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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.

Rosenberg, J. A green light for engineered algae: redirecting metabolism to fuel a biotechnology revolution. Article CAS PubMed Google Scholar. Shene, C. 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. Enhanced growth and lipid production of microalgae under mixotrophic culture condition: effect of light intensity, glucose concentration and fed-batch cultivation.

Zheng, Y. High-density fed-batch culture of a thermotolerant microalga Chlorella sorokiniana for biofuel production.

Energy , — Article CAS Google Scholar. PhycoTerra ® can easily be mixed with a variety of liquid fertilizers, making it easy to include in your nutrient management programs. Figure 1: Add PhycoTerra ® soil microbial food to your liquid fertilizer program to help make your fertilizer investment go further.

PhycoTerra ® has a flexible application window and can be applied post-emergence. Apply the right source of nutrient, at the right time, and in the right place to optimize the efficiency of fertilizer use and improve NUE.

The goal of 4R nutrient stewardship is to match nutrient supply with crop requirements, minimize nutrient losses from fields, and maximize farmer profitability. Right Source — Understanding your options and utilizing the right source of nutrient — whether that is a synthetic fertilizer e.

Right Rate — Soil testing and understanding individual crop demand is key to building a nutrient program targeted field by field. Frequent soil testing also allows the grower to see the effect of land management changes in real time and adjust accordingly.

Right Time — Assess dynamics of crop uptake, soil supply, nutrient loss risks, and field operation logistics post emergence and side dress applications.

Avoid loss to erosion, surface runoff, and leaching carbon products can help support this as well. Right Place — Root-soil dynamics, nutrient movement active soil microbes and improved soil structure to limit potential nutrient losses. Figure 2: There are several N-loss pathways that we are trying to reduce by applying PhycoTerra ® and improving soil health and NUE.

Growers are constantly fighting against these N-loss pathways to maximize their fertilizer investment. Farm Progress: Pay Attention to Nitrogen Loss Pathways. The practices we have discussed — utilizing cover crops, feeding soil biology, reducing tillage — are what growers need to realize optimal nutrient management for their crops in a volatile fertilizer market and extreme drought conditions to maximize their potential yield.

They also happen to be key practices in nutrient stewardship, conservation, and regenerative agriculture. While fertilizer prices are optimistically expected to cycle down again long-term, the regulatory and social scrutiny on synthetic fertilizer will likely remain for some time due to its impact on climate change and water pollution.

On the other hand, the agricultural nitrogen cycle , with all its real-world challenges, is far from a closed loop. When nitrogen is lost, everyone loses — the plant, the Earth, and the grower.

Figure 3: Improving nutrient use efficiency NUE through 4R practices benefits the grower protects and maximizes fertilizer investment the consumer social benefit of reduced food insecurity and the planet environmental gains by reducing water and air pollution.

Nitrogen: A Complex Reality Clean Water Iowa. Synthetic fertilizers have supported maximized yields per acre and global population to boom — fertilizers are a critical part of the food supply chain.

Everything else falls into place once you focus on giving your body what it needs. Over the past four years, we have amassed , days of macronutrient and micronutrient data from 34, people who have used Nutrient Optimiser to fine-tune their nutrition.

To provide clarity in the conflicted world of nutrition and make a dent in the diabesity epidemic, we wanted to understand which quantifiable parameters of our food align with eating more or less than we need to.

Rather than becoming dependent on the latest magical supplement, our data analysis clearly shows that our cravings subside. We eat less once we get enough of all the essential nutrients from the food we eat. Optimisers who progressively dial in their diet at a macro and micronutrient level to align with their goals and preferences are empowered to break free of the shackles that make them slaves to their appetite!

Sadly, our modern food system relies heavily on subsidised industrial agriculture and is optimised to maximise profit— not nutrition or your health. To move beyond average, you need to take your health and nutrition into your own hands. Focussing on getting enough nutrients without excess calories enables you to cut through the noise and conflicts of interest to identify the foods and meals that are optimal for you.

We can still create a nutrient-dense or nutrient-poor version of those diets while strictly conforming to their templates. Our extensive analysis found that many recipes from some of the most famous named diets provided the worst nutrition!

Many of these popular diets start to tank once people start to create hyperpalatable recipes and commercialised products that enable us to pack more energy in with fewer nutrients, while still staying within the sanctioned rules of that diet.

Diets that work have one common factor that initially gets people hooked: they help you eliminate nutrient-poor, hyper-palatable processed foods that are typically a combination of refined carbs and fats with minimal nutrients.

But once you identify yourself with the diet and the community and culture that comes with the diet, it becomes hard to continue to progress when your goals, context or preferences change. After four years of analysing data from free-living people , we have developed an intimate understanding of how to engineer our food choices to manage our appetite and achieve our goals as we progress through our health journey.

Figure 1: Add PhycoTerra ® soil microbial food to your liquid fertilizer program to help make your fertilizer investment go further.

PhycoTerra ® has a flexible application window and can be applied post-emergence. Apply the right source of nutrient, at the right time, and in the right place to optimize the efficiency of fertilizer use and improve NUE.

The goal of 4R nutrient stewardship is to match nutrient supply with crop requirements, minimize nutrient losses from fields, and maximize farmer profitability. Right Source — Understanding your options and utilizing the right source of nutrient — whether that is a synthetic fertilizer e.

Right Rate — Soil testing and understanding individual crop demand is key to building a nutrient program targeted field by field. Frequent soil testing also allows the grower to see the effect of land management changes in real time and adjust accordingly.

Right Time — Assess dynamics of crop uptake, soil supply, nutrient loss risks, and field operation logistics post emergence and side dress applications.

Avoid loss to erosion, surface runoff, and leaching carbon products can help support this as well. Right Place — Root-soil dynamics, nutrient movement active soil microbes and improved soil structure to limit potential nutrient losses. Figure 2: There are several N-loss pathways that we are trying to reduce by applying PhycoTerra ® and improving soil health and NUE.

Growers are constantly fighting against these N-loss pathways to maximize their fertilizer investment. Farm Progress: Pay Attention to Nitrogen Loss Pathways.

The practices we have discussed — utilizing cover crops, feeding soil biology, reducing tillage — are what growers need to realize optimal nutrient management for their crops in a volatile fertilizer market and extreme drought conditions to maximize their potential yield.

They also happen to be key practices in nutrient stewardship, conservation, and regenerative agriculture. While fertilizer prices are optimistically expected to cycle down again long-term, the regulatory and social scrutiny on synthetic fertilizer will likely remain for some time due to its impact on climate change and water pollution.

On the other hand, the agricultural nitrogen cycle , with all its real-world challenges, is far from a closed loop. When nitrogen is lost, everyone loses — the plant, the Earth, and the grower.

Figure 3: Improving nutrient use efficiency NUE through 4R practices benefits the grower protects and maximizes fertilizer investment the consumer social benefit of reduced food insecurity and the planet environmental gains by reducing water and air pollution.

Nitrogen: A Complex Reality Clean Water Iowa. Synthetic fertilizers have supported maximized yields per acre and global population to boom — fertilizers are a critical part of the food supply chain. The challenge the fertilizer frenzy of this growing season is bringing into focus is: how do we maximize NUE and create long-term, sustainable, and resilient agricultural practices and technologies that benefit growers, consumers, and the planet?

Trait convergence and plasticity among native and invasive species in resource-poor environments. Du, Y. A synthetic analysis of the effect of water and nitrogen inputs on wheat yield and water- and nitrogen-use efficiencies in China.

Field Crops Res. Duan, Z. Combined effect of nitrogen—phosphorus—potassium fertilizers and water on spring wheat yield in an arid desert region. Soil Sci. Dumas, Y. Effects of environmental factors and agricultural techniques on antioxidantcontent of tomatoes. Food Agric. Fernandez, J.

Late-season nitrogen fertilization on maize yield: a meta-analysis. Garnett, T. Root based approaches to improving nitrogen use efficiency in plants. Plant Cell Environ.

Gong, P. China must reduce fertilizer use too. Nature , — Guichard, S. Tomato fruit quality in relation to water and carbon fluxes. Han, W. Optimizing drip fertigation management based on yield, quality, water and fertilizer use efficiency of wine grape in North China.

He, X. Analysis of effect of nitrogen application on yield and quality of eggplant. Jalil Sheshbahreh, M. Effect of irrigation regimes and nitrogen sources on biomass production, water and nitrogen use efficiency and nutrients uptake in coneflower EChinacea purpurea L. Jiang, S. Root extension and nitrate transporter up-regulation induced by nitrogen deficiency improves nitrogen status and plant growth at the seedling stage of winter wheat Triticum aestivum L.

Ju, X. Nitrogen balance and groundwater nitrate contamination: comparison among three intensive cropping systems on the north China plain. Ju, Y. Anthocyanin accumulation and biosynthesis are modulated by regulated deficit irrigation in Cabernet Sauvignon Vitis vinifera L.

grapes and wines. Plant Physiol. Kamran, M. Interactive effects of reduced irrigation and nitrogen fertilization on resource use efficiency, forage nutritive quality, yield, and economic benefits of spring wheat in the arid region of Northwest China. Karimi, A. Bioactive compounds from by-products of eggplant: Functional properties, potential applications and advances in valorization methods.

Food Sci. Kunrath, T. Water use efficiency in perennial forage species: interactions between nitrogen nutrition and water deficit. Lahoz, I. Effect of water deficit on the agronomical performance and quality of processing tomato. Lambers, H.

Plant Physiological Ecology Berlin: Springer. Google Scholar. Lang, C. Different nitrogen N forms affect responses to N form and N supply of rootstocks and grafted grapevines.

Plant Sci. Lee, S. Preharvest and postharvest factors influencing vitamin C content of horticultural crops. Postharvest Biol. Li, Y. Effects of irrigation and fertilization on grain yield, water and nitrogen dynamics and their use efficiency of spring wheat farmland in an arid agricultural watershed of Northwest China.

Li, H. Optimizing irrigation and nitrogen management strategy to trade off yield, crop water productivity, nitrogen use efficiency and fruit quality of greenhouse grown tomato.

Effects of two slow-release nitrogen fertilizers and irrigation on yield, quality, and water-fertilizer productivity of greenhouse tomato. Liu, H. Optimizing irrigation frequency and amount to balance yield, fruit quality and water use efficiency of greenhouse tomato.

Lu, J. Response of yield, yield components and water-nitrogen use efficiency of winter wheat to different drip fertigation regimes in Northwest China. Luo, H. Tomato yield, quality and water use efficiency under different drip fertigation strategies. Matteau, J. Effects of irrigation thresholds and temporal distribution on potato yield and water productivity in sandy soil.

Meng, X. Temporal and spatial changes of temperature and precipitation in Hexi Corridor during — Miao, Q. Effects of water and fertilizer reduction on eggplant yield,Quality and incidence of verticillium wilt in continuous cropping. Mueller, N. Closing yield gaps through nutrient and water management.

Mwinuka, P. Optimizing water and nitrogen application for neglected horticultural species in tropical sub-humid climate areas: a case of african eggplant solanum aethiopicum L.

Nasar, J. Nitrogen fertilization coupled with iron foliar application improves the photosynthetic characteristics, photosynthetic nitrogen use efficiency, and the related enzymes of maize crops under different planting patterns. Nisha, P. A comparative study on antioxidant activities of different varieties of Solanum melongena.

Olak, Y. Yield and quality response of surface and subsurface drip-irrigated eggplant and comparison of net returns. Pereira, L. Improved indicators of water use performance and productivity for sustainable water conservation and saving. Reddy, P. Sustainable Intensification of Crop Production Singapore: Springer Nature.

Rosales, M. The effect of environmental conditions on nutritional quality of cherry tomato fruits: evaluation of two experimental Mediterranean greenhouses. Rostamza, M. Forage quality, water use and nitrogen utilization efficiencies of pearl millet Pennisetum americanum L. grown under different soil moisture and nitrogen levels.

Si, Z. Effects of nitrogen application rate and irrigation regime on growth, yield, and water-nitrogen use efficiency of drip-irrigated winter wheat in the North China Plain. Spiertz, J. Nitrogen, sustainable agriculture and food security.

A review. Sun, S. Effects of different nitrogen fertilizertion levels on quality of tomato cultivated in soalr greenhouse. Sun, Y. Tan, Y.

Effects of optimized N fertilization on greenhouse gas emission and crop production in the North China Plain. Teixeira, E. The impact of water and nitrogen limitation on maize biomass and resource-use efficiencies for radiation, water and nitrogen.

Toppino, L. A new intra-specific and high-resolution genetic map of eggplant based on a RIL population, and location of QTLs related to plant anthocyanin pigmentation and seed vigour.

Genes 11, Urioste, A. Cultivation effects on the distribution of organic carbon, total nitrogen and phosphorus in soils of the semiarid region of Argentinian Pampas. Geoderma 3 , — Wang, Y. Effect of irrigation regimes and nitrogen rates on water use efficiency and nitrogen uptake in maize. Wang, F.

Determination of comprehensive quality index for tomato and its response to different irrigation treatments. Comparative effects of deficit irrigation and alternate partial root-zone irrigation on xylem pH, ABA and ionic concentrations in tomatoes.

Wang, X. Nitrogen fertilizer regulated grain storage protein synthesis and reduced chalkiness of rice under actual field warming. Wang, Z. Grain yield, water and nitrogen use efficiencies of rice as influenced by irrigation regimes and their interaction with nitrogen rates.

Waraich, E. Role of mineral nutrition in alleviation of drought stress in plants. Crop Sci. Wei, Y. Policy incentives for reducing nitrate leaching from intensive agriculture in desert oases of Alxa, Inner Mongolia, China. Wu, Y. Responses of growth, fruit yield, quality and water productivity of greenhouse tomato to deficit drip irrigation.

Xiao, C. Effects of various soil water potential thresholds for drip irrigation on soil salinity, seed cotton yield and water productivity of cotton in northwest China. Yan, H. Parametrization of aerodynamic and canopy resistances for modeling evapotranspiration of greenhouse cucumber.

Yang, H. A comprehensive method of evaluating the impact of drought and salt stress on tomato growth and fruit quality based on EPIC growth model. Yang, X.

Yang, B. Effect of regulated deficit irrigation on the content of soluble sugars, organic acids and endogenous hormones in Cabernet Sauvignon in the Ningxia region of China.

Zhang, S. Optimizing irrigation amount and potassium rate to simultaneously improve tuber yield, water productivity and plant potassium accumulation of drip-fertigated potato in northwest China.

Zhang, B. Yield performance of spring wheat improved by regulated deficit irrigation in an arid area. Zhang, T. Processing tomato nitrogen utilization and soil residual nitrogen as influenced by nitrogen and phosphorus additions with drip-fertigation.

Soc Am. Zhong, Y. Effect of deficit irrigation on soil CO 2 and N 2 O emissions and winter wheat yield. Zhou, X. Extensive transcriptome changes underlying the fruit skin colour intensity variation in purple eggplant.

Keywords: eggplant, water and nitrogen management, yield, quality, water and nitrogen use efficiency. Citation: Zhou C, Zhang H, Yu S, Chen X, Li F, Wang Y, Wang Y and Liu L Optimizing water and nitrogen management strategies to improve their use efficiency, eggplant yield and fruit quality.

Received: 24 April ; Accepted: 23 August ; Published: 11 September Copyright © Zhou, Zhang, Yu, Chen, Li, Wang, Wang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License CC BY.

Optimisers who progressively dial in their diet at a macro and micronutrient level to align with their goals and preferences are empowered to break free of the shackles that make them slaves to their appetite!

Sadly, our modern food system relies heavily on subsidised industrial agriculture and is optimised to maximise profit— not nutrition or your health. To move beyond average, you need to take your health and nutrition into your own hands. Focussing on getting enough nutrients without excess calories enables you to cut through the noise and conflicts of interest to identify the foods and meals that are optimal for you.

We can still create a nutrient-dense or nutrient-poor version of those diets while strictly conforming to their templates.

Our extensive analysis found that many recipes from some of the most famous named diets provided the worst nutrition! Many of these popular diets start to tank once people start to create hyperpalatable recipes and commercialised products that enable us to pack more energy in with fewer nutrients, while still staying within the sanctioned rules of that diet.

Diets that work have one common factor that initially gets people hooked: they help you eliminate nutrient-poor, hyper-palatable processed foods that are typically a combination of refined carbs and fats with minimal nutrients. But once you identify yourself with the diet and the community and culture that comes with the diet, it becomes hard to continue to progress when your goals, context or preferences change.

After four years of analysing data from free-living people , we have developed an intimate understanding of how to engineer our food choices to manage our appetite and achieve our goals as we progress through our health journey. By progressively adding foods and meals containing more of your priority nutrients , you can quickly achieve a balanced diet at a micronutrient level.

But as well as micronutrients i. vitamins , minerals , amino acids , and fatty acids like omega-3s required for bodily function , you can also optimise your macronutrients to empower you eat more or less depending on your goals, by dialling in your macronutrients.

Our analysis has shown repeatedly that the most crucial factor in satiety , or your tendency to feel full with fewer calories, is the balance of energy between protein and fibre vs energy from fat and carbs. For more details, see Secrets of the Nutrient-Dense Protein Sparing Modified Fast PSMF Diet.

In the case of our biological inoculants, this relationship is extra efficient, because the fungus activates key metabolic processes such as photosynthesis, consequently taking more sugars from the plant and providing the plant with more water and nutrients in return.

This effective increase in photosynthetic capacity allows the crop to capture more CO 2. On the other hand, the fungus also encourages root system development in the crop, including the number of absorbent hairs. This way, our fungus can grow and form more connections with the plant for even greater absorption of water and nutrients.

Glomus iranicum var. tenuihypharum is not just any mycorrhizal fungus. Its connection with the plant is extraordinary thanks to its exclusive characteristics.

It produces small spores external to the root. Remember those absorbent hairs we told you about? The crop also tolerates two times more salinity levels and higher fertilizer concentrations.

But, concerning nutrient use efficiency NUE , we are interested in a particular feature of our Glomus iranicum var. tenuihypharum -containing biological inoculants: the abundant production of extramatrical mycelium. The fungus grows outward from the root deploying a network of hyphae capable of absorbing water and nutrients and transporting them to the arbuscules, the place where it exchanges water and nutrients for sugars.

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