Modelling cereal root systems for water and nitrogen capture: towards an economic optimum.
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ABSTRACT:
SUBMITTER: King J
PROVIDER: S-EPMC4244970 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time (R2 = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity (R2 = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined (n = 31) with an average error range of ±38 kg N ha-1 (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3% lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost.
| S-EPMC5909184 | biostudies-literature
Project description:The demand for nitrogen (N) for crop production increased rapidly from the middle of the twentieth century and is predicted to at least double by 2050 to satisfy the on-going improvements in productivity of major food crops such as wheat, rice and maize that underpin the staple diet of most of the world's population. The increased demand will need to be fulfilled by the two main sources of N supply - biological nitrogen (gas) (N2) fixation (BNF) and fertilizer N supplied through the Haber-Bosch processes. BNF provides many functional benefits for agroecosystems. It is a vital mechanism for replenishing the reservoirs of soil organic N and improving the availability of soil N to support crop growth while also assisting in efforts to lower negative environmental externalities than fertilizer N. In cereal-based cropping systems, legumes in symbiosis with rhizobia contribute the largest BNF input; however, diazotrophs involved in non-symbiotic associations with plants or present as free-living N2-fixers are ubiquitous and also provide an additional source of fixed N. This review presents the current knowledge of BNF by free-living, non-symbiotic and symbiotic diazotrophs in the global N cycle, examines global and regional estimates of contributions of BNF, and discusses possible strategies to enhance BNF for the prospective benefit of cereal N nutrition. We conclude by considering the challenges of introducing in planta BNF into cereals and reflect on the potential for BNF in both conventional and alternative crop management systems to encourage the ecological intensification of cereal and legume production.
| S-EPMC9133800 | biostudies-literature
Project description:Root phenotyping provides trait information for plant breeding. A shortcoming of high-throughput root phenotyping is the limitation to seedling plants and failure to make inferences on mature root systems. We suggest root system architecture (RSA) models to predict mature root traits and overcome the inference problem. Sixteen pea genotypes were phenotyped in (i) seedling (Petri dishes) and (ii) mature (sand-filled columns) root phenotyping platforms. The RSA model RootBox was parameterized with seedling traits to simulate the fully developed root systems. Measured and modelled root length, first-order lateral number, and root distribution were compared to determine key traits for model-based prediction. No direct relationship in root traits (tap, lateral length, interbranch distance) was evident between phenotyping systems. RootBox significantly improved the inference over phenotyping platforms. Seedling plant tap and lateral root elongation rates and interbranch distance were sufficient model parameters to predict genotype ranking in total root length with an RSpearman of 0.83. Parameterization including uneven lateral spacing via a scaling function substantially improved the prediction of architectures underlying the differently sized root systems. We conclude that RSA models can solve the inference problem of seedling root phenotyping. RSA models should be included in the phenotyping pipeline to provide reliable information on mature root systems to breeding research.
| S-EPMC5441853 | biostudies-literature
Project description:Background and Aims:Root architecture development determines the sites in soil where roots provide input of carbon and take up water and solutes. However, root architecture is difficult to determine experimentally when grown in opaque soil. Thus, root architecture models have been widely used and been further developed into functional-structural models that simulate the fate of water and solutes in the soil-root system. The root architecture model CRootBox presented here is a flexible framework to model root architecture and its interactions with static and dynamic soil environments. Methods:CRootBox is a C++-based root architecture model with Python binding, so that CRootBox can be included via a shared library into any Python code. Output formats include VTP, DGF, RSML and a plain text file containing coordinates of root nodes. Furthermore, a database of published root architecture parameters was created. The capabilities of CRootBox for the unconfined growth of single root systems, as well as the different parameter sets, are highlighted in a freely available web application. Key results:The capabilities of CRootBox are demonstrated through five different cases: (1) free growth of individual root systems; (2) growth of root systems in containers as a way to mimic experimental setups; (3) field-scale simulation; (4) root growth as affected by heterogeneous, static soil conditions; and (5) coupling CRootBox with code from the book Soil physics with Python to dynamically compute water flow in soil, root water uptake and water flow inside roots. Conclusions:CRootBox is a fast and flexible functional-structural root model that is based on state-of-the-art computational science methods. Its aim is to facilitate modelling of root responses to environmental conditions as well as the impact of roots on soil. In the future, this approach will be extended to the above-ground part of the plant.
| S-EPMC5906965 | biostudies-literature
Project description:BACKGROUND AND AIMS:It is well established that the nitrogen content of plants, including lettuce, decreases with time. It has also been observed that water content of lettuce increases between planting and harvest. This paper is an attempt at modelling these observations. METHODS:An existing dynamic model (Nicolet), designed to predict growth and nitrate content of glasshouse lettuce, is modified to accommodate the ontogenetic changes of reduced-nitrogen and water contents (on a dry matter basis). The decreasing reduced-N content and the increasing water content are mimicked by dividing the originally uniform plant into 'metabolically active' tissue and 'support' tissue. The 'metabolic' tissue is assumed to contain a higher nitrogen content and a lower water content than the 'support' tissue. As the plants grow, the ratio of 'support' to 'metabolic' tissue increases, resulting in an increased mean water content and a decreased reduced-N content. Simulations with the new model are compared with experimental glasshouse data over four seasons. KEY RESULTS:The empirical linear relationship between water and reduced-N contents, matches, to a good approximation, the corresponding relationship based on the model. The agreement between the two makes it possible to effectively uncouple the estimation of the 'ontogenetic' parameters from the estimation of the other parameters. The growth and nitrate simulation results match the data rather well and are hardly affected by the new refinement. The reduced-N and water contents are predicted much better with the new model. CONCLUSION:Prediction of nitrogen uptake for the substantial nitrate pool of lettuce depends on the water content. Hence, the modified model may assist in making better fertilization decisions and better estimates of nitrogen leaching.
| S-EPMC4242180 | biostudies-other
Project description:Introduction The application of controlled-release nitrogen fertilizer (CRN) has become an important production method to achieve high crop yield and ecological safety. However, the rate of urea-blended CRN for rice is usually determined by conventional urea, and the actual rate is still unclear. Methods A five-year field experiment was carried out in the Chaohu watershed in the Yangtze River Delta to study rice yield, N fertilizer utilization efficiency (NUE), ammonia (NH3) volatilization and economic benefit under the four urea-blended CRN treatments with a 4:3:3 ratio applied at one time (60, 120, 180, 240 kg/hm2, CRN60, CRN120, CRN180, CRN240), four conventional N fertilizer treatments (N60, N120, N180, N240) and a control without N fertilizer (N0). Results and Discussion The results showed that the N released from the blended CRNs could well satisfy the N demand of rice growth. Similar to the conventional N fertilizer treatments, a quadratic equation was used to model the relationship between rice yield and N rate under the blended CRN treatments. The blended CRN treatments increased rice yield by 0.9-8.2% and NUE by 6.9-14.8%, respectively, compared with the conventional N fertilizer treatments at the same N application rate. The increase in NUE in response to applied blended CRN was related to the reduction in NH3 volatilization. Based on the quadratic equation, the five-year average NUE under the blended CRN treatment was 42.0% when rice yield reached the maximum, which was 28.9% higher than that under the conventional N fertilizer treatment. Among all treatments, CRN180 had the highest yield and net benefit in 2019. Considering the yield output, environmental loss, labor and fertilizer costs, the optimum economic N rate under the blended CRN treatment in the Chaohu watershed was 180-214 kg/hm2, compared with 212-278 kg/hm2 under the conventional N fertilizer treatment. The findings suggest that blended CRN improved rice yield, NUE and economic income while decreasing NH3 volatilization and negative environmental outcomes.
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Project description:The quality of the drinking water distributed through the networks has become the main concern of most operators. This work focuses on one of the most important variables of the drinking water distribution networks (WDN) that use disinfection, chlorine. This powerful disinfectant must be dosed carefully in order to reduce disinfection byproducts (DBPs). The literature demonstrates researchers' interest in modelling chlorine decay and using several different approaches. Nevertheless, the full-scale application of these models is far from being a reality in the supervision of water distribution networks. This paper combines the use of validated chlorine prediction models with an intensive study of a large amount of data and its influence on the model's parameters. These parameters are estimated and validated using data coming from the Supervisory Control and Data Acquisition (SCADA) software, a full-scale water distribution system, and using off-line analytics. The result is a powerful methodology for calibrating a chlorine decay model on-line which coherently evolves over time along with the significant variables that influence it.
| S-EPMC9331035 | biostudies-literature
Project description:Intensive livestock farming generates vast amounts of organic materials, which are an important source of nitrogen releases. These anthropogenic nitrogen releases contribute to multiple environmental problems, including eutrophication of water systems, contamination of drinking water sources, and greenhouse gas emissions. Nitrogen recovery and recycling are technically feasible, and there exists a number of processes for nitrogen recovery from livestock material in the form of different products. In this work, a multi-scale techno-economic assessment of techniques for nitrogen recovery and recycling is performed. The assessment includes a material flow analysis of each process, from material collection to final treatment, to determine nitrogen recovery efficiency, losses, and recovery cost, as well as an environmental cost-benefit analysis to compare the nitrogen recovery cost versus the economic losses derived from its uncontrolled release into the environment. The results show that transmembrane chemisorption process results in the lowest recovery cost, 3.4-10.4 USD per kilogram of nitrogen recovered in the range of studied processing scales. The recovery of nitrogen from livestock material through three technologies, i.e., transmembrane chemisorption, MAPHEX, and stripping in packed bed, reveales to be cost-effective. Since the economic losses due to the harmful effects of nitrogen into the environment are estimated at 32-35 USD per kilogram of nitrogen released, nitrogen recycling is an environmentally and economically beneficial approach to reduce nutrient pollution caused by livestock operations.
| S-EPMC10659086 | biostudies-literature
Project description:AbstractBackground and aimsRoot hairs play a significant role in phosphorus (P) extraction at the pore scale. However, their importance at the field scale remains poorly understood.MethodsThis study uses a continuum model to explore the impact of root hairs on the large-scale uptake of P, comparing root hair influence under different agricultural scenarios. High vs low and constant vs decaying P concentrations down the soil profile are considered, along with early vs late precipitation scenarios.ResultsSimulation results suggest root hairs accounted for 50% of total P uptake by plants. Furthermore, a delayed initiation time of precipitation potentially limits the P uptake rate by over 50% depending on the growth period. Despite the large differences in the uptake rate, changes in the soil P concentration in the domain due to root solute uptake remains marginal when considering a single growth season. However, over the duration of 6 years, simulation results showed that noticeable differences arise over time.ConclusionRoot hairs are critical to P capture, with uptake efficiency potentially enhanced by coordinating irrigation with P application during earlier growth stages of crops.
| S-EPMC7062663 | biostudies-literature
Project description:Cereal aphids continue to be an important agricultural pest, with complex lifecycle and dispersal behaviours. Spatially-explicit models that are able to simulate flight initiation, movement direction, distance and timing of arrival of key aphid species can be highly valuable to area-wide pest management programmes. Here I present an overview of how knowledge about cereal aphid flight and migration can be utilized by mechanistic simulation models. This article identifies specific gaps in knowledge for researchers who may wish to further scientific understanding of aphid flight behaviour, whilst at the same time provides a synopsis of the knowledge requirements for a mechanistic approach applicable to the simulation of a wide range of insect species. Although they are one of the most comprehensively studied insect groups in entomology, it is only recently that our understanding of cereal aphid flight and migration has been translated effectively into spatially-explicit simulation models. There are now a multitude of examples available in the literature for modelling methods that address each of the four phases of the aerial transportation process (uplift, transport in the atmosphere, initial distribution, and subsequent movement). I believe it should now be possible to draw together this knowledgebase and the range of modelling methods available to simulate the entire process: integrating mechanistic simulations that estimate the initiation of migration events, with the large scale migration modelling of cereal aphids and their subsequent local movement.
| S-EPMC4337770 | biostudies-literature