Despite advancements in electrification and the transition to solar-based electricity production, India will continue to depend on land-based carbon offsets to achieve its net-zero target. Land-based climate mitigation strategies in India can be implemented by utilizing underutilized marginal lands or increasing land availability through technological interventions to close agricultural yield gaps. Both below-ground (e.g., soil carbon) and above-ground (e.g., standing tree biomass) options offer viable pathways for such measures. Key strategies include cultivating perennial bioenergy feedstocks, afforestation, establishing fast-growing Miyawaki forests, restoring wetlands and mangroves, and applying biosolids to land. However, caution is essential to prevent unintended consequences, such as clearing natural forests or introducing microplastics into soils. The cost of carbon sequestration and the resilience or permanence of stored carbon will be critical factors in determining the preferred approach. Additionally, land-based strategies often overlap spatially, making GIS-based tools indispensable for identifying optimal solutions tailored to local conditions. Integrating these strategies into the national carbon budget can enhance transparency and contribute significantly to India’s net-zero emissions goal.

Evapotranspiration (ET), a key component of the hydrological cycle, responds to and influences climate change, making accurate estimation of reference ET (ETo) critical for long-term impact assessments. The widely applied FAO Penman–Monteith (FAO-PM) equation for calculating ETo does not account for rising atmospheric CO2, which reduces vegetation stomatal conductance and can lead to systematic overestimation of ETo. We derived a modified FAO-PM equation incorporating CO2 effects on stomatal behavior. Using projections from five global circulation models, we compared spatiotemporal average of ETo estimates for India from the original and modified equations under SSP5-8.5 and SSP1-2.6. Differences were 0.11–1.29 mm day−1 (2021–2030), 0.09–1.90 mm day−1 (2051–2060), and 0.17–3.14 mm day−1 (2091–2100) under SSP5-8.5, with slightly lower values under SSP1-2.6. Seasonal differences between the predicted ETo from the two equations peaked during the pre-monsoon, reaching 3.90 mm day−1 (SSP5-8.5) and 1.74 mm day−1 (SSP1-2.6). Neglecting stomatal responses to CO2 could lead to ETo overestimation of ~29% under SSP5-8.5 by 2100, potentially biasing projections of droughts, heatwaves, and water demand. By contrast, overestimation is moderate (~13%) under SSP1-2.6. Incorporating the impact of CO2 into ETo estimation is therefore essential for robust climate change impact assessments.

Globally, plantation forests are widely recognized as an effective solution to combat land degradation. One such approach of creating plantation forest is the Miyawaki method of afforestation and reforestation, which involves dense planting of native species. This study investigates the carbon sequestration potential of three Miyawaki forests aged 2, 4, and 5 years in the south Indian cities of Bengaluru and Palakkad. We conducted field sampling to measure tree attributes, including Diameter at Breast Height (DBH) and height, which were used to calculate the above-ground biomass (AGB) using species-specific equations. Carbon storage and sequestration rates were then estimated using the same allometric approach, combined with the age of the Miyawaki forest stands. Our findings reveal that the annual growth rate of forest biomass increases significantly with age, resulting in a total biomass accumulation of 165.7 Mg C/ha within five years of planting. Additionally, carbon sequestration rates showed a rapid increase with forest age, with the 2-year-old forest sequestering 5.284 Mg C/ha-yr, the 4-year-old forest sequestering 20.042 Mg C/ ha-yr, and the 5-year-old forest sequestering 33.084 Mg C/ha-yr. We also identified over 200,000 km² of underutilized marginal land with climatic conditions similar to those of the study sites, offering vast potential for expanding Miyawaki forest interventions. In this context, the Miyawaki method could be positioned within policy interventions aimed at climate mitigation in India and beyond, considering the relevant biophysical and ecological factors.

Bioenergy with carbon capture and geological storage (BECCS) is considered one of the top options for both offsetting CO2 emissions and removing atmospheric CO2. BECCS requires using limited land resources efficiently while ensuring minimal adverse impacts on the delicate food-energy-water nexus. Perennial C4 biomass crops are productive on marginal land under low-input conditions avoiding conflict with food and feed crops. The eastern half of the contiguous U.S. contains a large amount of marginal land, which is not economically viable for food production and liable to wind and water erosion under annual cultivation. However, this land is suitable for geological CO2 storage and perennial crop growth. Given the climate variation across the region, three perennials are major contenders for planting. The yield potential and stability of Miscanthus, switchgrass, and energycane across the region were compared to select which would perform best under the recent (2000-2014) and future (2036-2050) climates. Miscanthus performed best in the Midwest, switchgrass in the Northeast and energycane in the Southeast. On average, Miscanthus yield decreased from present 19.1 t/ha to future 16.8 t/ha; switchgrass yield from 3.5 to 2.4 t/ha; and energycane yield increased from 14 to 15 t/ha. Future yield stability decreased in the region with higher predicted drought stress. Combined, these crops could produce 0.6-0.62 billion tonnes biomass per year for the present and future. Using the biomass for power generation with CCS would capture 703-726 million tonnes of atmospheric CO(2 )per year, which would offset about 11% of current total U.S. emission. Further, this biomass approximates the net primary CO(2 )productivity of two times the current baseline productivity of existing vegetation, suggesting a huge potential for BECCS. Beyond BECCS, C4 perennial grasses could also increase soil carbon and provide biomass for emerging industries developing replacements for non-renewable products including plastics and building materials.

<p>The Wayanad district of Kerala, India resides on the crest of the Western Ghats, one of the 36 Biodiversity hotspots in the world and known for its rich abundance of flora and ethnic cultures. Switching of farm practices from traditional to modern and rapid urban developmental activities is seen as a trend in the district. In this scenario, analysis of biodiversity associated with rice-based farms under various farming systems is important in this district. The adjacent upland agriculture area of rice fields of 9 rice-based agroecosystems was selected for the current study. Out of the 9 sites, 3 sites were traditional farms maintained by <em>Kurichiya</em> tribal communities, 3 were organic farms, and the other 3 farms were modern. A total of 45 families, 99 genera, 129 species of tree, and 101 bird species which belonged to 48 families, and 17 orders were identified from the study sites. This study recorded 7302, and 2072 tree and bird individuals respectively. The Normalized Difference Vegetation Index (NDVI) time series data was also derived for each site. The principal component analysis portrayed that there is a compositional relationship among native tree diversity indices, mean NDVI for May, June, August, and October, and bird diversity indices.  Further, Pearson Correlation proved their significant correlation. This study also exhibits the possibility of an increased abundance of Granivorous bird species in less native tree-abundant farming sites, which are considered a pest in rice farms. All the traditional farms were found to be abundant in native tree species and they are reported to have sustainable production in rice fields.  The culture and religious beliefs are the reason for the native tree abundance in their farming sites. Increasing native tree abundance can attract many species of birds which can act as natural enemies for the pests in the farmland.</p>

<p>Climate, hydrology, and plant processes are three factors that are intrinsically linked to one another. Integration of dynamic vegetation and canopy level processes governed by leaf biochemical traits with the subsurface water flow will help us to make more reliable and actionable predictions in the context of climate change. The limitations of hydrological works that consider plants to be statistical components are highlighted by a number of hydrological studies.</p> <p>This study aims to highlight how crucial it is to include plant and plant physiological processes as a significant and dynamic component when modeling hydrological processes. For this purpose, we demonstrate the impact of stomatal conductance, photosynthesis, and other biophysical traits on the soil water dynamics within the vadose zone under current and projected (in future) climate scenarios using a process-based crop growth model BioCro II which uses climate variables as its input. We compare our results with those obtained using HYDRUS 1-D, which is a state-of-art model that has a wide range of applications in agriculture and irrigation. HYDRUS 1-D is a model capable of simulating one-dimensional water, heat, and solute transport through an unsaturated porous media. We also discuss the merits of coupling these two models to address some of the future challenges. </p>

Climate Change threatens agriculture, and agriculture can also play an essential role in climate change mitigation and adaptation through increasing agricultural efficiencies and greening the energy sector by making room for sustainable renewable bioenergy crops. Efforts for climate change mitigation and adaptation with a focus on agriculture must come from transdisciplinary collaboration, which is often not easy and require one to come out of one’s comfort zone. However, at the same time, plenty of tools can facilitate and make such collaboration easy, especially for those who are not modellers or modelling with a focus on a specific aspect of climate change and agriculture. This chapter summarizes such tools and their application in accelerating transdisciplinary collaboration for more sustainable and climate-resilient agriculture.

<p>India has 192.25 million ha of arable land, of which 23.2% remains fallow. 25.49, 54.24, and 1.30 million ha are used only for Rabi, Kharib, and Zayad crops, respectively. Thus, leaving a significant portion (81.03 million ha) of agricultural land remains unutilized either throughout the year or for a considerable length of a year. Additionally, 34.53 million ha of wasteland (marginal land) can produce suitable energy crops requiring fewer inputs, such as Agave. Here, we estimate the total amount of sunlight falling over unutilized land areas over one year and the theoretical efficiencies of C3, C4, and CAM to convert solar radiation to biomass to estimate the potential availability of biomass on an annual basis. We further use industrial conversion efficiencies to produce various biofuels from biomass to perform a theoretical analysis of scaling up sustainable bioenergy without causing a conflict with crop production.</p>

Traditional agriculture relies on ecosystem services for sustainable food production and is also identified as a climate-smart approach. The present study analyses the agroforests associated with the rice farming system of three different agricultural practices for biodiversity richness by comparing two parameters: plants and birds. Out of the nine study sites, three sites were traditional farms maintained by Kurichiya tribal communities, three were natural farms, and the other three farms were modern. A total of 45 families, 104 genera, 128 species of plants, and 101 bird species belonged to 48 families, and 17 orders were identified from the study sites. The sample-size-based rarefaction and extrapolation (R/E) method was adopted to identify estimated biodiversity indices. Renyi profile was used to understand the native tree diversity profile of the selected sites. The result of this study indicates that bird diversity is positively correlated with native tree diversity and NDVI of May and October. Conserving more native trees in the farmland could be one of the reasons for the sustainable agriculture system of the Kurichiya tribal community as it attracts more bird species and contributes to the biological control of pests. Thus, the conservation of native tree species in the agroforest of rice-based agroecosystems will contribute to the sustainable agriculture system.

Grasslands are the largest contributor of nitrous oxide (N 2 O) emissions in the agriculture sector due to livestock excreta and nitrogen fertilizers applied to the soil. Nitrification inhibitors (NIs) added to N input have reduced N 2 O emissions, but can show a range of efficiencies depending on climate, soil, and management conditions. A meta-analysis study was conducted to investigate the factors that influence the efficiency of NIs added to fertilizer and excreta in reducing N 2 O emissions, focused on grazing systems. Data from peer-reviewed studies comprising 2164 N 2 O emission factors (EFs) of N inputs with and without NIs addition were compared. The N 2 O EFs varied according to N source (0.0001–8.25%). Overall, NIs reduced the N 2 O EF from N addition by 56.6% (51.1–61.5%), with no difference between NI types (Dicyandiamide—DCD; 3,4-Dimethylpyrazole phosphate—DMPP; and Nitrapyrin) or N source (urine, dung, slurry, and fertilizer). The NIs were more efficient in situations of high N 2 O emissions compared with low; the reduction was 66.0% when EF > 1.5% of N applied compared with 51.9% when EF ≤ 0.5%. DCD was more efficient when applied at rates > 10 kg ha −1 . NIs were less efficient in urine with lower N content (≤ 7 g kg −1 ). NI efficiency was negatively correlated with soil bulk density, and positively correlated with soil moisture and temperature. Better understanding and management of NIs can optimize N 2 O mitigation in grazing systems, e.g., by mapping N 2 O risk and applying NI at variable rate, contributing to improved livestock sustainability.

Nature-based solutions (Nbs) are seen as an effective way to mitigate climate change and stabilize the climate of the earth. Here, we report ground measurements of a newly established forest site on the campus of IIT Palakkad, Kerala India (lat = 10.809, lon =76.746). The site (approximately 1600 meter2 ) was previously dominated by fountain grass, which is locally considered to be an invasive species. After land preparation, a new forest utilizing approximately 20 native species of trees was planted following Miyawaki's methodology. Direct measurements of tree diameter at the breast height (tbh) were made to estimate total standing biomass using species specific allometric equations. The standing biomass after two years is estimated to be 3261 kg (5967 kg CO2) over the entire forest area. The total carbon sequestered during the first two years of this forest’s life is sufficient to neutralize carbon emission by a gasoline car driven for a distance of 48909 km or carbon emission by a car running on 100E fuel over a distance of 349355 km. Our work demonstrates that the carbon sequestration rate (18 tons CO2 ha-1 yr-1) by the forest established using the Miyawaki method at our study site is comparable to some of the most productive forests reported in the available literature. Further, our analysis demonstrates that NbS can be made more efficient if spatial land use planning can be optimized to make room for sustainable biomass production for energy and conservation purposes.

SummaryThe ‘One Hundred Important Questions Facing Plant Science Research’ project aimed to capture a global snapshot of the current issues and future questions facing plant science. This revisiting builds on the original 2011 paper. Over 600 questions were collected from anyone interested in plants, which were reduced to a final list of 100 by four teams of global panellists. There was remarkable consensus on the most important topics between the global subpanels. We present the top 100 most important questions facing plant science in 2022, ranging from how plants can contribute to tackling climate change, to plant‐defence priming and epigenome plasticity. We also provide explanations of why each question is important. We demonstrate how focussing on climate change, community and protecting plant life has become increasingly important for plant science over the past 11 years. This revisiting illustrates the collaborative and international need for long‐term funding of plant science research, alongside the broad community‐driven efforts to actively ameliorate and halt climate change, while adapting to its consequences.

<p>A combination of technological, nature-based and demand-side solutions are envisioned to avert the most drastic consequences of climate change, connected via a greenhouse gas (GHG) economy and government policies (e.g., net-zero incentives, compensations etc.). Measurement, Reporting and Verification (MRV) of GHGs reduced or removed from the atmosphere are central to ensuring that revenue streams develop in proportion to true climate benefits with equitable rewards for small and large originators.</p> <p>However, current MRV limitations (e.g., cost, robustness, interoperability, scalability, multi-year latency, etc.) curtail our ability to approach climate solutions in a well-informed and consistent manner. This challenge can be addressed by creating an MRV benchmark that is directly and frequently measured, uniformly derived, universally applicable to the technological and nature-based solutions, and traceable in near-real time and space. In order to narrow the knowledge-action gap the social and natural sciences both recognize this need for continuous information on local GHG emission and sequestration akin to weather intelligence.</p> <p>Technology transfer of the latest, most direct GHG quantification methods from academic climate science to the climate solution marketplace provides a promising avenue for creating such a benchmark: Next-generation information reconstruction (https://tinyurl.com/flux-tower-mapping) applied to existing local-to-global networks of direct GHG flux measurements can achieve unmatched statistical power, interpretability and process insight. This integration will generate an orders-of-magnitude improved stream of directly-measured emission and sequestration rates for robustly anchoring project-scale GHG mitigation and wall-to-wall remote sensing and models. The resulting benchmark directly represents a financial commodity: the physical emission and sequestration of GHGs. Thus, they can be used to manage GHGs in day-to-day practices and to assess the value of financial derivatives such as GHG certificates based on discipline-specific protocols, while accounting for reliability, storage duration and other factors.</p> <p>This approach will result in decameter-resolution maps of GHG emission and sequestration per unit of time, locked in a secure vessel such as a blockchain to prevent tampering, deleting, or modifying. Access via mobile Apps and APIs will enable public awareness and confidence, climate solution research, GHG certificate intercomparisons, development of regulatory and financial products, tools, climate-smart technologies, practices and commercial services, and national as well as local policies. Paths to monetization include licensing to credit originators, offset buyers and marketplaces, through connecting pixel-scale GHG exchange to regulatory practice for a range of GHG certificate protocols, industries, stakeholders and management practices. With this conceptual outline, we invite all types of stakeholders to join Carbon Dew: the Community of Practice that aims to anchor equitable climate solutions worldwide in direct measurements of GHG sequestration and emission (https://tinyurl.com/join-carbon-dew).</p>

Perennial grasses can reduce soil erosion, restore carbon stocks, and provide feedstocks for biofuels and bioproducts. Here, we show an additional benefit, amelioration of regional climate warming, and drying. Growing Miscanthus × giganteus, an example of perennial biomass crops, on US marginal land cools the Midwest Heartland summer by up to 1°C as predicted by a new coupled climate‐crop modeling system. This cooling is mainly caused by the increased duration and size of the Miscanthus × giganteus leaf canopy when compared with the existing vegetations on marginal land, resulting in larger solar reflection, more evapotranspiration, and decreased sensible heat transfer. Summer rainfall is increased through mesoscale circulation responses by 23–29 mm (14%–15%) and water vapor pressure deficit reduced by 5%–13%, lowering potential transpiration for all Midwest crops. Similar but weaker effects are simulated in the Southern Heartland. This positive feedback through the climate–crop interaction and teleconnection leads to 4%–8% more biomass production and potentially 12% higher corn and soybean yields, with greater yield stability. Growing perennials on marginal land could be a feasible solution to climate change mitigation and adaptation by strengthening food security and providing sustainable alternatives to fossil‐based products.

The central motivation for mechanistic crop growth simulation has remained the same for decades: to reliably predict changes in crop yields and water usage in response to previously unexperienced increases in air temperature and CO2 concentration across different environments, species, and genotypes. Over the years, individual process-based model components have become more complex and specialized, increasing their fidelity but posing a challenge for integrating them into powerful multiscale models. Combining models is further complicated by the common strategy of hard-coding intertwined parameter values, equations, solution algorithms, and user interfaces, rather than treating these each as separate components. It is clear that a more flexible approach is now required. Here we describe a modular crop growth simulator, BioCro II. At its core, BioCro II is a cross-platform representation of models as sets of equations. This facilitates modularity in model building and allows it to harness modern techniques for numerical integration and data visualization. Several crop models have been implemented using the BioCro II framework, but it is a general purpose tool and can be used to model a wide variety of processes.

The Midwestern "Corn-Belt" in the United States is the most productive agricultural region on the planet despite being predominantly rainfed. In this region, global climate change is driving precipitation patterns toward wetter springs and drier mid- to late-summers, a trend that is likely to intensify in the future. The lack of precipitation can lead to crop water limitations that ultimately impact growth and yields. Young plants exposed to water stress will often invest more resources into their root systems, possibly priming the crop for any subsequent mid- or late-season drought. The trend toward wetter springs, however, suggests that opportunities for crop priming may lessen in the future. Here, we test the hypothesis that early season dry conditions lead to drought priming in field-grown crops and this response will protect crops against growth and yield losses from late-season droughts. This hypothesis was tested for the two major Midwestern crop, maize and soybean, using high-resolution daily weather data, satellite-derived phenological metrics, field yield data, and ecosystem-scale model (Agricultural Production System Simulator) simulations. The results from this study showed that priming mitigated yield losses from a late season drought of up to 4.0% and 7.0% for maize and soybean compared with unprimed crops experiencing a late season drought. These results suggest that if the trend toward wet springs with drier summers continues, the relative impact of droughts on crop productivity is likely to worsen. Alternatively, identifying opportunities to breed or genetically modify pre-primed crop species may provide improved resilience to future climate change.

Brazil is one of the largest exporters of cattle meat production. Most of this production is under pasture areas, with different levels of livestock and field management. Remotely sensed images could be interesting tools to detect distinct temporal and spatial patterns of these systems. In this context, classification algorithms have been proposed to use information from satellite images to map different land covers. The Time-Weighted Dynamic Time Warping (TWDTW) is an algorithm that has the advantage of working well with datasets with enough amounts of temporal information and seasonality patterns. In the present work, the TWDTW was performed to classify pasture managements in farms located in Western region of São Paulo State in Brazil for the years 2017 and 2018, as a primary study. It was used Normalized Difference Vegetation Index (NDVI) time series images from Moderate Resolution Imaging Spectroradiometer – MODIS sensor (products MOD13Q1 and MYD13Q) with 250 meters of spatial resolution. In classifications for the years 2017 and 2018, it was observed a predominance of traditional pasture. Total areas of degraded and traditional pasture were very similar between 2017 and 2018. The year of 2017 showed higher spatial distribution of intensified pastures than year 2018. The classification achieved satisfying results with complete accuracy in validation. The information collected from field visits were important to analyse general aspects of the results. Therefore, in this pilot study TWDTW algorithm demonstrated to have potential in differentiating classes of pasture management. Next steps will be to explore the possibilities to classify pasture systems in large areas.

This paper aims at analyzing how technologies, like AI, can enable us to take better measures against COVID-19 which has caused multi-disciplinary changes at the global level. Governments are seen trying to curb the spread of COVID-19. But one of the major problems they have been facing is the shortage of testing equipment. Considering this a strategy for finding alternative solutions is a must to ensure minimizing the number of tests that needed to be done. One such approach is: pool sampling, i.e. combined patient samples and testing the combine samples once. Pooling can succeed at a unitary cost, if all the samples taken are negative. But if a single sample comes out to be positive then infected patient does not mean failure. This paper describes how to optimally detect infected patients in pool samples, i.e. using a minimum number of tests to exactly recognize them, by making an assumption the a priori probabilities that every patient is healthy. Estimation of those probabilities using questionnaires, supervised machine learning or clinical examinations can be done. The algorithmic results achieved, are like informed divide-and-conquer methodologies and are efficient at performance.