system" that is more resilient to climate change influences on food security.