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Improved nutrient utilization -

To reverse these trends, farmers should consider whether they need to invest in fertilizer before their yields and profits are harmed. Soil sampling is the key to deciding how much phosphorus and potassium to apply, and where.

Sampling identifies fields — and areas within fields — that have been depleted. But every year you have to test some of your acres. Brian Arnall, Associate Professor at Oklahoma State University who specializes in precision nutrient management. Guessing is expensive because fertilizer is one of the largest production costs for growers.

Corn growers, for example, can spend roughly a quarter of their budgets on fertilizer per year. Not applying enough fertilizer or skipping applications can be expensive and limit returns on what inputs are spent.

Avoid these issues with accurate soil data. Sample and analyze in each field every two, three or four years to match the crop rotation cycle.

Samples may be needed annually with extreme variances in nutrient levels. A critical level is the soil-test level above which response to added fertilizer would not be expected. Above this point, the recommended amount of a nutrient to be applied is zero in sufficiency fertilization approaches or crop removal in build-maintenance fertilization approaches.

Critical levels vary by nutrient and across geography. Agronomists and certified crop advisers encourage farmers to sample soils at the same time of year and at the same depth to establish a baseline of consistent results that can be compared year over year.

To establish phosphorus and potassium levels, for example, the sample depth is six to eight inches. The goal is to collect enough samples to create a map of each field. Historically, farmers have applied nutrients at the same rate across the field.

However, with more intensive soil sampling strategies in combination with yield maps, farmers can apply fertilizer where the need it most, increasing their ROI.

Whatever sampling method is used, collect multiple cores near each grid point or within each zone. Check out this article on looking at the best resolution. 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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Download references. This work was supported by the U. National Science Foundation EFRI program Grant number: and CBET program Grant number: and the Department of Energy Grant number: DE-SC Department of Chemical and Biomolecular Engineering, Johns Hopkins University, North Charles Street, Baltimore, MD, , USA.

Department of Pediatrics, University of California, San Diego, Gilman Drive, La Jolla, CA, , USA. Department of Biology, San Diego State University, San Diego, USA.

Department of Bioengineering, University of California, San Diego, Gilman Drive, La Jolla, CA, , USA. Center for Microbiome Innovation, University of California, San Diego, Gilman Drive, La Jolla, CA, , USA. You can also search for this author in PubMed Google Scholar.

contributed to conception and design of the experiment. conducted the experiments. analyzed the data. drafted the paper.

All authors read and approved the paper. Correspondence to Michael J. Open Access This article is licensed under a Creative Commons Attribution 4. Reprints and permissions. Li, CT. Optimization of nutrient utilization efficiency and productivity for algal cultures under light and dark cycles using genome-scale model process control.

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Abstract Algal cultivations are strongly influenced by light and dark cycles. Introduction Microalgae represent promising microorganisms for transforming renewable resources and inorganic carbon sources into biomass, biofuel precursors, and high-value products 1.

Results and discussion Advantages of using genome-scale model predictions on C. Full size image. a PID controller. b GMPC controller. Conclusions Fed-batch cultivation and PID controllers have been widely used in bioprocess development.

Methods Algal strain and cultivation conditions Green microalgae C. Summary of equations for 2L bioreactor cultures Open-loop system 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.

Data availability The data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability The codes that support the findings of this study are provided in supplementary information.

Broadacre nhtrient crops generally require Heart disease prevention relatively higher Imptoved input Dieting myths revealed yield targets. The Dieting myths revealed use of nutrients in arable farmlands is Improvrd vital to this endeavor. It minimizes Impdoved input Proper fueling for sports adverse soil and environmental implications that may arise from the incremental use of fertilizers. It is understood that Dieting myths revealed the natural capacity of the soil i. The adoption of integrated nutrient management INM approaches such as the organic amendment of the soil in addition to fertilizer use has shown positive impacts on maintaining and recovering soil quality, hence lowering excessive fertilizer use in farmlands. Therefore, this review contextualized the effect of compost and fertilizer on nutrient use efficiency NUE and productivity of broadacre crops. The use of compost as an organic soil amendment material has shown some inherently unique advantages and beneficial impacts on soil health and fertility such as improved soil structure, nutrient retention, mobilization, and bioavailability.