Crop Yield Potential Research Articles

Effect of plant growth regulators for yield enhancement in finger millet under drought stress

Finger millet (Ragi) is considered as wonder millet because of its high nutritive and therapeutic values. Drought is a major abiotic stress which limits the crop production and yield potential of finger millet. Considering the yield losses faced by the farmers during drought condition, foliar application of plant growth regulators (PGRs) is widely used to improve the physiological response that might lead to rapid changes in phenotype of the plant and enhancing the production of crops. Finger millet is less efficient in partitioning of assimilates and has low actual yield potential even as a C4 crop. To address these issues, experiments were conducted for improving the partitioning efficiency and yield. Hence, an experiment was conducted to mitigate the drought in finger millet through PGRs. The stress was imposed during flowering stage in the finger millet variety CO15 where different kinds of PGRs viz., Brassinosteroid, Salicylic acid and Chlormequat chloride were used in this experiment. After imposing the treatments, physiological, biochemical and yield traits were recorded. Foliar spray of 0.3 ppm brassinosteroid and 100 ppm salicylic acid exhibited superior performance for improving the osmotic potential, reduces the membrane lipid damage, better chlorophyll pigments, nitrate reductase activity and improved antioxidant system which leads to mitigate the negative impacts of drought in finger millet.

Response of yield and associated traits of wheat genotypes to temperature fluctuations at Dera Ismail Khan

Wheat is the third largest staple food crop accounting for approximately 4% of cropland and its demand is continuously increasing to feed the growing world population. Average surface temperature and recurrent heat waves are continuously rising from last few decades that adversely influence yield potential of various crops including wheat. Temperature differences can be fashioned by cultivating at diverse planting times. Aim of current investigation was to appraise impact of planting times and thermal high temperature (growing degree days) on yield of 8 cultivars. Crop was exposed to four planting times in wheat crop season 2022-23. Experiment was carried out in randomized complete block (RCB) design with 3 replications. Results revealed that planting times depicted significant variances for all considered yield and associated traits. Similarly, cultivars too exhibited highly significant differences for plant height, physiological maturity, spike length, spikelets/spike, grains/spike, thousand grain weight and grain yield/plant however 50% days to heading and tillers per plant depicted non-significant differences among genotypes. Interaction of planting time to genotypes was also significant for 50% days to heading, days to physiological maturity, spike length, spikelets/spike, grains/spike and 1000 grain weight however it was non-significant for plant height, tillers/plant and grain yield per plant. Cultivar AZRC-Dera produced highest grain yield/plant on account of maximum spikelets, grains spike-1, tillers and thousand grain weight. Highest grain yield was recorded in planting times from 1st November to 20th November due to accumulation of more heat units whereas late cultivation reduced production on account of less accumulation of heat units. Therefore it is determined that wheat should be planted from 1st November to 20th November in Dera Ismail Khan to have good harvest

Exogenous application of chemical elicitors affects crop yield and nutraceutical potential of yellow summer squash (Cucurbita pepo L. var. cylindrica) fruits

ABSTRACT The aim of this study was to evaluate the effects of applying chitosan (CHT), methyl jasmonate (MeJA), and salicylic acid (SA) during cultivation of yellow summer squash on selected nutraceutical properties. Plants treated with SA (3 mM) increased the number of fruits, fruit weight, and crop yield by 91.6, 51.4, and 188.8%, respectively (p ≤ 0.05). SA (3 mM) and MeJA (1.5 mM) increased the total phenolic content (TPC) by 19.3 and 39.8%, respectively. SA (3 mM) also increased total flavonoid content (TF) by 28.2% and the antioxidant capacity (AC) by 15.5–60.5%. The three elicitors CHT (1.5 g/L), MeJA (1.5 mM) and SA (3 mM) increased (13.3–17.4%) the cellular antioxidant activity (CCA) compared to the control. Among the treatments, SA (3 mM) was the most effective in enhancing crop yield and key nutraceutical properties while preserving postharvest quality. Although pronounced, CHT (1.5 g/L) and MeJA (1.5 mM) also contributed to crop yield, TPC, and CAA, underscoring their potential as complementary strategies to optimise the production and quality of yellow summer squash.

Agronomic and Post-Harvest Performance of Strawberry Cultivars in High Tunnel and Open-Field Environment in Southeast Virginia

ABSTRACT This study evaluated crop yield potential, season extension, and post-harvest parameters of strawberry cultivars grown in open-field and high tunnel, annual hill production systems. Tested cultivars included “Albion,” “Camino Real,” “Chandler,” “Merced,” “Rocco,” “Ruby June,” “San Andreas,” and “Sweet Ann.” Strawberry plugs were transplanted in the first week of October 2019. The harvest period in the high tunnel was January 2020 through June 2020, while the harvest period in the open-field was April 2020 through June 2020. Except for “Albion” (474 g/plant) having low yield, most cultivars had similar total yields in the high tunnel. “Chandler,” “Rocco” and “Sweet Ann” had the greatest total yields in the open-field (~870 to 780 g/plant). “Albion,” “Merced,” “Ruby June,” “San Andreas” and “Sweet Ann” had the greatest fruit weights in each environment. “Camino Real” and “Ruby June” had the firmest fruit in both environments. In addition, “Merced” produced firm fruits under a high tunnel environment. “Rocco,” and “Ruby June,” had the greatest total soluble solids (~8.7 °Brix) and titratable acidity (>1.85%) in the high tunnel. Total soluble solids for most cultivars were similar in open-field environments. Titratable acidity was greatest (>1.68%) for “Albion,” “Chandler,” “Rocco” and “Ruby June” in open-field production. Different cultivars offered slightly different market-desirable traits. Although the high tunnel system extended the season, achieving marketable yield in high tunnel is challenging due to various biotic and abiotic stresses. Principal component analysis indicated that a more moderate plant size may deliver a greater proportion of large, marketable berries.

Successful PLC Based Solar Powered Automated Irrigation System Prototyping for Water-Strained Agricultural Nations Like Pakistan

Water scarcity poses a critical challenge to agricultural sustainability in nations like Pakistan, despite abundant solar energy potential offering a sustainable solution for year-round productivity enhancement. This research focuses on designing and prototyping a PLC-based solar-powered automated irrigation system tailored for water-strained agricultural nations. The primary goal of this research is to design, prototype, and evaluate a PLC-based solar-powered automated irrigation system. The study utilizes PLC technology (Siemens SIMATIC S7-1200) for automation, incorporating capacitive moisture sensors for closed-loop control based on soil moisture and environmental data for data-driven decision making and precision agriculture. HMI is employed for real-time monitoring and control interface. Results from prototype development show promising outcomes in optimizing water use efficiency and reducing reliance on unreliable electricity sources. This paper underscores the potential of such technologies in fostering sustainable AgriTech development and addressing the pressing water challenges faced by agricultural sectors in developing countries. Prototype development and testing have shown significant improvements in water efficiency compared to traditional irrigation methods. Automated control based on real-time sensor feedback has demonstrated precise water management, enhancing crop yield potential without increasing water consumption.

Mathematical Models for Yield Prediction of Bioenergy Crop

Purpose. To determine mathematical models suitable for yield prediction of bioenergy crops and to establish the patterns of the crop health monitoring. Methods. Field studies were conducted in the zone of unstable soil moisture (Experimental Field of the Institute of Bioenergy Crops and Sugar Beet NAAS of Ukraine, Ksaverivka Druha, Kyiv region) and sufficient soil moisture (Yaltushkiv Experimental Breeding Station of the IBCSB NAAS of Ukraine, Chereshneve, Vinnytsia region) in the Right-Bank Forest Steppe of Ukraine in 2022−2024. Results. Frosts can destroy new giant miscanthus sprouts and reduce crop yield by 15.5% in the sufficient soil moisture zone and by 22.4% in the unstable soil moisture zone. Additionally, under the conditions of unstable soil moisture, yield formation of giant miscanthus is limited by insufficient soil moisture, as miscanthus plants can efficiently use only 20 kg/ha of nitrogen fertiliser. In this case, the yield of giant miscanthus can be predicted using the equation: y = 15.19 + 0.29X – 0.005X². Meanwhile, under the conditions of sufficient soil moisture, small doses of fertilisers do not limit the linear biomass growth function, and the equation for yield prediction is: y = 18.44 + 0.25X. Conclusions. For yield prediction in the specific region, it is advisable to use the regression model proposed by Vossen, which takes into account the average yield, linear time trend, and linear regression function. For a specific agroclimatic region, the potential yield of bioenergy crops could be determined by a formula that considers the total land area of the region, the share of bioenergy plantations, and the predicted yield for the specific conditions of the region. Yield prediction model for bioenergy crop consists of the following levels: (1) determining the areas of bioenergy plantations in the GIS map of the region, specifying the plantation age and the varieties used; (2) refining environmental conditions (soil type, moisture regime, and mineral nutrition); (3) weather observation; (4) vegetation index observation; and (5) yield modelling.

Exploring the role of grafting in abiotic stress management: Contemporary insights and automation trends.

Grafting is a technique that involves attaching a rootstock to the aerial part of another genotype or species (scion), leading to improved crop performance and sustainable growth. The ability to tolerate abiotic stresses depends on cell membrane stability, a reduction in electrolyte leakage, and the species of scion and rootstock chosen. This external mechanism, grafting, serves as a beneficial tool in influencing crop performance by combining nutrient uptake and translocation to shoots, promoting sustainable plant growth, and enhancing the potential yield of both fruit and vegetable crops. Grafting helps to enhance crop production and improve the capacity of plants to utilize water when undergoing abiotic stress, particularly in genotypes that produce high yields upon rootstocks that are capable of decreasing the impact of drought stress on the shoot. The rootstock plays a pivotal role in establishing a grafted plant by forming a union between the graft and the rootstock. This process is characterized by its integrative, reciprocal nature, enabling plants to tolerate abiotic stress conditions. Grafting has been shown to alleviate the overproduction of lipid peroxidation and reactive oxygen species in the leaves and roots and enhance drought tolerance in plants by maintaining antioxidant enzyme activities and stress-responsive gene expression. Phytohormones, such as cytokinin, auxin, and gibberellin, play a critical role in maintaining rootstock-scion interactions. This review unveils the role of grafting in mitigating various environmental stressors, establishment of a robust graft junction, physiology of rootstock-scion communication, the mechanism underlying rootstock influence, hormonal regulations and the utilization of agri-bots in perfect healing and further cultivation of vegetable crops through grafting.

Spatial heterogeneity analysis on distribution patterns and underlying determinants of historical rural settlements: a case study in Taihang Baxing

As vital carriers of cultural heritage, rural areas are facing the threats of degradation and urbanization during rapid urbanization processes. In recent years, China has formulated and implemented a series of national policies, regulations, and heritage protection lists to safeguard and perpetuate the traditional patterns and historical features of rural heritage. However, these rural heritage listings exhibit significant macro-level distributional imbalances and generally lack consideration of regional characteristics. This study uses the Taihang Baxing (THBX) in China as a case study, initially employing historical local documents and maps as base data. It utilizes the stability in the evolution of Chinese characters to compare historical and contemporary village names, filtering out existing historical rural settlements (HRS) to construct a dataset. Further, a multi-scale geographically weighted regression (MGWR) model is applied to analysis the impacts of natural, economic, and social factors on the concentration of HRS (CHRS). The study reveals that the CHRS in the region is markedly characterized by clustering, with evident spatial heterogeneity in the impacts of various factors. Slope (SLP), total annual precipitation (TP), and potential crop yield (PCY) significantly influence the CHRS globally, while elevation (ELV), distance to ancient path (DAP) and surface runoff (DSR), gross domestic product (GDP), and road density (RD) have significant local impacts. This research offers a methodological framework for HRS data compilation and provides theoretical and methodological references for national and local levels of rural heritage protection, optimization of rural spatial patterns, prediction of rural evolution pathways, and implementation of China’s rural revitalization strategy.

Impact of Salinity Stress on Morpho-physiology of Four Rice (Oryza sativa L.) Varieties

Abiotic stress, especially salinity, is one of the major restraints for attaining self-sufficiency in the food grain production. The saline soils affect the crop growth both directly and indirectly by interfering with plant metabolism resulting in reduction in yield potential of various crops. Rice (Oryza sativa L.), the staple food of much of the world’s population, is categorized as a salt-susceptible crop. Improving the salt tolerance of rice would increase the potential of saline-alkali land and ensure food security. To investigate the effect of different levels of salinity on morpho-physiological characters and yield of four rice variety viz. Pokkali, Narendra Ushar Dhan 3, IR 28 and IR 29. A pot experiment was conducted during Kharif 2011 and 2012 at the Department of Crop Physiology, Narendra Deva University of Agriculture and Technology, Kumarganj, Faizabad (U.P.) under normal soil and saline conditions with EC 6.0 and EC 10.0 dS/m. Gradual increase on plant height as well as biomass with the advancement in the age of crop up to dough grain in all the varities under both normal and salinity treatment. Maximum grain yield recorded in tolerant varieties Narendra Usar Dhan 3 followed by Pokkali, whereas minimum in IR 28. Under saline conditions regarding sink potential, the tolerant varieties recorded less reduction in yield attributes like length of panicle (cm), number of panicle per plant, number grain panicle-1 and grain yield plant-1 as compared to normal condition. The salinity tolerance capacity of tolerance varieties was associated with increased the activity of catalase and superoxide dismutase enzyme batter accumulation of proline and total carbohydrate as compared to normal condition.

Effect of center-pivot and subsurface drip irrigation systems on growth and evapotranspiration of volunteer corn in corn, soybean, and sorghum

Abstract Volunteer corn (Zea mays L.) is a competitive weed in corn-based cropping systems. Scientific literature does not exist about the water use of volunteer corn grown in different crops and irrigation systems. The objectives of this study were to characterize the growth and evapotranspiration (ETa) of volunteer corn in corn, soybean [Glycine max (L). Merr.], and sorghum [Sorghum bicolor (L.) Moench] under center-pivot irrigation (CPI) and subsurface drip irrigation (SDI) systems. Field experiments were conducted in south-central Nebraska in 2021 and 2022. Soil moisture sensors were installed at depths of 0 to 0.30, 0.30 to 0.60, and 0.60 to 0.90 m to track soil water balance and quantify seasonal total ETa. Corn was the most competitive, as volunteer corn had the lowest biomass, leaf area, and plant height compared with the fallow. Soybean was the least competitive with volunteer corn, as the plant height, biomass, and leaf area of volunteer corn in soybean were similar to fallow at 15, 30, 45, and 60 d after transplanting (DATr). Averaged across crop treatments, irrigation type did not affect volunteer corn growth at 15 to 45 DATr. Soil water depletion and ETa were similar across crop treatments with and without volunteer corn, as water was not a limiting factor in this study. The ETa of volunteer corn was the highest in soybean (623 mm), followed by sorghum (622 mm), and corn (617 mm) under CPI. The SDI had higher irrigation efficiency, because without affecting crop yield, it had 3%, 6%, and 8% lower ETa in soybean (605 mm), sorghum (585 mm), and corn (571 mm), respectively. Although soil water use did not differ with volunteer corn infestation, a soybean yield loss of 27% was observed, which suggests that volunteer corn may not compete for moisture under fully irrigated conditions; however, it can impact the crop yield potential due to competition for factors other than soil moisture.

Yield variation and yield potential of organic arable crops in Finland derived from statistical data

This study aims to expand and diversify the analysis of crop yields in organic production by utilising the long-term statistical data reported by farmers. We examined the variation of the harvested yields of organic arable crops (season’s ad hoc harvest) on Finnish organic farms and determined the yield potential for different organic crops and in comparison, with conventional counterparts. When comparing median yields per ha, organic yields were on average 65% of conventional yields. The mean yield difference significantly differed between plant species. Among the analysed crops, faba bean performed best, gaining on average 84% of conventional yields, while organic cereals attained an average of 54–68% of conventional yields. The ratio of organic and conventional median yields was compared between geographical regions, and significant regional differences were found. The performance of organic cereals remained stable over the years, but the relative performance of the organic grain legumes, pea and faba bean, depended more on growing conditions. An average yield gap between the best-performing and medianfarms was 38% for organic farms and 28% for conventional farms. This indicates that Finnish organic farms have greater potential to improve the yield level than conventional farms.

Radiation Limits the Yield Potential of Main Crops Under Selected Agrivoltaic Designs—A Case Study of a New Shading Simulation Method

Agrivoltaics (APVs) represent a growing technology in Europe that enables the co-location of energy and food production in the same field. Photosynthesis requires photosynthetic active radiation, which is reduced by the shadows cast on crops by APV panels. The design of the module rows, material, and field orientation significantly influences the radiation distribution on the ground. In this context, we introduce an innovative approach for the effective simulation of the shading effects of various APV designs. We performed an extensive sensitivity analysis of the photovoltaic (PV) geometry influence on the ground-incident radiation and crop growth of selected cultivars. Simulations (2013–2021) for three representative arable crops in eastern Austria (winter wheat, spring barley, and maize) and seven different APV designs that only limited to the shading effect showed that maize and spring barley experienced the greatest annual above-ground biomass and grain yield reduction (up to 25%), with significant differences between the APV design and the weather conditions. While spring barley had similar decreases within the years, maize was characterized by high variability. Winter wheat had only up to a 10% reduction due to shading and a reduced photosynthetic performance. Cold/humid/cloudy weather during the growing season had more negative yield effects under APVs than dry/hot periods, particularly for summer crops such as maize. The lowest grain yield decline was achieved for all three crops in the APV design in which the modules were oriented to the east at a height of 5 m and mounted on trackers with an inclination of +/−50°. This scenario also resulted in the highest land equivalent ratios (LERs), with values above 1.06. The correct use of a tracker on APV fields is crucial for optimizing agricultural yields and electricity production.

Assimilation of Sentinel‐Based Leaf Area Index for Modeling Surface‐Ground Water Interactions in Irrigation Districts

AbstractVegetation‐related processes, such as evapotranspiration (ET), irrigation water withdrawal, and groundwater recharge, are influencing surface water (SW)—groundwater (GW) interaction in irrigation districts. Meanwhile, conventional numerical models of SW‐GW interaction are not developed based on satellite‐based observations of vegetation indices. In this paper, we propose a novel methodology for multivariate assimilation of Sentinel‐based leaf area index (LAI) as well as in‐situ records of streamflow. Moreover, the GW model is initially calibrated based on water table observations. These observations are assimilated into the SWAT‐MODFLOW model to accurately analyze the advantage of considering high‐resolution LAI data for SW‐GW modeling. We develop a data assimilation (DA) framework for SWAT‐MODFLOW model using the particle filter based on the sampling importance resampling (PF‐SIR). Parameters of MODFLOW are calibrated using the parameter estimation (PEST) algorithm and based on in‐situ observation of the GW table. The methodology is implemented over the Mahabad Irrigation Plain, located in the Urmia Lake Basin in Iran. Some DA scenarios are closely examined, including univariate LAI assimilation (L‐DA), univariate streamflow assimilation (S‐DA), and multivariate streamflow‐LAI assimilation (SL‐DA). Results show that the SL‐DA scenario results in the best estimations of streamflow, LAI, and GW level, compared to other DA scenarios. The streamflow DA does not improve the accuracy of LAI estimation, while the LAI assimilation scenario results in significant improvements in streamflow simulation, where, compared to the open loop run, the (absolute) bias decreases from 75% to 6%. Moreover, S‐DA, compared to L‐DA, underestimates irrigation water use and demand as well as potential and actual crop yield.

Propose and implement a Rule-Based System to Predict Crop Yield Production

Accurate meteorological forecasting plays a pivotal role in agricultural decision-making, particularly in determining crop yield potential and optimizing agricultural practices. This study investigates the application of data mining techniques in meteorological forecasting to enhance crop yield prediction accuracy Preliminary findings suggest that data mining techniques, including machine learning algorithms, neural networks, and ensemble methods, offer significant potential for improving the accuracy and reliability of meteorological forecasts for crop yield prediction. In conclusion, this study underscores the potential of data mining techniques in improving meteorological forecasting for crop yield potential assessment.

Crop Yield Prediction and Classification using the Agriculture Resources and Data Mining Techniques

This study investigates the application of data mining techniques in meteorological forecasting to enhance crop yield prediction accuracy Moreover, the integration of meteorological forecasting with agronomic models and geographical information systems (GIS) facilitates site-specific crop management and precision agriculture practices. By combining meteorological data with soil properties, crop phenology, and socio-economic factors, data mining techniques enable the development of predictive models that enhance crop yield potential and optimize resource allocation. By leveraging advanced data analytics and machine learning algorithms, agricultural stakeholders can make informed decisions, mitigate risks, and enhance productivity in the face of changing climatic conditions and environmental uncertainties.

Resistance of Sesame (<i>Sesamum indicum L</i>.) Genotypes Against Bacterial Blight (<i>Xanthomonas campestris pv. sesami</i>) in Benishangul Gumuz Region, Northwestern Ethiopia

Bacterial blight poses a significant threat to sesame production in Ethiopia, especially in regions with high rainfall. It causes yield reduction and affects seed quality by inducing premature leaf defoliation. To address these challenges, evaluating existing germplasm for disease resistance is crucial. This study assessed various sesame genotypes for resistance to bacterial blight and their performance in seed yield and seed yield related traits. Seventeen genotypes were evaluated in a randomized complete block design at Kamashi research sub-station. Resistance evaluations were conducted every 14 days from emergence up to 72 days, along with recording seed yield and related agronomic and morphological traits. The mean area under the disease progress curve (AUDPC) varied from 673.86 to 825.01, indicating differing susceptibility levels to disease advancement. Approximately 46.67% of the tested genotypes exhibited lower AUDPC compared to Benishangul-1, a variety specifically developed for its adaptability and resistance for bacterial blight-prone regions. Initially, at 14 and 28 days after emergence (DAE), no noticeable bacterial blight symptoms were observed across the genotypes. However, at 42, 56, and 72 DAE, the average severity index steadily rose to 16.92%, 20.78%, and 27.71%, respectively. This transition from immunity to moderate susceptibility underscores the dynamic nature of disease progression and the significant challenge posed by bacterial blight in later sesame growth stages. Notably, significant differences (P<0.05) were noted in days to 50% flowering, days to 90% maturity, plant height to the first branch, overall plant height, length of the capsule-bearing zone, and seed yield. This comprehensive evaluation offers valuable insights into the genetic diversity to improve crop performance and yield potential.

On-farm evaluation of a crop forecast-based approach for season-specific nitrogen application in winter wheat

Inadequate nitrogen (N)-fertilisation practices, that fail to consider seasonally variable weather conditions and their impacts on crop yield potential and N-requirements, cause reduced crop N-use efficiency. As a result, both the ecological and economic sustainability of crop production systems are put at risk. The aim of this study was to develop a season-specific crop forecasting approach that allows for a targeted application of N in winter wheat while maintaining farm revenue compared to empirical N-fertilisation practices. The crop forecasts of this study were generated using the process-based crop model SSM in combination with state-of-the-art seasonal ensemble weather forecasts (SEAS5) for the case study region of Eastern Austria. Results from three winter wheat on-farm experiments showed a significant reduction in applied N when implementing a crop forecast-based N-application approach (-43.33 kgN ha-1, -23.42%) compared to empirical N-application approaches, without compromising revenue from high-quality grain sales. The benefit of this reduced N-application approach was quantified through the economic return to applied N (ERAN). While maintaining revenue, the lower amounts of applied N led to significant benefits of + 30.22% (+ 2.20 € kgN-1) in ERAN.

Assessment of cluster front line demonstration on yield and economics of summer sesame

Sesame is one of the most important oilseed crops which play a major role in increasing the income of small and marginal farmers of the Kutch region of Gujarat. To promote the improved cultivation technology of sesame with high yielding variety ‘GT4’ and ‘GJT5’, 123 cluster front line demonstrations (CFLDs) were conducted in 63.2 ha area during summer, at ICAR-CAZRI, Krishi Vigyan Kendra, Kukma, Bhuj, Kachchh, Gujarat during summer 2017 to 2020. The four years data revealed that the average yield increased to 16.34% over the existing practice due to adoption of improved practice. The average technology gap, extension gap and technology index were 270.5 kg/ha, 121 kg/ha and 24.95%, respectively. Furthermore, the economics of the demonstrations and farmers practice showed an increased net return of ₹39,319/ha with benefit: cost ratio (BCR) of 2.47 in demonstrated plots over conventional plots where it was ₹30,700/ha and 2.19, respectively. By conducting front line demonstration of the proven technologies, yield potential of sesame crop could be enhanced to a greater extent with increase in the income level of the farming community.

Nitrogen form mediates sink strength and resource allocation of a C3 root crop under elevated CO2

Plant physiological processes alter with elevated carbon dioxide concentrations in the atmosphere (eCO2), which affects the growth and yield potential of crops. Among plants with C3 carbon fixation, root crops show highest yield response under eCO2 which is suggested to be linked to their large carbon (C) sink strength. The high C gain under eCO2 can be limited by processes that constrain sink capacity, such as nitrogen (N) supply. Different N sources may interact with eCO2 and thus have variable impacts on carboxylation activity, N uptake efficiency and plant development. This study aims at contributing to a better understanding of sink-driven assimilation, re-allocation and finally growth processes under eCO2 as a function of N-form. Radish (Raphanus sativus L. var. sativus), a C3 tuber plant with strong sink strength, was used. The plants were grown in pots in climate chambers at 400 ppm (aCO2) and 1000 ppm CO2 (eCO2) with either pure nitrate or ammonium-dominated nutrition. A split plot design was applied. Plants were harvested after four weeks and physiological, morphological and chemical parameters in leaves and tubers were assessed. The N-form had no effect on N acquisition, but affected C and N partitioning into plant organs differently under eCO2. N assimilation processes of nitrate-fed plants were focussed on leaves being both source and sink, and those of ammonium-fed plants on tubers, the strongly pronounced sink. Acclimation of CO2 fixation due to eCO2 did not occur for both N forms, probably due to altered sink strength and low N content in leaves. The N-form influences the sink-driven C and N balance and thus enables unrestricted carbon gain as well as the variation of organ development under future eCO2 conditions. These processes can be utilized in cultivation and breeding.

Biochar as a carbon dioxide removal strategy in integrated long-run mitigation scenarios

Limiting global warming to under 2 °C would require stringent mitigation and likely additional carbon dioxide removal (CDR) to compensate for otherwise unabated emissions. Because of its technology readiness, relatively low cost, and potential co-benefits, the application of biochar to soils could be an effective CDR strategy. We use the Global Change Analysis Model, a global multisector model, to analyze biochar deployment in the context of energy system uses of biomass with CDR under different carbon price trajectories. We find that biochar can create an annual sink of up to 2.8 GtCO2 per year, reducing global mean temperature increases by an additional 0.5%–1.8% across scenarios by 2100 for a given carbon price path. In our scenarios, biochar’s deployment is dependent on potential crop yield gains and application rates, and the competition for resources with other CDR measures. We find that biochar can serve as a competitive CDR strategy, especially at lower carbon prices when bioenergy with carbon capture and storage is not yet economical.