Optimizing nutrient delivery channels -
Precision placement is possible with drip-tube irrigation, but sprinkler irrigation has the same limitations as spinner spreaders. Nutrients should not be applied through sprinkler irrigation systems unless vegetative buffers are provided.
Right Timing. Apply Nitrogen and Phosphorus When Needed. The timing of application is more important with nitrogen than with any other nutrient because nitrogen is applied in large amounts to many crops and is highly mobile. Phosphorus is stable when it is mixed into the soil and can be applied when most convenient.
Ideally, nitrogen should be applied frequently in small amounts that are tailored to the plants' immediate needs. This is usually feasible only where fertigation is used or with high-value crops. For most crops, nitrogen should be applied in split applications that coincide as closely as possible with the uptake pattern of the crop.
For example, corn requires relatively little nitrogen early in the growth cycle, but the need increases considerably when the plant begins to elongate. Therefore, most of the nitrogen required by corn should be applied as sidedressing after the plants are established.
Fall application of nitrogen for spring-planted crops is never recommended in North Carolina. Improper amounts or placement of fertilizers or animal waste can lead to water pollution and poor crop growth. Guidelines on proper use of animal waste can be found in Extension Service publications Swine Manure as a Fertilizer Source , AG and Poultry Litter as a Fertilizer Source , AG All nutrients can be lost when soil is eroded, but phosphorus is especially vulnerable.
The primary way to prevent phosphorus loss is to control erosion. If no sediments leave the land, sediment-attached phosphorus does not leave, although soluble phosphorus may be lost.
Many erosion control BMPs can be used in various cropping systems across North Carolina. A conservation farm plan providing for erosion control should be developed with assistance from the USDA Natural Resources Conservation Service or your county Soil and Water Conservation District. Some specific practices are:.
The first step in manure nutrient management is to control where the manure is deposited. Most swine and poultry are confined, and their manure is concentrated and managed. Recommended practices for handling and using swine and poultry manure are given in the Extension publications noted earlier.
Most cattle, horses, sheep, and other large animals are pastured. Manure is therefore deposited at random, making nutrient management more difficult. Cattle and other livestock should not be allowed free access to streams and ponds as waste will be deposited directly in the water resources, and animal traffic can cause soil disturbance and accelerated erosion of stream banks.
After fencing the livestock out of the water, alternate water supplies should be provided by diverting or pumping water to livestock, preferably using watering tanks. Clean water sources benefit animal health and rate-ofgain as well as water quality. Feed, water, and lounge areas where animals congregate should be located so that runoff is filtered through vegetative buffer strips before entering streams and ponds.
We are becoming increasingly aware that almost everything we do may have some potential negative effect on the environment. Conservation practices or BMPs are designed to reduce the negative effects of agricultural production on surface and ground water resources.
In some especially sensitive areas, the acceptable level of production may be minimal, especially with respect to fertilization. In other places, fertilizers may be used along with BMPs. Fertilizers and other nutrient sources should never be applied haphazardly.
No single set of BMPs applies in all situations. The BMPs presented here are for nutrient management on a wide variety of agricultural lands across the state. The best set of practices for a specific cropping situation will depend on individual circumstances; however, it is always recommended to use a combination of BMPs to avoid, control, and trap nitrogen and phosphorus.
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Browse SoilFacts. Best Management Practices for Agricultural Nutrients SoilFacts. Best Management Practices Skip to Best Management Practices. Nutrient Management or the 4 Rs Skip to Nutrient Management or the 4 Rs. Erosion Control Skip to Erosion Control. Livestock Exclusion Fencing Skip to Livestock Exclusion Fencing.
Conclusion Skip to Conclusion. Authors Ekrem Ozlu Assistant Professor and Extension Specialist. Deanna Osmond Department Extension Leader Nutrient Mgt and Water Quality Crop and Soil Sciences.
Keywords: Best Management Practice Water Quality Nitrogen Nutrient Management Phosphorus Management Soil. Find more information at the following NC State Extension websites: Poultry Extension Soil Fertility Soil Health and Management Swine.
This publication printed on: Feb. Recent studies have attempted to couple GSMs with dynamic flux balance analysis to optimize feeding strategies in order to improve ethanol production in Saccharomyces cerevisiae and Escherichia coli cultures in silico 11 , Another study used a GSM to optimize media supply and maintain C.
vulgaris biomass The majority of these studies utilized a single model to optimize nutrient feeding, however, cells dynamically change their biomass composition under different culture conditions Previously, our group developed a genome-scale metabolic model with dynamic biomass compositions based on experimentally determined biomass measurements and corresponding omics data We then applied this dynamic model to predict nutrient requirements under autotrophic, heterotrophic, and nitrogen-limited conditions MPC has become a widely used strategy to optimize biomanufacturing by using a model to predict cellular behavior, which is a highly nonlinear process.
To utilize this approach, kinetic models have been applied with a nonlinear MPC algorithm to optimize fixation of CO 2 in C. vulgaris cultures Another study applied an artificial neural network to build data-driven models and regulate light intensity for a microalgae culture Both studies demonstrated the robustness of this MPC strategy but primarily utilized kinetic equations modeling nutrient transport from extracellular environments without detailed representation of the entire metabolism.
Indeed, a previous large-scale outdoor culture study indicated that the significant barrier to algal bioproduction was the lack of understanding of microalgal biology for optimal biomass generation To improve our knowledge of metabolism, GSMs have been constructed for multiple algal species by our group and others and used as tools to improve strain development, to identify metabolic bottleneck and to understand microbiome interactions, among other applications 20 , In this regard, GSMs have been used to predict metabolic demands under different culture conditions such as photoautrophic and heterotrophic environments, depletion of nitrogen source or for multiple species interaction This expansive description of cellular metabolism provides a valuable knowledge base that can also be used as a powerful tool for controlling cellular performance in biomanufacturing processes.
Therefore, in this study we incorporated separate photoautotrophic i CZPA-T1 and heterotrophic i CZH-T1 genome-scale metabolic models for light and dark cycles, respectively, in order to optimize metabolic pathways and utilization of nutrient supplies and compared their performance in terms of production of biomass, fatty acids FAs , and lutein to standard autotrophic and heterotrophic cultivations.
The metabolic models were then used to control nutrient supply in both open-loop and closed-loop configurations, which represents a useful approach to efficiently channel diverse nutrients to algal cellular components and enhance production of algal-derived bioproducts during day-night cycles.
To enhance control, we adapted the parameters in the closed-loop controller based on feedback measurements of biomass and nutrients to improve model predictions for bioreactor operations. This approach of closed-loop genome model process control GMPC demonstrated significant improvements in nutrient utilization efficiencies compared to conventional fed-batch approaches.
Finally, we demonstrated superior performance for this GMPC system compared to conventional PID systems, illustrating the value of this methodology for improving biomanufacturing processes. Previously, a collection of genome-scale metabolic models GSMs of C. Here, we expanded on this methodology to apply photoautotrophic i CZPAT1 and heterotrophic i CZHT1 models, in sequence to predict nutrient requirements under alternating light and dark cycles, which mimic conditions present in outdoor cultivation systems Fig.
The OD measurements were converted to biomass concentration, which served as constraints in genome-scale metabolic models to predict nutrient requirements to support a fixed growth rate under alternating autotrophic and heterotrophic conditions. The GSM i CZH-T1 was applied to predict glucose and nitrate requirements during heterotrophic growth, which averaged around 0.
In this way, the alternating day-night cultures received the appropriate glucose and nitrate feeds, based on the model predictions, for growth during the dark periods. Alternatively, for the light cycle, the model was switched to the photoautotrophic model i CZPA-T1 see Supplementary Table 1 in order to predict nitrate feeding requirements, which averaged around 0.
b Cell growth. c Glucose uptake in all three conditions. e Nitrate uptake in all three conditions. g Biomass concentration, fatty acid titer and lutein titer.
h Biomass yield, Fatty acid yield and lutein yield on glucose. While no glucose was present in the autotrophic cultures, the cells still grew by fixing CO 2 and reached OD of approximately 1. Previous studies found that culturing C.
vulgaris under heterotrophic conditions can improve its growth under subsequent autotrophic conditions Indeed, the activation of Rubisco protein under autotrophic conditions can also support higher growth rates under subsequent heterotrophic conditions These studies demonstrated the synergistic benefits from alternating light and dark cycles and helped to explain the comparable growth profiles we observed under alternating light and dark cycles blue line in Fig.
Alternatively, C. vulgaris grew more slowly under pure autotrophic conditions red line in Fig. Glucose levels in the media were measured before we supplemented glucose. Nitrate consumption was also compared in the three different cultures Fig.
Subsequently, the GSMs i CZPA-T1 and i CZH-T1 were applied to estimate the nitrate required at the beginning of the light and dark periods for each cycle. This strategy resulted in no significant excess of nitrate remaining in the media at the end of both periods Fig.
The slower growth rate at the h light A conditions resulted in the lowest FA titer and biomass concentrations. Interestingly, given the combinatorial effect of high biomass accumulation from heterotrophic conditions and high lutein content from photoautotrophic light cycles, the highest lutein titer of 2.
Previous studies indeed found that light is an important factor to stimulate lutein production in microalgae Overall lutein, FA and biomass yields per gram of consumed glucose increased significantly between three and eightfold under alternating light and dark cycles as compared to heterotrophic conditions Fig.
During the light and dark cycling, C. In this way, our results showed that total biomass, lutein, and FA titers were all higher for light—dark cycling compared with autotrophic cultures Fig.
Previous omic 26 and fermentation studies 27 typically compared mixotrophic with autotrophic or heterotrophic conditions because they did not have the capability to control nutrient supply accurately under alternating light and dark cycles.
However, both metabolic pathways are important for C. Autotrophic metabolism provides algae with the capability to fix carbon dioxide and synthesize high amount of carotenoids such as lutein in order to protect the free radicals generated during photosynthesis.
Heterotrophic metabolism provides algae with the capacity to utilize organic carbon for efficient biomass and FA generation.
By taking advantages of both metabolic capacities, our nutrient control approach clearly exemplifies the benefits of genome-scale metabolic models to optimize nutrient supply for maximizing biomass and product yields under light and dark cycles.
After demonstrating the capability of using these genome-scale metabolic models to control nutrient supply against autotrophic and heterotrophic systems, we next compared the performance of cultures using genome-scale model process control GMPC with traditional fed-batch cultures for alternating photoautotrophic light and heterotrophic dark cycles.
Alternatively, an open-loop model control system relied on Matlab-based GSM predictions to control the nutrients required for growth without any inputs of measurements from the system.
This algorithm runs by setting the initial biomass concentration X 0 , initial nitrate concentration N 0 , and glucose concentration G 0 , which are three controlled variables in the system, along with fixed photoautotrophic and heterotrophic growth rates, which were based on previous test runs.
a Flowchart of standard fed-batch and open-loop GMPC cultures. c Growth rate comparison between GMPC Experiment and GMPC Prediction. d Glucose supply during the cultures. e Glucose level in the media. f Biomass yield on glucose. Measured growth of C.
vulgaris was significantly lower than model simulation, indicating an inconsistency between model predictions and experimental results during this time period. A growth rate comparison between GMPC prediction and experimental results for individual growth periods, including heterotrophic and autotrophic cycles, is shown in Fig.
The experimental growth rate of autotrophic cultures blue bars in Fig. Previous studies have observed that progressive increase in biomass will block light penetration and thus alter algal growth 28 , which may explain the gradual decline in the algal growth rate and resulting deviations away from model predictions, as this effect was not considered in our open-loop model predictive control.
Furthermore, the heterotrophic growth rate was around 0. As a result, C. vulgaris was likely consuming some glucose and CO 2 simultaneously during the light cycle, resulting in mixotrophic conditions for both the GMPC case and standard fed-batch cultures. Indeed, we observed declines in the glucose levels of C.
vulgaris during the light cycles in both cases white sections in Fig. This may explain the similar cell growth curves of C. vulgaris between standard fed-batch cultures and GMPC cultures Fig. However, even though C.
The extra glucose measured Fig. In addition, the performance of open-loop GMPC in terms of biomass yield on glucose was only moderately better than fed-batch cultures, likely in part because the glucose supply was not controlled appropriately under dark cycles for the GMPC conditions.
The measurements were then used as inputs into the model green box in Fig. Unlike the open-loop system in Fig. For the heterotrophic cycles, the calculated growth rate μ C , calculated glucose demand F G,C , and calculated nitrate demand on a per L basis F N,C during the 8-h period were determined based on measured inputs X m , G m , N m.
Next, the model and algorithm optimizer green and blue boxes in Fig. The algal cells were assumed to operate under two different types of metabolism in the simulations for the dark cycle.
One fraction of algal cells was assumed to grow strictly heterotrophically, as represented by model i CZH-T1.
In addition, a certain fraction a of algal biomass was assumed to grow mixotrophically and thus fixes CO 2 during the dark cycle as suggested in previous publications In this simulation, we therefore set the light intensity in the i CZPA-T1 model to a minimum for the current simulations in dark periods of the cycle.
As a result, three equations were added to consider this combined metabolic operation and its impact on growth rate, glucose consumption rate, and nitrate consumption rate.
The algorithm optimizes six variables including autotrophic growth rate μ A , autotrophic nitrate demand F NA , autotrophic biomass percentage a , heterotrophic growth rate μ H , heterotrophic glucose demand F HG , and heterotrophic nitrate demand F NG to minimize the difference between model simulations and experimental growth rate as well as glucose and nitrate demand for the most recent 8-h dark cycle.
Based on the predictions, the control pump supplies glucose and nitrate to the bioreactor. a Flowchart. b Model controller in heterotrophic dark cycles. c Model controller in autotrophic light cycles.
Alternatively, in the photoautotrophic phase, a differential equation for cell mass accumulation with respect to time was incorporated, which includes a term to describe the logarithmic decay of cell growth rate that occurs at increasing biomass concentrations due to light shading Fig.
This equation was built based on our experimental biomass measurements from a separate autotrophic culture run. The calculated growth rate was then used in i CZPA-T1 to predict and optimize nitrate supply during the light cycles. The GMPC culture was then compared with a standard fed-batch culture similar to the conditions used in the open-loop experiment Fig.
Unlike the open-loop system, algal growth for the GMPC culture blue line in Fig. Previous studies indicated the success of model predictive control is contingent on a robust process model and on-line measurements 29 , Indeed, in our closed-loop system, the model predictive algorithm was modified based on experimental measurements of cell density, glucose, and nitrate for both autotrophic and heterotrophic conditions in order to predict nutrient requirements for every cycle for the closed-loop system.
a Cell growth. b Growth rate comparison between GMPC Experiment and GMPC Prediction. c Glucose supply during the cultures.
d Glucose level in the media. e Biomass yield on glucose. The growth rates between simulation and experimental results were compared for individual time periods of the cycling photoautotrophic and heterotrophic cultures Fig.
Both the model predictions and experimental growth rates changed dynamically over different heterotrophic and autotrophic cycles.
The model predictions green bars in Fig. For the experiment, the growth rates blue bars in Fig. Meanwhile, the model predictions for growth during the light cycles gradually declined from 0.
The experimental growth rates followed the same trend, decreasing from 0. Due to the efficient glucose utilization occurring during the dark cycles of this closed-loop control system, the biomass yield on glucose increased dramatically by 2.
In contrast, the open-loop GMPC system only had a modest Overall, the closed-loop GMPC demonstrated more accurate controller performance than the open-loop GMPC system. To address this technical challenge, other more rapid nutrient and metabolite measurement tools could be integrated such as in situ Raman spectrometry for metabolite measurements 31 , Alternatively, off gas analysis can be used to characterize cell metabolism toward biomass accumulation or lipid synthesis 33 for future versions of GSM control.
After demonstrating the advantages of closed-loop model prediction and its associated higher efficiency of biomass productivity with respect to glucose fed, the model predictive controller was compared to a standard PID controller in silico and experimentally.
Using Simulink TM , a kinetic model consisting of four ODE equations was incorporated in order to describe changes in biomass, nutrient levels, and medium volume during the heterotrophic dark periods in a bioreactor Supplementary Fig.
Genome-scale metabolic models were then used to determine the relationship between growth rate, glucose uptake rate, and nitrate uptake rate as described previously Next a PID controller and an GMPC controller were used to control glucose and nitrate levels separately in the bioreactor Fig.
Both PID and GMPC controllers were simulated to control glucose supply and nitrate supply every hour. The simulated glucose and nitrate levels exhibited damped oscillations when using a PID controller, a common response for this controller type yellow lines in Fig.
In contrast, employing the GMPC controller eliminated the damping issues and enabled the glucose and nitrate level to more rapidly reach values near the target levels yellow lines in Fig. Meanwhile, the glucose supply and nitrate supply increased gradually in the GMPC controller red lines in Fig.
Instead, the amount added red lines in Fig. a Simulink model for model predictive control and PID control. Glucose control: b PID controller. c GMPC controller. Nitrate control: d PID controller. e GMPC controller. The PID controller gains were tuned on Matlab TM to achieve optimal performance with proportional gain K p , integral gain K i and derivative gain K d equal to 1.
The glucose levels were measured every hour and the data was fed to both the PID controller and the closed-loop GMPC controller with a pure heterotrophic model since light and dark cycles were not presented. The feedback signal could compensate for the modeling errors and also help to reject the disturbance in the GMPC controller.
After the setpoint change, the glucose level gradually decreased and was stably controlled. Overall, both Simulink TM simulations and experimental results demonstrated that the GMPC approach provided more robust and precise control than traditional PID controllers. While the model could anticipate the future behavior of the fermentation and take appropriate control action, the PID controller did not have this capability resulting in oscillations and overshoot behavior in both simulations and experiments.
Thus, our study demonstrates how GMPC systems can serve as a bridge between genome-scale metabolic modeling and control algorithms. Since the cultivation conditions can change and affect algal cellular metabolism, our system connected feedback measurements with genome-scale metabolic models and achieved more efficient nutrient utilization and higher product yields for dynamic algal cultivation conditions.
In this way, genome-scale metabolic models can be effectively utilized to improve biomanufacturing of microalgae and other industrially important microbial cell factories.
Fed-batch cultivation and PID controllers have been widely used in bioprocess development. Unfortunately, fed-batch cultivation often results in poor nutrient control and wasted nutrients and conventional PID control can lead to oscillating cell behaviors and poor performance under dynamic conditions.
In this study, we have utilized the power of genome-scale metabolic models to predict and control glucose and nitrate supply for C. vulgaris cultures under light and dark cycles and compared this approach to conventional autotrophic and heterotrophic processes.
Our results first showed that utilizing genome scale models to track and limit glucose and nitrate feeding led to higher titers of biomass, FAs, and lutein than autotrophic conditions and more efficient glucose utilization and higher product yields than heterotrophic conditions.
Next, implementing these models into an open loop system modestly improved performance. Finally, both computational simulations and experimental results demonstrated that this genome-based MPC system exhibits superior controller performance compared to conventional PID methods.
Green microalgae C. vulgaris UTEX was obtained from the Culture Collection of Algae at the University of Texas at Austin and maintained on sterile agar plates 1. Liquid cultures were inoculated with a single colony in For alternating light and dark cycles, autotrophic conditions were used for light sections and heterotrophic conditions were used for dark sections.
The lyophilized algal dry biomass was weighted gravimetrically using an analytical balance. The glucose concentration was measured using YSI biochemistry analyzer Yellow Springs, OH.
FAME production followed the procedure provided by Dong et al. Helium was used as carrier gas. Lutein extraction followed the procedure provided by Yuan et al. The solution was filtered before HPLC analysis.
The mobile phases are eluent A dichloromethane: methanol: acetonitrile: water, 5. The i CZ model, including six different biomass compositions for autotrophic conditions PAT1-PAT6 and five different biomass compositions for heterotrophic conditions HT1-HT5 , was obtained from Zuniga et al.
GSM simulations were performed using the Gurobi Optimizer Version 5. The experimental setup is shown in Supplementary Fig. The manipulated variables were glucose demand F G and nitrate demand on a per L basis F N for 8-h period. Two pumps were used to control both variables automatically by Matlab TM through Arduino chip.
All the control algorithms were run on Matlab TM and the codes are provided in Supplementary information. The Simulink TM simulation is shown in Fig. The blue box in Fig.
Four equations were built inside the blue box as shown in Supplementary Fig. The inputs were F G and F N. The outputs were biomass, nitrate level, glucose level and volume.
Only nitrate levels and glucose levels were fed into the PID and GMPC controller. For the proportional-integral-derivative PID controller, the proportional gain K p , integral gain K i and derivative gain K d equal to 1. The PID controller and GMPC controller were used to control glucose supply and nitrate supply every hour in both simulation and experiment.
Changes in the setpoint for glucose were introduced to see how both PID and GMPC responded to those changes. Initial biomass levels x 0 , glucose levels G 0 and nitrate levels N 0 were measured as described above and used as inputs into the open-loop system.
Three equations shown below were used to predict biomass growth, nitrate consumption rate, and glucose consumption rate in the open-loop system. The growth rates under light and dark cycles were determined based on previous experimental data.
After that, the growth rates were constrained in the autotrophic and heterotrophic GSMs, respectively to determine nutrient exchange rates r N and r G under light and dark cycles. The methods for using growth rate to estimate nutrient exchange rates have been described previously in Chen et al.
We assumed a rapid switch to a new operational steady state following the transition between light and dark cycles. Initial biomass levels x 0 , glucose levels G 0 and nitrate levels N 0 were measured and used as inputs into the closed-loop system. During the experiment, biomass levels x m , glucose levels G m and nitrate levels N m were msured and used as inputs into the closed-loop system.
For the light cycle, two equations were built to describe and predict biomass accumulation rate and nitrate consumption rate. Unlike the open loop system, the light shielding effect was considered and the growth rate would decrease as the biomass concentration increased as described in the equation below and shown in Fig.
The GSM was used to predict nutrient exchange rate r N based on the measured growth rate. For the dark cycles, three model equations were built to predict biomass accumulation rate, nitrate consumption rate and glucose consumption rate as listed below and shown in Fig. In the biomass equation, we assumed a fraction of heterotrophic biomass, a , was derived from autotrophic metabolism and the simulated growth rate was μ A.
Meanwhile, some biomass was derived through heterotrophic metabolism with the simulated growth rate, μ H. The nutrient exchange rates r NA , r NH , r GH were determined by inputting simulated growth rates into the autotrophic and heterotrophic GSMs respectively. where μ A is simulation growth rate from autotrophic metabolism, μ H is the growth rate from heterotrophic metabolism, r NA is nitrate exchange rate from autotrophic metabolism, r NH is the nitrate exchange rate from heterotrophic metabolism, r GH is the glucose exchange rate from heterotrophic metabolism.
Next, we applied a fitting objective function J to minimize the difference between calculated values and simulated model values in order to estimate the optimal parameter values a , μ A , μ H , r NA , r NH , r GH for dictating the actual nitrate and glucose feeds to the bioreactor.
The actual bolus nitrate demand F N and the glucose demand F G were thus determined by using values obtained from this fitting objective function. The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Metabolic modelling and simulation of the light and dark metabolism of Chlamydomonas reinhardtii. Plant J. Kato, Y. et al. Biofuels 12 , 39 Article PubMed PubMed Central Google Scholar. Cheirsilp, B.
How do sessile Optimizimg cope with irregularities in Nutritional periodization nutrient availability? The uptake Digestive health optimization essential minerals from the Channrls influences plant nytrient and development. However, most environments do not provide sufficient nutrients; rather nutrient distribution in the soil can be uneven and change temporally according to environmental factors. To maintain mineral nutrient homeostasis in their tissues, plants have evolved sophisticated systems for coping with spatial and temporal variability in soil nutrient concentrations. Among these are mechanisms for modulating root system architecture in response to nutrient availability.
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