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Undoubtedly, the advancement of IoT technology has created a plethora of new applications and a growing number of devices connected to the internet. Among these developments emerged the novel concept of smart farming. In this context, sensor nodes are used in farms to help farmers acquire a deeper insight into the environmental factors affecting their productivity. In recent years, we have witnessed an emerging trend of scholarly literature focused on smart farming. Some focus has been on system architecture for monitoring purposes, while another area of interest includes yield prediction. Humidity, air and soil temperature, solar radiation, and wind speed are some key weather elements monitored in smart farms. We introduce a mechanistic crop growth model to predict crop growth and subsequent yield, subject to weather, soil parameters, crop characteristics and management practices. We also seek to measure the influence of nitrogen on yield throughout the growing season. The machine learning models are trained to emulate the crop growth model in the state of Iowa (US). The multilayer perceptron (MLP) is chosen to evaluate the model prediction as it generates fewer errors. Furthermore, the MLP optimization model is used to maximize corn yield. The experiment was performed using different scenarios, stochastic gradient descent (SGD), and adaptive moment estimation (Adam) optimizers. The experiment results revealed that the SGD optimizer and the dataset with the scenario of unchanged parameters provided the highest crop yield compared to the mechanistic crop growth model.

Evaluating large numbers of genotypes and phenotypes in multi-environment trials is key to crop improvement for biomass performance in sorghum. In this dissertation, we developed an approach that integrates crop growth models with remote-sensing data and genetic information for modeling and predicting sorghum biomass yield. The goal of studies described in Chapter 2 was to parameterize the Agricultural Production Systems sIMulator (APSIM) crop growth models with remote-sensing and ground-reference data to predict variation in phenology and yield-related traits for 18 commercial grain and biomass sorghum hybrids. These studies showed that (i) biomass sorghum hybrids tended to have higher maximum plant height, final dry biomass and radiation use efficiency (RUE) than grain sorghum, (ii) photoperiod-sensitive sorghum hybrids exhibited greater biomass potential in longer growing environments and (iii) the parameterized APSIM models performed well in above-ground biomass simulations across years and locations. Crop growth models that integrate remote-sensing data offer an efficient approach to parameterize models for larger plant breeding populations. Understanding the genetic architecture of biomass productivity and bioenergy-related traits is another key aspect of bioenergy sorghum breeding programs. In Chapter 3, 619 sorghum genotypes from the sorghum diversity panel were individually crossed to ATx623 to create a half-sib population that was planted and evaluated in field trials in three consecutive years. Single-nucleotide polymorphisms (SNPs) were used in a genome-wide association study (GWAS) to identify genetic loci associated with variation in plant architecture and biomass productivity. A few SNPs associated with these traits were located in previously described genes including the sorghum dwarfing genes <i>Dw1</i> and <i>Dw3</i> and stay-green QTLs <i>Stg1</i> and <i>Stg4</i>. Of particular interest were seven genetic loci that were discovered for biomass yield. For three of these loci, the minor or uncommon allele exhibited a favorable effect on productivity suggesting opportunities to further improve the crop for biomass accumulation through plant breeding. Marker-assisted and genomic selection strategies may provide tools to introgress and exploit these genes for bioenergy sorghum development. Since parameterizing biophysical crop models requires extensive time and manual effort, a simple model was developed in Chapter 4 that used time-dependent measurements of RGB canopy cover and daily radiation coupled with end-of-season biomass for estimating seasonal radiation use efficiency (SRUE) in 619 sorghum hybrids. SRUE was shown to be a stable and heritable trait that has a positive relationship with aboveground dry biomass (ADB) over seasons. GWAS identified 11 SNPs associated with SRUE with the favorable effect represented by the minor allele for seven of these SNPs. Increasing the frequency of these favorable alleles may improve the breeding population. These results demonstrated that the simple model for calculating SRUE can be used in genetic studies and for parameterizing biophysical crop models. The studies integrating crop growth models with remote sensing technologies provide an opportunity to evaluate a large number of phenotypes for the target population to understand the underlying genetic variation of bioenergy sorghum.

Com o objetivo de parametrizar o modelo LINTUL (Light Interception and Utilization - Interceptação e Utilização da Luz) foi conduzido um experimento de campo, na safra 2003/2004, em Piracicaba-SP, na área experimental do Departamento de Produção Vegetal (Esalq) da Universidade de São Paulo, utilizando o procedimento de análise de crescimento. Ao longo do ciclo da cultura, foram efetuadas 24 amostragens, com 4 repetições, para determinação da massa de matéria seca de raiz, colmo, folha e órgão reprodutivo. Em função dos resultados obtidos referentes à parametrização do modelo LINTUL com base na comparação dos valores medidos e estimados do índice de área foliar (erro relativo entre -3,6 e +3,9%) e da produtividade potencial (erro relativo entre -1,0 e +3,2%) de grãos da cultura de milho, os valores dos parâmetros são: (i) escala: diária (1 dia); (ii) soma térmica entre a semeadura e emergência: 115oC.dia; (iii) soma térmica entre a emergência e o florescimento: 902oC.dia; (iv) soma térmica entre o florescimento e o ponto de maturidade fisiológica: 1009ºC.dia; (v) área foliar específica: 0,036 m2.g-1; (vi) índice de área foliar inicial: 0,017 m2.m-2 (BRS-1001 e BRS-3003) e 0,022 m2.m-2 (BRS-1010); (vii) índice de área foliar crítico: 5,7 m2.m-2; (viii) temperatura basal inferior: 10oC; temperatura basal superior: 40oC; (ix) temperatura ótima inferior: 28oC; (x) temperatura ótima superior: 35oC; (xi) eficiência do uso da radiação: 2,38 g.MJ-1 e (xii) coeficiente de extinção de luz: 0,7 m2.m-2. / With the purpose of parameterizing the LINTUL (Light Interception and Utilization) model a field experiment was carried out, during the season 2003/2004, in Piracicaba, state of São Paulo, Brazil, in the experimental area of Crop Science Department (Esalq) of University of São Paulo, using the crop growth analysis procedure. During the crop cycle, 24 sampling were done, with 4 replications, for root, stem, leaf and storage organ dry matter measurements. According to parameterization results of the LINTUL model based on comparison of the measured and estimated values of the leaf area index (relative error between -3.6 and +3.9%) and of the corn crop potential productivity (relative error between -1.0 and +3.2%), the parameters values are: (i) scale: daily (1 day); (ii) thermic index between sowing and emergence: 115oC.day; (iii) termic index between emergence and flowering: 902oC.day; (iv) termic index between flowering and physiological maturity point: 1009ºC.day; (v) specific leaf area: 0.036 m2.g-1; (vi) initial leaf area index: 0.017 m2.m-2 (BRS-1001 and BRS-3003) and 0.022 m2.m-2 (BRS-1010); (vii) critical leaf area index: 5.7 m2.m-2; (viii) basal temperature (inferior): 10oC; (ix) basal temperature (superior): 40oC; (x) optimum temperature (inferior): 28oC; (xi) optimum temperature (superior): 35oC; (xii) radiation efficiency use: 2.38 g.MJ-1 and (xiii) light extintion coefficient: 0.7 m2.m-2.

<div>Switchgrass (Panicum virgatum L.) is an important warm-season perennial grass in livestock systems and has been extensively researched as an herbaceous energy crop. Objectives of this series of studies were to compare morphological development, compositional quality, crop growth, and yield of a recently developed biofuel cultivar ‘Liberty’ to an improved forage cultivar ‘Shawnee’ in multiple Indiana environments. Pure stands of each cultivar were sampled in the field at Trafalgar and Roann, Indiana in 2016. In 2017, samples were collected at Trafalgar, Roann, and Lafayette, Indiana. Samples were collected weekly during the early season and every other week in the late season with development determined by use of the Mean Stage Count (MSC) and Mean Stage Weight (MSW) system.</div><div>In the morphological development study, MSC and MSW were linearly related to both GDD and DOY for both years. ‘Liberty’ growth lagged behind ‘Shawnee’ throughout the whole growing season by approximately seven days. Prediction equations for MSC and MSW were developed based on accumulated GDD and DOY for Trafalgar and Roann in 2017. The prediction equations for MSC as predicted by GDD explained from 84 to 93 percent of the variation in MSC across locations for ‘Shawnee’ and between 90 to 94 percent of the variation for ‘Liberty’. For MSW, ‘Shawnee’ and ‘Liberty’ prediction equations explained from 84 to 93 percent and 90 to 95 percent of the variation as predicted by GDD across locations, respectively.</div><div><br></div><div>In the compositional quality study, samples from every other sampling date were ground and analyzed using near-infrared reflectance spectroscopy (NIRS). Increasing nitrogen fertilizer caused a higher nitrogen concentration at a given MSC. The 0 kg N ha-1 fertilizer rate dropped below 10 mg g-1 nitrogen by MSC 2.2, whereas the 134 kg N ha-1 fertilizer rate had greater than 10 mg g-1 until MSC 2.7. ‘Liberty’ had increased Neutral Detergent Fiber (NDF) concentration as compared to ‘Shawnee’. For whole-plant samples, ‘Liberty’ averaged 727 mg g-1 NDF as compared to ‘Shawnee’ which averaged 718 mg g-1. ‘Liberty’ had 18 mg g-1 higher acid detergent fiber (ADF), on average, as compared to ‘Shawnee’. Acid Detergent Lignin (ADL) was not different among nitrogen fertilizer treatments. Stem-plus-sheath material accounted for a higher percentage of NDF, ADF, and ADL, in whole-plants as MSC increased, as compared to leaf blades. ‘Shawnee’ had higher IVDMD as compared to ‘Liberty’ and the biggest differences occurred around MSC 2.9. At MSC 2.9, ‘Shawnee’ whole-plant IVDMD was 448 mg g-1 and ‘Liberty’ whole-plant IVDMD was 430 mg g-1. Whole-plant ash concentration decreased as MSC increased.<br></div><div><br></div><div>For the study that evaluated crop growth and yield, differences in grams m-2, mass tiller-1, and tiller number per unit area were analyzed in response to growing degree days (GDD) and day of year (DOY). Number of tillers had a negative linear response to GDD and DOY for both years, whereas, mass tiller-1 had a positive linear response to GDD and DOY for both years. Grams m-2 responded quadratically to GDD and DOY. Generally, ‘Liberty’ had 20 percent higher mass tiller-1 and lower number of tillers per m-2 at the end of the season as compared to ‘Shawnee.’ Addition of nitrogen fertilizer generally increased mass tiller-1 and grams m-2. Roann, the northern most site, also had highest tiller numbers at the beginning of the season and decreased faster than at the central Indiana sites. ‘Liberty’ yielded 8.8 percent higher than ‘Shawnee’ across locations, nitrogen rates, and sampling years. Addition of nitrogen fertilizer did not conclusively increase yield. Grams m-2, mass tiller-1, and tillers per sample area helped explain some yield differences. For example, ‘Liberty’ had increased yield as compared to ‘Shawnee’, and ‘Liberty’ also had higher mass tiller-1 with no differences in tiller number between cultivars. While additions of nitrogen fertilizer increased grams per tiller, yield was not significantly increased with added nitrogen fertilizer. Therefore, these measures should not stand alone as a predictor of yield differences between cultivars. Switchgrass is a bunchgrass and has inherent difference in numbers of plant and tillers per plant within a plot, which may not be truly represented by one crop growth parameter alone.</div><div><br></div><div>This study confirms that switchgrass has great potential as a forage and biofuel crop in Indiana with low nitrogen fertilizer requirements and high yield. Understanding how switchgrass morphological development, compositional quality, growth, and yield responds in Indiana environments across locations, years, and nitrogen rates will help guide the future switchgrass management decisions of producers and researchers.</div>

Com o objetivo de parametrizar o modelo LINTUL (Light Interception and Utilization - Interceptação e Utilização da Luz) foi conduzido um experimento de campo, na safra 2003/2004, em Piracicaba-SP, na área experimental do Departamento de Produção Vegetal (Esalq) da Universidade de São Paulo, utilizando o procedimento de análise de crescimento. Ao longo do ciclo da cultura, foram efetuadas 24 amostragens, com 4 repetições, para determinação da massa de matéria seca de raiz, colmo, folha e órgão reprodutivo. Em função dos resultados obtidos referentes à parametrização do modelo LINTUL com base na comparação dos valores medidos e estimados do índice de área foliar (erro relativo entre -3,6 e +3,9%) e da produtividade potencial (erro relativo entre -1,0 e +3,2%) de grãos da cultura de milho, os valores dos parâmetros são: (i) escala: diária (1 dia); (ii) soma térmica entre a semeadura e emergência: 115oC.dia; (iii) soma térmica entre a emergência e o florescimento: 902oC.dia; (iv) soma térmica entre o florescimento e o ponto de maturidade fisiológica: 1009ºC.dia; (v) área foliar específica: 0,036 m2.g-1; (vi) índice de área foliar inicial: 0,017 m2.m-2 (BRS-1001 e BRS-3003) e 0,022 m2.m-2 (BRS-1010); (vii) índice de área foliar crítico: 5,7 m2.m-2; (viii) temperatura basal inferior: 10oC; temperatura basal superior: 40oC; (ix) temperatura ótima inferior: 28oC; (x) temperatura ótima superior: 35oC; (xi) eficiência do uso da radiação: 2,38 g.MJ-1 e (xii) coeficiente de extinção de luz: 0,7 m2.m-2. / With the purpose of parameterizing the LINTUL (Light Interception and Utilization) model a field experiment was carried out, during the season 2003/2004, in Piracicaba, state of São Paulo, Brazil, in the experimental area of Crop Science Department (Esalq) of University of São Paulo, using the crop growth analysis procedure. During the crop cycle, 24 sampling were done, with 4 replications, for root, stem, leaf and storage organ dry matter measurements. According to parameterization results of the LINTUL model based on comparison of the measured and estimated values of the leaf area index (relative error between -3.6 and +3.9%) and of the corn crop potential productivity (relative error between -1.0 and +3.2%), the parameters values are: (i) scale: daily (1 day); (ii) thermic index between sowing and emergence: 115oC.day; (iii) termic index between emergence and flowering: 902oC.day; (iv) termic index between flowering and physiological maturity point: 1009ºC.day; (v) specific leaf area: 0.036 m2.g-1; (vi) initial leaf area index: 0.017 m2.m-2 (BRS-1001 and BRS-3003) and 0.022 m2.m-2 (BRS-1010); (vii) critical leaf area index: 5.7 m2.m-2; (viii) basal temperature (inferior): 10oC; (ix) basal temperature (superior): 40oC; (x) optimum temperature (inferior): 28oC; (xi) optimum temperature (superior): 35oC; (xii) radiation efficiency use: 2.38 g.MJ-1 and (xiii) light extintion coefficient: 0.7 m2.m-2.

Doctor of Philosophy / Precision Agriculture (PA) strives towards holistic production and environmental management. A fundamental research challenge is the continuous expansion of ideas about how PA can contribute to sustainable agriculture. Some associated pragmatic research challenges include quantification of spatio-temporal variation of crop yield; crop growth simulation modelling within a PA context and; evaluating long-term financial and environmental outcomes from site-specific crop management (SSCM). In Chapter 1 literature about managing whole farms with a mind towards sustainability was reviewed. Alternative agricultural systems and concepts including systems thinking, agro-ecology, mosaic farming and PA were investigated. With respect to environmental outcomes it was found that PA research is relatively immature. There is scope to thoroughly evaluate PA from a long-term, whole-farm environmental and financial perspective. Comparatively, the emphasis of PA research on managing spatial variability offers promising and innovative ways forward, particularly in terms of designing new farming systems. It was found that using crop growth simulation modelling in a PA context is potentially very useful. Modelling high-resolution spatial and temporal variability with current simulation models poses a number of immediate research issues. This research focused on three whole farms located in Australia that grow predominantly grains without irrigation. These study sites represent three important grain growing regions within Australia. These are northern NSW, north-east Victoria and South Australia. Note-worthy environmental and climatic differences between these regions such as rainfall timing, soil type and topographic features were outlined in Chapter 2. When considering adoption of SSCM, it is essential to understand the impact of temporal variation on the potential value of managing spatial variation. Quantifying spatiotemporal variation of crop yield serves this purpose; however, this is a conceptually and practically challenging undertaking. A small number of previous studies have found that the magnitude of temporal variation far exceeds that of spatial variation. Chapter 3 of this thesis dealt with existing and new approaches quantifying the relationship between spatial and temporal variability in crop yield. It was found that using pseudo cross variography to obtain spatial and temporal variation ‘equivalents’ is a promising approach to quantitatively comparing spatial and temporal variation. The results from this research indicate that more data in the temporal dimension is required to enable thorough analysis using this approach. This is particularly relevant when questioning the suitability of SSCM. Crop growth simulation modelling offers PA a number of benefits such as the ability to simulate a considerable volume of data in the temporal dimension. A dominant challenge recognised within the PA/modelling literature is the mismatch between the spatial resolution of point-based model output (and therefore input) and the spatial resolution of information demanded by PA. This culminates into questions about the conceptual model underpinning the simulation model and the practicality of using point-based models to simulate spatial variability. iii The ability of point-based models to simulate appropriate spatial and temporal variability of crop yield and the importance of soil available water capacity (AWC) for these simulations were investigated in Chapter 4. The results indicated that simulated spatial variation is low compared to some previously reported spatial variability of real yield data for some climate years. It was found that the structure of spatial yield variation was directly related to the structure of the AWC and interactions between AWC and climate. It is apparent that varying AWC spatially is a reasonable starting point for modelling spatial variation of crop yield. A trade-off between capturing adequate spatio-temporal variation of crop yield and the inclusion of realistically obtainable model inputs is identified. A number of practical solutions to model parameterisation for PA purposes are identified in the literature. A popular approach is to minimise the number of simulations required. Another approach that enables modelling at every desired point across a study area involves taking advantage of high-resolution yield information from a number of years to estimate site-specific soil properties with the inverse use of a crop growth simulation model. Inverse meta-modelling was undertaken in Chapter 5 to estimate AWC on 10- metre grids across each of the study farms. This proved to be an efficient approach to obtaining high-resolution AWC information at the spatial extent of whole farms. The AWC estimates proved useful for yield prediction using simple linear regression as opposed to application within a complex crop growth simulation model. The ability of point-based models to simulate spatial variation was re-visited in Chapter 6 with respect to the exclusion of lateral water movement. The addition of a topographic component into the simple point-based yield prediction models substantially improved yield predictions. The value of these additions was interpreted using coefficients of determination and comparing variograms for each of the yield prediction components. A result consistent with the preceding chapter is the importance of further validating the yield prediction models with further yield data when it becomes available. Finally, some whole-farm management scenarios using SSCM were synthesised in Chapter 7. A framework that enables evaluation of the long-term (50 years) farm outcomes soil carbon sequestration, nitrogen leaching and crop yield was established. The suitability of SSCM across whole-farms over the long term was investigated and it was found that the suitability of SSCM is confined to certain fields. This analysis also enabled identification of parts of the farms that are the least financially and environmentally viable. SSCM in conjunction with other PA management strategies is identified as a promising approach to long-term and whole-farm integrated management.

Doctor of Philosophy / Precision Agriculture (PA) strives towards holistic production and environmental management. A fundamental research challenge is the continuous expansion of ideas about how PA can contribute to sustainable agriculture. Some associated pragmatic research challenges include quantification of spatio-temporal variation of crop yield; crop growth simulation modelling within a PA context and; evaluating long-term financial and environmental outcomes from site-specific crop management (SSCM). In Chapter 1 literature about managing whole farms with a mind towards sustainability was reviewed. Alternative agricultural systems and concepts including systems thinking, agro-ecology, mosaic farming and PA were investigated. With respect to environmental outcomes it was found that PA research is relatively immature. There is scope to thoroughly evaluate PA from a long-term, whole-farm environmental and financial perspective. Comparatively, the emphasis of PA research on managing spatial variability offers promising and innovative ways forward, particularly in terms of designing new farming systems. It was found that using crop growth simulation modelling in a PA context is potentially very useful. Modelling high-resolution spatial and temporal variability with current simulation models poses a number of immediate research issues. This research focused on three whole farms located in Australia that grow predominantly grains without irrigation. These study sites represent three important grain growing regions within Australia. These are northern NSW, north-east Victoria and South Australia. Note-worthy environmental and climatic differences between these regions such as rainfall timing, soil type and topographic features were outlined in Chapter 2. When considering adoption of SSCM, it is essential to understand the impact of temporal variation on the potential value of managing spatial variation. Quantifying spatiotemporal variation of crop yield serves this purpose; however, this is a conceptually and practically challenging undertaking. A small number of previous studies have found that the magnitude of temporal variation far exceeds that of spatial variation. Chapter 3 of this thesis dealt with existing and new approaches quantifying the relationship between spatial and temporal variability in crop yield. It was found that using pseudo cross variography to obtain spatial and temporal variation ‘equivalents’ is a promising approach to quantitatively comparing spatial and temporal variation. The results from this research indicate that more data in the temporal dimension is required to enable thorough analysis using this approach. This is particularly relevant when questioning the suitability of SSCM. Crop growth simulation modelling offers PA a number of benefits such as the ability to simulate a considerable volume of data in the temporal dimension. A dominant challenge recognised within the PA/modelling literature is the mismatch between the spatial resolution of point-based model output (and therefore input) and the spatial resolution of information demanded by PA. This culminates into questions about the conceptual model underpinning the simulation model and the practicality of using point-based models to simulate spatial variability. iii The ability of point-based models to simulate appropriate spatial and temporal variability of crop yield and the importance of soil available water capacity (AWC) for these simulations were investigated in Chapter 4. The results indicated that simulated spatial variation is low compared to some previously reported spatial variability of real yield data for some climate years. It was found that the structure of spatial yield variation was directly related to the structure of the AWC and interactions between AWC and climate. It is apparent that varying AWC spatially is a reasonable starting point for modelling spatial variation of crop yield. A trade-off between capturing adequate spatio-temporal variation of crop yield and the inclusion of realistically obtainable model inputs is identified. A number of practical solutions to model parameterisation for PA purposes are identified in the literature. A popular approach is to minimise the number of simulations required. Another approach that enables modelling at every desired point across a study area involves taking advantage of high-resolution yield information from a number of years to estimate site-specific soil properties with the inverse use of a crop growth simulation model. Inverse meta-modelling was undertaken in Chapter 5 to estimate AWC on 10- metre grids across each of the study farms. This proved to be an efficient approach to obtaining high-resolution AWC information at the spatial extent of whole farms. The AWC estimates proved useful for yield prediction using simple linear regression as opposed to application within a complex crop growth simulation model. The ability of point-based models to simulate spatial variation was re-visited in Chapter 6 with respect to the exclusion of lateral water movement. The addition of a topographic component into the simple point-based yield prediction models substantially improved yield predictions. The value of these additions was interpreted using coefficients of determination and comparing variograms for each of the yield prediction components. A result consistent with the preceding chapter is the importance of further validating the yield prediction models with further yield data when it becomes available. Finally, some whole-farm management scenarios using SSCM were synthesised in Chapter 7. A framework that enables evaluation of the long-term (50 years) farm outcomes soil carbon sequestration, nitrogen leaching and crop yield was established. The suitability of SSCM across whole-farms over the long term was investigated and it was found that the suitability of SSCM is confined to certain fields. This analysis also enabled identification of parts of the farms that are the least financially and environmentally viable. SSCM in conjunction with other PA management strategies is identified as a promising approach to long-term and whole-farm integrated management.

<p>Nitrogen (N) is one of the major nutrient elements that affects growth, development, and quality of floriculture crops. Both sub-optimal and supra-optimal levels of N can negatively affect crop growth. In addition, over- fertilization may cause run-off and leaching of the N fertilizer leading to environmental pollution. Therefore, it is crucial to maintain optimal N level in plant tissue to produce good quality crops and increase productivity. This requires regular monitoring and measurement of plant N status. Laboratory analysis, the only direct method available to measure tissue N content, is destructive of plant tissue and expensive. Other available indirect methods are laborious, expensive, and/ or less reliable. In addition to measuring plant N status, it is crucial to understand acclimation responses at biochemical, leaf, and whole-plant levels in floriculture crops to N-deficit conditions. This will aid in developing a mechanistic model of plant responses to sub-optimal levels of N, proper fertilizer guidelines during production, and screening tools for identifying new varieties with tolerance to low-N level in the root zone. Unfortunately, there is limited research on floriculture crops that is simultaneously focused on plant responses at different scales to N-deficit conditions. The objectives of this research were to (i) assess the feasibility of image-based reflectance ratios for estimating tissue N content in poinsettia (Expt. 1), (ii) develop an affordable, remote sensor that can accurately and non-destructively estimate tissue N <a>content</a> in poinsettia (Expt. 2), (iii) study the physiological acclimation at whole-plant, leaf, and biochemical scales in poinsettia cultivars to N-deficit conditions (Expt. 3).</p> <p>In Expt. 1, we compared several spectral ratios based on the ratio of reflectance of near infrared <i>(R₈₇₀)</i> to reflectance of blue (<i>R</i><i>₈₇₀/R₄₅₀</i>), green (<i>R₈₇₀/R₅₂₁</i>), yellow (<i>R₈₇₀/R₅₉₃</i>), red (<i>R₈₇₀/R₆₂₅</i>), hyper-red (<i>R₈₇₀/R₆₆₀</i>), and far-red(<i>R₈₇₀/R₇₃₀</i>) wavelengths from plants<i></i>to measure whole-plant tissue N content in<i></i>four cultivars of poinsettia (<i>Euphorbia pulcherrima</i>) using a multispectral image station. Results indicated the reflectance ratio <i>R₈₇₀/R₆₂₅</i> was most suitable for assessing tissue N content in plants. In Expt. 2, a low-cost remote sensor was developed based on the findings of Expt. 1 that captured red and near-infrared images of plants, from which a reflectance ratio (<i>R_ratio</i>) was developed. The ratio was linearly related to tissue N content in all poinsettia cultivars. Furthermore, <i>R_ratio</i>was found to be more specific to N than to other elements in the tissue and related to the chlorophyll concentration of the plant. In Expt. 3, poinsettia cultivars ‘Jubilee Red’ (‘JR’) and ‘Peterstar Red’ (‘PSR’) displayed different acclimation strategies for physiology and growth under N-deficit conditions. Significantly higher growth was observed in ‘JR’ than in ‘PSR’ in the sub-optimal treatment, which indicates that ‘JR’ is more tolerant to N stress compared to ‘PSR’. Further analyses indicated that N uptake was higher in ‘JR’ than in ‘PSR’ under N-deficit conditions, without any changes in root morphology or growth. This is possible when higher levels of energy are available to transport nitrate and/or ammonia from the substrate into the root cells. Supporting this, significantly higher photosynthesis and carboxylation efficiency were observed in ‘JR’ than ‘PSR’ under N-deficit condition. These results shows that higher growth of ‘JR’ than ‘PSR’ under N-deficit conditions was likely due to increased N uptake (likely due to increased energy-driven transporter activity), which increased tissue N and chlorophyll levels. Further, these increases resulted in higher carboxylation efficiency and photosynthesis by ‘JR’ than ‘PSR’. Increased carbohydrate synthesis supported leaf growth and provided required energy in the fine root cells for N uptake from the substrate.</p>

<p><a>Facing challenges for increasing demands for agricultural land, water, and energy, aquaponics has emerged as a sustainable solution that can contribute to global food production while minimizing environmental impacts. In a recirculating aquaponic system, the waste produced by aquatic animals is processed through microbes and breaks down into compounds for plant uptake. By recycling nutrients and water between hydroponics and aquaculture systems, aquaponics can reduce the waste of fish feeds and the use of chemical fertilizers and use 90-99% less water than conventional aquaculture. However, a few studies reported that nutrient use efficiency is still low in aquaponics, and only 10-37% and 20-30% of nitrogen (N) is typically assimilated by plants and fish, respectively. Yield reduction is commonly reported for plants in aquaponics. Due to the unique water physical and chemical environment, the microbiomes are more diverse in aquaponics than in hydroponics. While the most important microbial group is considered nitrifying bacteria, <i>Nitrosomonas</i> spp. and <i>Nitrobacter</i> spp. mediating the N conversion process from ammonia into nitrate,</a> some plant growth-promoting bacteria (PGPB) in soils were found in aquaponics indicating their important function in the system. Meanwhile, the use of aquaculture wastewater can introduce and promote the growth of harmful microbial pathogens, posing a food safety concern. </p> The goal of this research is to investigate the effects of microbiomes in aquaponic systems. A series of studies were conducted to examine the effects of different bacterial groups on food crop yield and quality and investigate the potential risk of contamination with enteric pathogens in aquaponic systems. The specific objectives are: to 1) examine whether enteric pathogens present in aquaponics and hydroponics; 2) investigate the effects of plant age and root damage on internalization of STEC <i>E. coli</i> in leafy vegetables and herbs. 3) examine the effects of pH on the plant yield in aquaponics; and 4) investigate the effects of PGPB on lettuce in aquaponics and hydroponics3. The data obtained from this research will fill the knowledge gap and provide new management strategies for cultivating crops in aquaponics, which will greatly promote the application of aquaponics to provide a solution for the increasing food demands in the future.