Optimal Control for Indoor Vertical Farms Based on Crop Growth

Vertical farming allows for year-round cultivation of a variety of crops, overcoming environmental limitations and ensuring food security. This closed and highly controlled system allows the plants to grow in optimal conditions, so that they reach maturity faster and yield more than on a conventional outdoor farm. However, one of the challenges of vertical farming is the high energy consumption. In this work, we optimize wheat growth using an optimal control approach with two objectives: first, we optimize inputs such as water, radiation, and temperature for each day of the growth cycle, and second, we optimize the duration of the plant's growth period to achieve the highest possible yield over a whole year. For this, we use a nonlinear, discrete-time hybrid model based on a simple universal crop model that we adapt to make the optimization more efficient. Using our approach, we find an optimal trade-off between used resources, net profit of the yield, and duration of a cropping period, thus increasing the annual yield of crops significantly while keeping input costs as low as possible. This work demonstrates the high potential of control theory in the discipline of vertical farming.Comment: This work has been accepted for presentation at IFAC World Congress 202

Climate service driven adaptation may alleviate the impacts of climate change in agriculture

Building a resilient and sustainable agricultural sector requires the development and implementation of tailored climate change adaptation strategies. By focusing on durum wheat (Triticum turgidum subsp. durum) in the Euro-Mediterranean region, we estimate the benefits of adapting through seasonal cultivar-selection supported by an idealised agro-climate service based on seasonal climate forecasts. The cost of inaction in terms of mean yield losses, in 2021–2040, ranges from −7.8% to −5.8% associated with a 7% to 12% increase in interannual variability. Supporting cultivar choices at local scale may alleviate these impacts and even turn them into gains, from 0.4% to 5.3%, as soon as the performance of the agro-climate service increases. However, adaptation advantages on mean yield may come with doubling the estimated increase in the interannual yield variability.info:eu-repo/semantics/publishedVersio

Global crop yields can be lifted by timely adaptation of growing periods to climate change

Adaptive management of crop growing periods by adjusting sowing dates and cultivars is one of the central aspects of crop production systems, tightly connected to local climate. However, it is so far underrepresented in crop-model based assessments of yields under climate change. In this study, we integrate models of farmers’ decision making with biophysical crop modeling at the global scale to simulate crop calendars adaptation and its effect on crop yields of maize, rice, sorghum, soybean and wheat. We simulate crop growing periods and yields (1986-2099) under counterfactual management scenarios assuming no adaptation, timely adaptation or delayed adaptation of sowing dates and cultivars. We then compare the counterfactual growing periods and corresponding yields at the end of the century (2080-2099). We find that (i) with adaptation, temperature-driven sowing dates (typical at latitudes >30°N-S) will have larger shifts than precipitation-driven sowing dates (at latitudes <30°N-S); (ii) later-maturing cultivars will be needed, particularly at higher latitudes; (iii) timely adaptation of growing periods would increase actual crop yields by ~12%, reducing climate change negative impacts and enhancing the positive CO2 fertilization effect. Despite remaining uncertainties, crop growing periods adaptation require consideration in climate change impact assessments

Global crop yields can be lifted by timely adaptation of growing periods to climate change

Adaptive management of crop growing periods by adjusting sowing dates and cultivars is one of the central aspects of crop production systems, tightly connected to local climate. However, it is so far underrepresented in crop-model based assessments of yields under climate change. In this study, we integrate models of farmers’ decision making with biophysical crop modeling at the global scale to simulate crop calendars adaptation and its effect on crop yields of maize, rice, sorghum, soybean and wheat. We simulate crop growing periods and yields (1986-2099) under counterfactual management scenarios assuming no adaptation, timely adaptation or delayed adaptation of sowing dates and cultivars. We then compare the counterfactual growing periods and corresponding yields at the end of the century (2080-2099). We find that (i) with adaptation, temperature-driven sowing dates (typical at latitudes >30°N-S) will have larger shifts than precipitation-driven sowing dates (at latitudes <30°N-S); (ii) later-maturing cultivars will be needed, particularly at higher latitudes; (iii) timely adaptation of growing periods would increase actual crop yields by ~12%, reducing climate change negative impacts and enhancing the positive CO2 fertilization effect. Despite remaining uncertainties, crop growing periods adaptation require consideration in climate change impact assessments

Influences of increasing temperature on Indian wheat: quantifying limits to predictability

As climate changes, temperatures will play an increasing role in determining crop yield. Both climate model error and lack of constrained physiological thresholds limit the predictability of yield. We used a perturbed-parameter climate model ensemble with two methods of bias-correction as input to a regional-scale wheat simulation model over India to examine future yields. This model configuration accounted for uncertainty in climate, planting date, optimization, temperature-induced changes in development rate and reproduction. It also accounts for lethal temperatures, which have been somewhat neglected to date. Using uncertainty decomposition, we found that fractional uncertainty due to temperature-driven processes in the crop model was on average larger than climate model uncertainty (0.56 versus 0.44), and that the crop model uncertainty is dominated by crop development. Simulations with the raw compared to the bias-corrected climate data did not agree on the impact on future wheat yield, nor its geographical distribution. However the method of bias-correction was not an important source of uncertainty. We conclude that bias-correction of climate model data and improved constraints on especially crop development are critical for robust impact predictions

Impact of Climate Change on Corn Yields in Alabama

Abstract The study used calibrated Crop Environment Resource Synthesis (CERES) maize (corn) model to simulate maize (corn) physiological growth processes and yields under 2045 and 2075 projected climate change scenarios for six representative counties in Alabama. The future climatologies for two emission scenarios Representative Concentration Pathway (RCP) 4.5 (medium) and RCP 8.5 (high) were developed based on the IPSL-CM5A-MR high resolution climate model. Average yield decreases of 19.5% and 37.3% were, respectively, projected under RCP 4.5 and RCP 8.5 for 2045, and average yield decreases of 32.5% and 77.8% were, respectively, projected under RCP 4.5 and RCP 8.5 for 2075. These yield decreases were largely influenced by increasing temperatures as evidenced by the shortening of various development stages such as anthesis and maturity, which are important determinants of the final grain yield and number. Corn production in Autauga County was projected to be highly vulnerable to climate change, while production in Limestone County was least vulnerable. Corn crops in Alabama appear to be sensitive to climate change and will require adaptation strategies. Keywords: Climate Change, CERES-Maize model, General Circulation Model (GCM), Representative Concentration Pathway (RCP) emission scenarios

The Agricultural Model Intercomparison and Improvement Project (AgMIP) Town Hall

AgMIP (www.agmip.org) is an international community of climate, crop, livestock, economics, and IT experts working to further the development and application of multi-model, multi-scale, multi-disciplinary agricultural models that can inform policy and decision makers around the world. This meeting will engage the AGU community by providing a brief overview of AgMIP, in particular its new plans for a Coordinated Global and Regional Assessment of climate change impacts on agriculture and food security for AR6. This Town Hall will help identify opportunities for participants to become involved in AgMIP and its 30+ activities

Climate impact and adaptation to heat and drought stress of regional and global wheat production

Wheat (Triticum aestivum) is the most widely grown food crop in the world threatened by future climate change. In this study, we simulated climate change impacts and adaptation strategies for wheat globally using new crop genetic traits (CGT), including increased heat tolerance, early vigor to increase early crop water use, late flowering to reverse an earlier anthesis in warmer conditions, and the combined traits with additional nitrogen (N) fertilizer applications, as an option to maximize genetic gains. These simulations were completed using three wheat crop models and five Global Climate Models (GCM) for RCP 8.5 at mid-century. Crop simulations were compared with country, US state, and US county grain yield and production. Wheat yield and production from high-yielding and low-yielding countries were mostly captured by the model ensemble mean. However, US state and county yields and production were often poorly reproduced, with large variability in the models, which is likely due to poor soil and crop management input data at this scale. Climate change is projected to decrease global wheat production by −1.9% by mid-century. However, the most negative impacts are projected to affect developing countries in tropical regions. The model ensemble mean suggests large negative yield impacts for African and Southern Asian countries where food security is already a problem. Yields are predicted to decline by −15% in African countries and −16% in Southern Asian countries by 2050. Introducing CGT as an adaptation to climate change improved wheat yield in many regions, but due to poor nutrient management, many developing countries only benefited from adaptation from CGT when combined with additional N fertilizer. As growing conditions and the impact from climate change on wheat vary across the globe, region-specific adaptation strategies need to be explored to increase the possible benefits of adaptations to climate change in the future.info:eu-repo/semantics/publishedVersio

An AgMIP framework for improved agricultural representation in integrated assessment models

Integrated assessment models (IAMs) hold great potential to assess how future agricultural systems will be shaped by socioeconomic development, technological innovation, and changing climate conditions. By coupling with climate and crop model emulators, IAMs have the potential to resolve important agricultural feedback loops and identify unintended consequences of socioeconomic development for agricultural systems. Here we propose a framework to develop robust representation of agricultural system responses within IAMs, linking downstream applications with model development and the coordinated evaluation of key climate responses from local to global scales. We survey the strengths and weaknesses of protocol-based assessments linked to the Agricultural Model Intercomparison and Improvement Project (AgMIP), each utilizing multiple sites and models to evaluate crop response to core climate changes including shifts in carbon dioxide concentration, temperature, and water availability, with some studies further exploring how climate responses are affected by nitrogen levels and adaptation in farm systems. Site-based studies with carefully calibrated models encompass the largest number of activities; however they are limited in their ability to capture the full range of global agricultural system diversity. Representative site networks provide more targeted response information than broadly-sampled networks, with limitations stemming from difficulties in covering the diversity of farming systems. Global gridded crop models provide comprehensive coverage, although with large challenges for calibration and quality control of inputs. Diversity in climate responses underscores that crop model emulators must distinguish between regions and farming system while recognizing model uncertainty. Finally, to bridge the gap between bottom-up and top-down approaches we recommend the deployment of a hybrid climate response system employing a representative network of sites to bias-correct comprehensive gridded simulations, opening the door to accelerated development and a broad range of applications