Title Quantification of carbon sequestration of agroforestry systems Allometry approach.pdf ✅

Quantification of carbon sequestration of agroforestry systems: Allometry approach Sangram B Chavan1 , Uthappa AR2 , Keerthika A.3 , S. Suresh Ramanan4 , AK Handa 4 , Rupali Sing1 and Shubham Gurav1 1 ICAR-National Institute of Abiotic Stress Management, Baramati, Maharashtra 2 ICAR-Central Coastal Agricultural Research Institute, Ela, Goa 3 ICAR-Central Arid Zone Research Institute (CAZRI), RRS, Pali-Marwar,Rajasthan- 306 401 4 ICAR-Central Agroforestry Research Institute, Jhansi, Uttar Pradesh 1. Introduction Carbon sequestration, the process of removing CO2 from the atmosphere and storing it in long-lived pools in the biosphere, plays a pivotal role in mitigating climate change impacts. Agroforestry systems offer significant carbon sequestration potential, with aboveground and belowground biomass serving as major carbon pools (Fig 1). These systems facilitate the absorption of atmospheric CO2 during photosynthesis, capturing and storing fixed carbon in trees, detritus, and soil, thus contributing to a safer environment. Agroforestry's prominence is evident from its widespread adoption across the globe. With approximately 1 billion hectares of agricultural land practicing agroforestry, benefitting around 1.5 billion farmers, its significance in enhancing sustainability and resilience is increasingly recognized. Additionally, vast unproductive croplands and grasslands hold the potential for future agroforestry expansion, further increasing carbon sequestration capacity. In India, agroforestry covers roughly 25 million hectares, representing 8.2% of the country's declared geographical area. Trees are present not only on farmland but also within agricultural lands, making agroforestry an integral component of the country's land-use strategy. Different agroforestry systems sequester varying amounts of carbon, depending on factors such as system type, species composition, soil characteristics, and climate conditions. Recognizing the significance of agroforestry in climate change mitigation, India has implemented various policies, missions, and national action plans to promote tree-based farming systems. The Green India mission, National Agroforestry Policy, and National Agroforestry and Bamboo Mission are some notable initiatives aimed at increasing tree cover and offsetting greenhouse gas emissions. Quantifying carbon stocks in agroforestry systems remains challenging, but it is essential for monitoring and reporting carbon storage accurately. Ground-based estimations, aided by allometric equations, provide valuable reference points for regional biomass carbon mapping. These equations establish precise relationships between tree attributes, facilitating reliable biomass estimation. However, most existing allometric equations are developed for forests or sole plantations and may underestimate biomass carbon in agroforestry due to specific planting geometries, local environmental conditions, and tree management practices. In this context, this study aims to develop and refine allometric equations tailored to agroforestry trees, improving carbon stock estimations in this critical land-use system. By enhancing our understanding of
carbon sequestration potential, we can contribute to national and global efforts to combat climate change, achieve climate targets, and create sustainable and resilient agroforestry landscapes. Fig 1: Process and carbon pools in agroforestry 2. Carbon pools in Agroforestry Carbon pools in agroforestry refer to the various components of the agroforestry system where carbon is stored. These pools play a crucial role in carbon sequestration, helping to remove carbon dioxide (CO2) from the atmosphere and store it in long-lived forms. The major carbon pools in agroforestry include: 1. Aboveground Biomass: This pool comprises the living parts of trees, such as stems, branches, leaves, and fruits. Aboveground biomass is a significant carbon store, as trees absorb CO2 from the atmosphere during photosynthesis, converting it into organic carbon in their tissues. 2. Belowground Biomass: Belowground biomass includes tree roots and associated soil organisms. Roots contribute to carbon storage in the form of organic matter, and soil microorganisms help decompose organic materials, converting them into stable soil carbon. 3. Soil Organic Carbon: Agroforestry systems enhance soil carbon sequestration due to the presence of trees and diverse vegetation. Trees shed leaves, branches, and other organic matter, contributing to soil organic carbon content. Additionally, the root systems of trees enhance soil structure and facilitate the accumulation of carbon in the soil. 4. Litter and Detritus: Litter and detritus refer to dead plant material, such as fallen leaves, twigs, and fruits, as well as decomposed organic matter on the forest floor. These materials gradually decompose, releasing CO2 back into the atmosphere, but a significant portion can also be incorporated into the soil carbon pool.
5. Dead Wood: Dead wood includes standing or fallen dead trees and branches. This carbon pool contributes to carbon storage in the ecosystem as it decomposes slowly, depending on factors like climate and microorganisms present. Each of these carbon pools plays a vital role in the overall carbon sequestration capacity of agroforestry systems. The management practices, tree species composition, soil conditions, and climate of the specific agroforestry system influence the distribution and dynamics of these carbon pools. By understanding and optimizing these pools, agroforestry can serve as a valuable climate- smart land-use option, contributing to climate change mitigation and sustainable agricultural practices. The protocol to estimate carbon stock of agroforestry systems in provided in fig 2. Fig 2: Flowchart of Measurement of CSP of agroforestry in Haryana (Chavan, 2019). 3. Allometry equations An allometric relation is one whereby one measured parameter is a good estimate of another unmeasured parameter in the same organism. A less harmful way to carry out biomass estimate is to develop an allometric equation that will allow us to estimate the mass of a tree from a few simple measurements of it and then to apply this equation to the trees in a forest. The term allometry is defined as "the measure and study of relative growth of a part in relation to an entire organism or to a standard". It is based upon a principle first describe by Galileo Galilee in the 1630's about how the proportions of an organism must change as it gets bigger. Allometric equations, relating biomass with one or more tree dimensions, are frequently used to compute
average tree biomass. Component-wise biomass estimation based on non-harvest technique is desirable as against the techniques, which involves harvest of different parts of trees, including felling of entire tree for generation of equations for estimating biomass. Allometric equations that relate tree diameter at breast height (1.37 m) to other attributes such as standing carbon stock and leaf area are an important and often-used tool in ecological research as well as for commercial purposes. Such tools represent the primary method for estimating above-ground forest dry matter or carbon. If we call biomass B and diameter D, this second definition means that there is a coefficient a such that: dB b = a dD D which integrates to a power relationship: B = b × D a Parameter ‘a’ gives the allometry coefficient (proportionality between relative increases), whereas parameter b indicates the proportionality between cumulated variables. It may be necessary to add a y-intercept to this relation that becomes B = c + bDa , where c is the biomass of an individual before it reaches the height at which diameter is measured (e.g. 1.30 m if D is measured at 1.30 m). In Simple words Regression is a method to mathematically formulate relationship between variables that in due course can be used to estimate, interpolate and extrapolate. Suppose we want to estimate the weight of trees, which is influenced by height, DBH, wood density, form factor, tree management (pruning/thinning) etc. Here, Weight is the predicted variable. height, DBH, wood density, form factor, tree management are predictor variables. 3.1 Assumptions of Regression analysis Regression analysis makes several major assumptions to ensure the validity and reliability of the results. Let's discuss these assumptions in the context of a regression analysis with tree diameters as independent parameters and tree biomass as the dependent parameter: ➢ Linearity: The relationship between the independent variable (tree diameters) and the dependent variable (tree biomass) should be linear. In other words, the effect of tree diameters on tree biomass should be constant and proportional. In a linear regression analysis, we assume that an increase in tree diameters will lead to a proportional increase in tree biomass. For instance, if the diameter of a tree doubles, the biomass should also double. ➢ Independence: The observations used in the regression analysis should be independent of each other. Each data point's value should not be influenced by or dependent on the value of another data point. Example: In a dataset with tree diameters and biomass measurements, each tree's diameter and biomass should be measured independently, without being influenced by other trees' measurements. ➢ Homoscedasticity: The residuals (the differences between the observed tree biomass and the predicted tree biomass based on diameters) should have constant variance across all levels of tree diameters. Example: Homoscedasticity means that the spread of the residuals around the regression line should be consistent for all tree diameters. In other words, the variability of the errors should not change as tree diameters increase or decrease.