Integrated aquaponic food production systems are capable of producing more food on less land using less water than conventional food systems, and marine systems offer the potential of conserving freshwater resources. However, critical factors such as suitable species combinations, environmental conditions (salinity and pH), and nutrient management (animal to plant ratio, C/N ratio, and dietary crude protein) have not been fully understood for marine systems. There were four objectives in this project. The first objective was to evaluate the growth performance of potential comparable combination (whiteleg shrimp with three halophytic plants) for the development of marine aquaponics with BFT under different salinities. The second objective was to evaluate stocking densities and the C/N ratio on growth and production of whiteleg shrimp and three halophytes. The third objective was evaluation of varying concentrations of dietary crude protein in practical diets fed to shrimp raised in biofloc aquaponic saltwater systems. The fourth objective was to evaluate effects of pH levels and additional C on the growth and production of whiteleg shrimp and five plant species in marine aquaponics. Four conclusions were determined: 1) Regarding marine aquaponics, whiteleg shrimp and the three halophytes (Atriplex hortensis, Salsola komarovii, and Plantago coronopus) are suitable combinations for future development. According to the research results, shrimp performed better in a salinity of 15 and 20 ppt; yet, plants performed better in a salinity of 10 and 15 ppt. Therefore, a salinity of 15 ppt is suggested as the optimal saline condition for shrimp and the three halophytes in an indoor marine aquaponics system. In addition, inoculating probiotics do have the efficiency of stabilizing water quality, cultivating microbial community, and enhancing the health of shrimp and plants in the operation of aquaponics. 2) The stocking density ratio and C/N ratio exerted significant impacts on the performance of shrimp and plants in marine aquaponics. Shrimp performed better with the stocking density of 2:1 and 3:1, with no impact from the C/N ratio. Conversely, plants performed better with the stocking density of 3:1 and 5:1 with the C/N ratio at 15. Therefore, a stocking density ratio of 3:1 with a C/N ratio at 15 is suggested as the optimal condition for shrimp and the three halophytes in an indoor marine aquaponic food production system. Inoculating the water with biofloc and applying probiotics regularly can enhance the management of water quality and the health of shrimp and plants in aquaponics. 3) Among the findings of the study, shrimp growth was not affected by the protein content of the feed, suggesting that it is possible to use feeds with lower protein concentration when culturing shrimp in biofloc-based marine aquaponics. However, plants grew better in the treatments with higher protein content feed in the early and middle stages of production. Hence, for maximum production, providing a higher protein concentration feed (35 %) in the early stages of system start-up, and switching to a lower protein concentration feed (30 %) in the later stages of cultivation might be feasible. 4) The current study found no significant effects of pH or additional C on shrimp performance. In contrast, plants grew better in lower pH treatments, while additional C supplements improved the performance of plants grown in higher pH treatments and had similar results to the lower pH treatments. We suggest that RO water is not suitable source of water for shrimp-based marine aquaponics if ionic composition is not managed. The addition of C, however, led to improved growth and yields of most plants. Hence, adding C can be a promising approach in marine aquaponics to enhance the resistance to the abiotic stress of plants and improve their growth.<div> <br>The present study on marine aquaponics has produced important findings that will fill some knowledge gaps, provide management guidelines for production, and facilitate its development. <br></div>

<p>Breeding for improved animal welfare is paramount for increasing the long-term sustainability of the animal food industry. In this context, the main objectives of this dissertation were to understand the genetic and genomic background of welfare indicators in livestock and evaluate the feasibility of single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) for performing genomic selection in beef cattle. This dissertation includes five studies. First, we aimed to test and identify an optimal ssGBLUP scenario for crossbreeding schemes. We simulated multiple populations differing based on the genetic background of the trait, and then we tested alternative models, such as multiple-trait weighted ssGBLUP. Even though more elaborated scenarios were evaluated, a single-trait ssGBLUP approach was recommended when genetic correlation across populations were higher than 0.70. The goal of the second study was to identify genomic regions controlling behavior traits that are conserved across livestock species. We systematically reviewed genomic regions associated with behavioral indicators in beef and dairy cattle, pigs, and sheep. The genomic regions identified in this study were located in genes previously reported controlling human behavioral, neural, and mental disorders. In the third study we used a large dataset (675,678 records) from North American Angus cattle to investigate the genetic background of temperament, a behavioral indicator, recorded on one-year-old calves, and provide the models and protocols for implementing genomic selection. We reported a heritability estimate equal to 0.38 for yearling temperament, and it was, in general, genetically favorably correlated with other productivity and fertility traits. Candidate genomic regions controlling yearling temperament were also identified. The fourth study was based on temperament recorded on North American Angus cows from 2 to 15 years of age (797,187 records). The goal was to understand the genetic and genomic background of temperament across the animal’s lifetime. By fitting a random regression model, we observed that temperament is highly genetically correlated across time. However, animals have differential learning and behavioral plasticity (LBP; changes of the phenotype overtime), although the LBP heritability is low. In our last study we evaluated foot scores (foot angle, FA; and claw set, CS) in American (US) and Australian (AU) Angus cattle aiming to assess the genetic and genomic background of foot scores and investigate the feasibility of performing an across-country genomic evaluation (~1.15 million animals genotyped). Foot scores are heritable (heritability from 0.22 to 0.27), and genotype-by-environment interaction was observed between US and AU Angus populations (genetic correlation equal to 0.61 for FA and 0.76 for CS). An across-country genomic prediction outperformed within-country evaluations in terms of predictivity ability (bias, dispersion, and validation accuracy) and theoretical accuracies. We have also identified genes associated with FA and CS previously reported in human’s bone structure and repair mechanism. In conclusion, this dissertation presents a comprehensive genetic and genomic characterization of welfare indicators (temperament and foot scores) in (inter)national livestock populations. </p>

<p>Field-edge buffers are a promising way to address nonpoint source pollution from agricultural runoff, but concentrated runoff flow often renders standard fixed-width linear buffers ineffective. AgBufferBuilder (ABB) is a tool within ESRI ArcMap Geographic Information Systems software that designs and evaluates targeted, nonlinear buffers based on hydrologic modeling and other field-specific parameters. We tested ABB on n=45 Areas of Interest (AOIs) stratified based on estimated sediment loading across three sub-watersheds within Michigan’s Saginaw Bay watershed to evaluate the effectiveness of ABB relative to existing practices across a wide range of landscape conditions. We modeled tractor movement around ABB buffer designs to assess more realistic versions of the likely final designs. ABB regularly failed to deliver the desired 75% sediment capture rate using default 9 m x 9 m output raster resolution, with Proposed buffers capturing from 0% to 68.49% of sediment within a given AOI (mean=37.56%). Differences in sediment capture between Proposed and Existing buffers (measured as Proposed – Existing) ranged from -48% to 66.81% of sediment (mean=24.70%). Proposed buffers were estimated to capture more sediment than Existing buffers in 37 of 45 AOIs, representing potential for real improvements over Existing buffers across the wider landscape. In 13 of 45 AOIs, ABB buffers modified for tractor movement captured more sediment than Existing buffers using less total buffer area. We conducted a collaborative design process with three Saginaw Bay watershed farmers to assess their willingness to implement ABB designs. Feedback indicated farmers may prefer in-field erosion control practices like cover cropping and grassed waterways over field-edge ABB designs. More farmer input is needed to better assess farmer perspectives on ABB buffers and to identify preferred data-based design alternatives. Engineered drainage systems with raised ditch berms and upslope catch basins piped underground directly into ditches were encountered several times during site visits. ABB only models surface flow and does not recognize drain output flow entering waterways. Modified ABB functionality that models buffers around drain inlets would greatly improve its functionality on drained sites. This may be accomplishable through modification of user-entered AOI margins but requires further investigation. Unfortunately, the existing tool is built for outdated software and is not widely accessible to non-expert users. We suggest that an update of this tool with additional functionality and user accessibility would be a useful addition in the toolbox of conservation professionals in agricultural landscapes.</p>

This research work concentrate on developing digital soil maps to support field based plant phenotyping research. We have developed soil organic matter content (OM), cation exchange capacity (CEC), natural soil drainage class, and tile drainage line maps using topographic indices and aerial imagery. Various prediction models (universal kriging, cubist, random forest, C5.0, artificial neural network, and multinomial logistic regression) were used to estimate the soil properties of interest.