Evaluating the Challenges and Strategies for SDG.15.3: A Case Study of Goa State

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The Sustainable Development Goals (SDGs) thus become an important part of our collective efforts. The present study aims at assessing SDG 15: Life on land (SDG 15.3.1) for India’s state of Goa following the good practices guidelines of the United Nations to Combat Desertification (UNCCD) by employing the “Trends. Earth” plug-in in QGIS software. The assessment of the indicator is based on three sub-indicators: soil carbon, productivity, and land cover. The study yields a total land area improvement of 81.16% to the overall area of the state. The individual sub-indicator of productivity depicted an improvement of 85.14%, whereas 8.70% area was with stable productivity. Other sub-indicators of land cover showed a larger portion of land as stable, with 90.26% and a minor improvement of 5.20%. The soil organic carbon depicted an improvement of 0.54% with a maximum stable soil organic carbon of 94.91%. Several factors, such as better resource utilisation, improved governance practices, and a more sustainable approach to development, have affected the overall improvement of the indicators. Over the years, these improvements have allowed the state to grow as a sustainable tourist attraction for Indian nationals and foreign visitors. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The development that tends to meet the present needs without compromising the needs of future generations is termed sustainable development. Members of The United Nations (UN) 2015 adopted the Agenda 2030 focusing on 17 Sustainable Development Goals (SDGs) encompassing action plans to counter poverty and hunger, elevate the level of education and health, preserve and protect the environment, tackle climate change all along with economic growth [ 1 ]. These 17 SDGs consist of targets which consist of indicators measured by various means to track the progress toward achieving the 2030 Agenda. There are 231 such Indicators related to 17 SDGs [ 2 ]. The United Nations Sustainable Development Goal (SDG) 15, known as 'Life on Land,' aims to safeguard, rehabilitate, and encourage sustainable utilization of terrestrial ecosystems and the sustainable management of forests. Moreover, it seeks to combat desertification, reverse land degradation, and terminate biodiversity loss. Specifically, Target 15.3 is focused on reducing desertification and reversing this degradation, including land affected by desertification, drought, and floods, and striving towards achieving a land degradation-neutral world by 2030. The definition of land degradation adopted by parties of the United Nations Convention to Combat Desertification (UNCCD) for SDG Indicator 15.3.1 demarcates the decline or depletion in the productivity and complexity of rain-fed cropland, irrigated cropland, or range, pasture, forest, and woodlands due to a mixture of pressures, including land use and management practices [ 3 ]. Based on this definition, Indicator 15.3.1 is calculated using three sub indicators, including changes in land cover, land productivity and carbon stocks represented as soil organic carbons [ 4 ]. Over the years, based on these guidelines, different studies have been conducted worldwide, identifying several causes leading to land degradation. A study of Mozambique in 2020 highlighted the assessment of land productivity change at a national scale. The major factors that governed the land degradation in Mozambique were significantly due to human activity, resulting in climate variability alone being responsible for a 19.7% decrease [ 5 ]. Also, these studies have been beneficial in identifying other important parameters. Study of Tunisia in 2023 Study for Tunisia made use of Plant Functional Type (PFT) classifications to assess the patterns of cereal croplands, improving the understanding of land degradation process and its dynamics that aid the identification of necessary actions by focusing on specific plant functional characteristics [ 6 ]. Though ample attempts have been made to study SDG indicator 15.3.1 worldwide, very little focus has been placed on assessing these indicators for individual Indian states. Also, studies have highlighted that developing countries lack periodic data on SDG Indicator 15.3.1 [ 7 ]. In 2022, initial steps were taken to Assess land degradation factors for the Indian state of Assam. Suggested conduction of SDG 15.3.1 studies for other states of India, vulnerable to changes as a reason for development and climatic actions [ 8 ]. Similarly, our study aims to study the indicator 15.3.1 goals in the framework of land neutrality in the State of Goa for 2001–2021. This study enables a step forward in performing a micro-level analysis of the Indian subcontinent to determine land neutrality and suggest alternative shifts to prevent land degradation in upcoming years. 2. Area of study The region of study has been selected as the Goa state of India. Goa is amongst the top performers in the NITI Aayog National Indicator Framework (NIF) on Sustainable development goals (SDG), enabling the monitoring of the advancement of SDGs on a national scale [ 9 ]. Goa, located approximately between 14.53° N latitude and 74.12° E longitude, is situated on India's west coast and bordered by the Arabian Sea to the west and the Western Ghats to the east. The rivers in Goa have their source in the Western Ghats and are crucial for various activities in the region. These rivers are essential as they carry a significant volume of water, particularly during the monsoon season. The availability of water in these rivers is fundamental for the sustenance of all activities. With a local population of 14.5 lakhs, Goa also caters to an additional 28–30 lakh tourists annually by providing water resources. Despite experiencing substantial rainfall from June to September, certain areas in Goa often encounter water scarcity [ 10 ]. 3. Data source and software The study employs QGIS, which is open-source software, utilising the “Trends.Earth” plugin that works on the guidelines of UNCCD. The Global Environment Facility (GEF) is a consortium of various financial mechanisms to address the challenges posed by biodiversity decline, climate variability, and environmental contamination while promoting the sustainability of terrestrial and marine ecosystems. The GEF facility also funded the project “Enabling the use of global data sources to access the monitor land degradation at multiple scales”, which resulted in the creation of Trends. Earth facility. This allows it to directly fetch data from various international observatories, including the European Space Agency Climate Change data (ESA CCI – LC) and Land Productivity Dynamics (LPD) of the Joint Research Centre (JRC) European Commission [ 11 ]. 4. Methodology The study utilizes the methodology suggested by the good practice guidelines (GPG) of UNCCD to calculate the SDG 15.3.1 indicator. This includes binary quantification of sub-indicators for the base period and analysis for the reporting period. The overall SDG 15.3.1 indicator is achieved based on the One-out-all-out method (1OAO), where a negative trend in any sub-indicator results in overall degradation [ 12 ]. Figure 2 represents the method of calculating the land degradation, yielding the SDG 15.3.1 indicator. This indicator helps nations to regularly monitor their commitment toward achieving Land Neutrality. 4.1 Land Cover Land cover informs the functional relationship between terrain, climate, and soils, providing biophysical insights into the environment and drivers of change [ 13 ]. Land cover can be classified into 7 classes, for both the maps as tree covered (TC), grassland (GL), cropland (CL), wetland (WL), artificial area (AL), other land (BL), and water body (WB). A positive change depicts improvement (+ sign), a negative change depicts degradation (- sign), and no change in land condition depicts stability (zero). The alteration in land cover necessitates the utilisation of land cover maps encompassing the specified study region for the beginning and end years as an essential input. Both maps must exhibit high precision to generate reliable and comparable outcomes. Trends-Earth utilises the ESA CCI LC data; alternatively, Copernicus global land service cover at 100m (CGLS-LC 100) can be employed. Table 1 generates the output for land cover change for each pixel in the map. 4.2 Land Productivity Land productivity is the biological productive capacity of the land, the source of all the food, fibre and fuel that sustains humans [ 14 ]. The land productivity over a large area can be assessed using Net Primary Productivity (NPP), defined as the organic matter production rate after photosynthesis and autotropic respiration over a given period (kg/ha/yr.). Remote sensing can be a very useful means to obtain the NPP. Trends.Earth utilises Land Productivity Dynamics (LPD) data from the Joint Research Centre (JRC) European Commission to obtain NPP globally. Alternatively, the MODIS MOD13 Q1–250m vegetation index data and Sentinel 2 Multispectral Instrument (MSI) can be used to prepare the Normalized Difference Vegetation Index (NDVI), which helps calculate NPP. 4.3 Carbon Stocks Carbon stocks are calculated based on metric Soil Organic Carbon (SOC), which refers to organic matter's carbon components up to the top 0–30 cm layer present in soil [ 15 ]. The computation of change in SOC had certain limitations regarding spatial volatility in soil properties, time, and cost. Trends-Earth thus utilises the Land cover/SOC method based on data of Soil Grids 250m to locate the potentially degraded area. Land cover is similarly classified into seven components to calculate the change in carbon stocks. Table 2 represents the carbon conversion coefficient for each global climatic region. Table 2 Carbon regeneration coefficient Land Cover categories at reporting period TC GL CL WL AL OL WB Land cover categories at the base year. TC 1.00 1.00 F 1.00 0.10 0.10 1.00 GL 1.00 1.00 F 1.00 0.10 0.10 0.10 CL 1/f 1/f 1.00 1/0.71 0.10 0.10 1.00 WL 1.00 1.00 0.71 1.00 0.10 0.10 1.00 AL 2.00 2.00 2.00 2.00 1.00 1.00 1.00 OL 2.00 2.00 2.00 2.00 1.00 1.00 1.00 WB 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Here f represents different coefficient depending on regional climate mainly Temperate Dry (f = 0.80), Temperate Moist (f = 0.69), Tropical Dry (f = 0.58), Tropical Moist (f = 0.48), and Tropical Montane (f = 0.64). 4.4 SDG 15.3.1 Indicator The combined result of these three indicators results in obtaining the SDG 15.3.1 indicator based on the one-out-all-out (1OAO) method. This means degradation or negative sign in any of the three indicators, namely land cover, land productivity, and soil organic carbon stocks, will result in output as overall degradation of SDG 15.3.1 indicator. Table 3 presents the aggregate process based on the 1OAO approach. The ↑ indicate improving condition of sub-indicator, = for stable and ↓ for declining. Table 3 SDG 15.3.1 indicator calculation based on 1OAO approach Land Cover Land Productivity SOC stocks The combined output of SDG 15.3.1 Indicator ↑ ↑ ↑ ↑ ↑ ↑ = ↑ ↑ ↑ ↑ ↑ ↑ = ↑ ↑ ↑ = = ↑ ↑ = ↓ ↓ ↑ ↓ ↑ ↓ ↑ ↓ = ↓ ↑ ↓ ↓ ↓ = ↑ ↑ ↑ = ↑ = ↑ = ↑ ↓ ↓ = = ↑ ↑ = = = = = = ↓ ↓ = ↓ ↑ ↓ = ↓ = ↓ = ↓ ↓ ↓ ↓ ↑ ↑ ↓ ↓ ↑ = ↓ ↓ ↑ ↓ ↓ ↓ = ↑ ↓ ↓ = = ↓ ↓ = ↓ ↓ ↓ ↓ ↑ ↓ ↓ ↓ = ↓ ↓ ↓ ↓ ↓ ↑ Improvement ↓ Degradation = No Change 5. Result and Discussion The three-sub indicator i.e. Land cover, land productivity, and SOC stocks were analysed to study land degradation and ultimately report the SDG 15,3,1 indicator for the Indian state of Goa. “Trends.Earth, an open-source plugin of QGIS software, was used to perform the analysis over the period of 2001 to 2021, with the target year as 2021. The results for individual sub-indicators are discussed below. 5.1 Land Cover Shift For the study period, the land cover shift shows degradation, stable, and improving trends for different components. The study depicts a positive shift in tree cover, with an improvement of 9%. An increase of 300% is noted in Artificial build-ups. Components like grasslands and cropland showed negative or declining trends. Table 4 presents the shift in land cover from 2001 to 2021. Table 4 The land cover trend from the initial period to the final period Land cover categories Initial area (sq. km) Final area (sq. km) Change in area (sq. km) Change in the area (per cent) TC 1,644.20 1,789.11 144.91 9% GL 185.64 136.75 -48.88 -26% CL 1,365.00 1,135.99 -229.01 -17% WL 33.26 37.04 3.79 11% AL 42.12 168.42 126.30 300% BL 0.00 0.00 0.00 0% WB 150.89 153.79 2.90 2% Figure 3 below represents the land cover map for the period between 2001 and 2021, which shows the major increase of artificial cover from 42.12 sq. km to 168.42 sq. km. The land cover transition is mostly along the coastal regions, which act as a major tourist attraction for the state of Goa. This transition is clearly depicted in Fig. 3 . Table 5 Land Cover by Type of Land Cover Transition in km 2 Land Cover categories at reporting period TC GL CL WL AL OL WB Land cover categories at the base year. TC 1,612.94 7.72 15.37 0.82 3.79 0.00 3.57 GL 45.24 127.78 1.86 2.08 8.47 0.00 0.22 CL 130.93 1.19 1,118.54 2.45 111.90 0.00 0.00 WL 0.00 0.07 0.00 31.63 1.19 0.00 0.37 AL 0.00 0.00 0.00 0.00 42.12 0.00 0.00 OL 0.00 0.00 0.00 0.00 0.00 0.00 0.00 WB 0.00 0.00 0.22 0.07 0.97 0.00 149.63 Table 5 presents the land area by type of land cover transition for each component from the initial time of 2001 to the final reporting period of 2021. The artificial cover increase is the outcome of the growing tourism industry of Goa. The concentration of tourism growth in Goa is primarily focused on the regions of Bardez, Salcete, Tiswadi, and Marmagao. These areas account for approximately 66 per cent of Goa's Gross State Domestic Product (GSDP), making them the most economically developed regions in the state [ 16 ]. The infrastructure in these regions has played a crucial role in their development, enabling them to achieve high population densities and economic prosperity. The small-scale industry sector in these regions is particularly significant, as it encompasses 70% of small-scale industries, attracts 78.5% of capital investment in such industries, and provides employment opportunities for 68% of the workforce [ 16 ]. These regions' strategic positioning and focus on tourism and industry have contributed significantly to Goa's overall economic growth and development. Additionally, supportive infrastructure has further facilitated establishing and expanding businesses in these key areas, driving economic progress and creating employment opportunities for the local population. Overall, the regions of Bardez, Salcete, Tiswadi, and Marmagao stand out as vital hubs of economic activity and growth within the state of Goa, playing a crucial role in shaping its economic landscape. Overall, land covers a maximum area of 3,088.05 sq. km, with stable land cover accounting for 90.26% of the total land area. Also, 5.20% of land showed improved land cover with an area of 178.02 sq. km. This data is represented in Table 6 Table 6 Land Cover change Area in km 2 % age of total land area Total land area under study 3,421.1 100.00% Land area with improved land cover 178.02 5.20% Land area with stable land cover 3,088.05 90.26% Land area with degraded land cover 155.03 4.53% 5.2 Land productivity shift The sub-indicator of land productivity has shown a significant improvement. The land cover transition is employed to study productivity individually as improving, stable, stressed, moderate decline, decline, and non-available data. SDG indicator areas are considered to be improved if they have "Improving" productivity, stable if they have "stable" or "stressed" productivity, and degraded if they are classified as in "moderate decline" or "declining." The tables below present this individual output. Table 7 Improved productivity for land cover transition Land Cover categories at reporting period TC GL CL WL AL OL WB Total Land cover categories at the base year. TC 1,485.24 7.42 14.26 0.52 2.82 0.00 0.07 1,510.33 GL 42.56 106.99 1.71 1.86 5.12 0.00 0.07 158.32 CL 119.94 0.37 972.54 1.86 69.06 0.00 0.00 1,163.76 WL 0.00 0.07 0.00 18.56 0.59 0.00 0.00 19.22 AL 0.00 0.00 0.00 0.00 27.93 0.00 0.00 27.93 OL 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 WB 0.00 0.00 0.22 0.00 0.30 0.00 32.74 33.26 Total 1,647.74 114.86 988.73 22.79 105.83 0.00 32.89 2,912.83 Table 8 Stable productivity for land cover transition Land Cover categories at reporting period TC GL CL WL AL OL WB Total Land cover categories at the base year. TC 109.21 0.22 0.74 0.22 0.67 0.00 0.00 111.07 GL 2.45 15.51 0.15 0.15 2.45 0.00 0.00 20.71 CL 8.24 0.67 100.80 0.45 27.02 0.00 0.00 137.18 WL 0.00 0.00 0.00 5.79 0.45 0.00 0.00 6.24 AL 0.00 0.00 0.00 0.00 8.54 0.00 0.00 8.54 OL 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 WB 0.00 0.00 0.00 0.00 0.30 0.00 9.14 9.43 Total 119.91 16.41 101.69 6.61 39.43 0.00 9.14 293.18 Table 9 Stressed productivity for land cover transition Land Cover categories at reporting period TC GL CL WL AL OL WB Total Land cover categories at the base year. TC 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 GL 0.00 0.45 0.00 0.00 0.07 0.00 0.00 0.52 CL 0.00 0.00 0.67 0.00 0.89 0.00 0.00 1.56 WL 0.00 0.00 0.00 0.96 0.00 0.00 0.00 0.96 AL 0.00 0.00 0.00 0.00 0.07 0.00 0.00 0.07 OL 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 WB 0.00 0.00 0.00 0.00 0.00 0.00 1.41 1.41 Total 0.00 0.45 0.67 0.96 1.04 0.00 1.41 4.53 Table 10 Moderate declining productivity for land cover transition Land Cover categories at reporting period TC GL CL WL AL OL WB Total Land cover categories at the base year. TC 10.62 0.07 0.37 0.07 0.30 0.00 0.00 11.43 GL 0.22 2.97 0.00 0.07 0.37 0.00 0.00 3.63 CL 1.78 0.00 22.04 0.00 8.46 0.00 0.00 32.29 WL 0.00 0.00 0.00 1.11 0.00 0.00 0.07 1.19 AL 0.00 0.00 0.00 0.00 3.05 0.00 0.00 3.05 OL 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 WB 0.00 0.00 0.00 0.00 0.07 0.00 0.89 0.97 Total 12.62 3.04 22.41 1.26 12.25 0.00 0.97 52.55 Table 11 Declining productivity for land cover transition Land Cover categories at reporting period TC GL CL WL AL OL WB Total Land cover categories at the base year. TC 5.12 0.00 0.00 0.00 0.00 0.00 0.00 5.12 GL 0.00 1.26 0.00 0.00 0.37 0.00 0.00 1.63 CL 0.97 0.15 15.00 0.00 5.49 0.00 0.00 21.60 WL 0.00 0.00 0.00 2.15 0.00 0.00 0.15 2.30 AL 0.00 0.00 0.00 0.00 2.23 0.00 0.00 2.23 OL 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 WB 0.00 0.00 0.00 0.00 0.00 0.00 0.97 0.97 Total 6.09 1.41 15.00 2.15 8.09 0.00 1.11 33.85 Table 12 No data of productivity for land cover transition Land Cover categories at reporting period TC GL CL WL AL OL WB Total Land cover categlories at the base year. TC 2.75 0.00 0.00 0.00 0.00 0.00 3.49 6.24 GL 0.00 0.59 0.00 0.00 0.07 0.00 0.15 0.82 CL 0.00 0.00 7.50 0.15 0.96 0.00 0.00 8.61 WL 0.00 0.00 0.00 3.05 0.15 0.00 0.15 3.34 AL 0.00 0.00 0.00 0.00 0.30 0.00 0.00 0.30 OL 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 WB 0.00 0.00 0.00 0.07 0.30 0.00 104.49 104.86 Total 2.75 0.59 7.50 3.27 1.78 0.00 108.28 124.17 Figure 3 represent change in productivity for the state of Goa with major area as improving with minor area as declining. The major trends of declining productivity are near Corlim region, consisting of land cover transition and an increase in artificial built-ups to support tourist accommodation and industrial estates. Further, the land area of Goa, with increased productivity, forms 85.14% of the total available land, which is nearly 2,912.83 sq km. This is represented in the table below. Table 13 Overall productivity change Area in km 2 % age of total land area Total land area under assessment 3,421.1 100.00% Land area with improved productivity 2,912.83 85.14% Land area with stable productivity 297.71 8.70% Land area with degraded productivity 86.40 2.53% 5.3 Soil Organic Carbon (SOC) Stocks Trend SOC stocks are difficult to track due lack of data along with time and cost-consuming methods to study the change. SOC plays a fundamental role in the preservation of soil health, the functionality of ecosystems, and the guarantee of global food security. It is the primary measure to evaluate the above and below-ground carbon reserves. Compared to other indicators, alterations in SOC reserves linked to modifications in land utilisation and practices necessitate extended monitoring periods to assess the impact accurately. These variations, although significant, are relatively minor when juxtaposed with the vast quantities of carbon stored in the soil and the inherent fluctuations within these reserves. For individual class transition changes in SOC is tabulated. Based on the difference, the change in SOC is plotted, representing a close association with changes in land cover trends and increased artificial built-ups. The overall SOC for the observed period shows stable behaviour, with more than 94.91%. However, there was a higher negative trend in comparison to improved SOC. The degraded SOC accounted for 4.27% against the improvement of 0.54%, and an overall negative change of -1.60% from the initial to the final year was observed. Table 14 SOC trend from initial year to final year Initial soil organic carbon (tonnes/ha) Final soil organic carbon (tonnes/ha) Initial area (sq. km) Final area (sq. km) Initial soil organic carbon (tonnes) Final soil organic carbon (tonnes) Change in soil organic carbon (tonnes) Change in soil organic carbon (percent) TC 84.08 83.97 1,644 1,789 1,38,25,037 1,50,23,510 11,98,472.30 9% GL 81.47 82.00 186 137 15,12,425 11,21,350 -3,91,074.76 -26% CL 74.63 74.55 1,365 1,136 1,01,87,068 84,68,279 -17,18,788.70 -17% WL 91.87 94.59 33 37 3,05,543 3,50,402 44,858.59 15% AL 71.80 44.59 42 168 3,02,399 7,50,951 4,48,552.20 148% BL 0.00 0.00 0 0 0 0 0.00 0% Total : 3,270.22 3,267.32 2,61,32,472.05 2,57,14,491.67 -4,17,980.38 Table 15 SOC trend from initial to final year Area in km 2 % age of total land area Total land area : 3,270.2 100.00% Land area with improved soil organic carbon : 17.59 0.54% Land area with stable soil organic carbon : 3,103.75 94.91% Land area with degraded soil organic carbon : 139.59 4.27% Land area with no data for soil organic carbon : 9.28 0.28% Percent change in soil organic carbon storage from initial year to final year : − 1.60% Further, this change can be studied using the initial and final year map for SOC, which is represented below in Fig. 5 . 5.4 SDG 15.3.1 Indicator As suggested by the Good Practice Guidelines (GPG) of UNCCD, the 1OAO approach, as stated earlier, is used to obtain the SDG 15.3.1 indicator. Figure 5 shows the trends of the overall SDG 15.3.1 report from the initial to final years. This highlights the major degradation along the coastal regions associated with declining productivity and land cover change patterns. A total of 2,776.66 sq. km of area was observed as improving, resulting in the heading of land degradation neutrality covering 81.16% of the total land. However, a degradation of 6.82% is observed for the area of 233.34 sq km, along with a stable area of 7% covering 239.42 sq km. The change in land use pattern is plotted below. The bar chart represents the land cover trend over the initial to final year. Table 16 SDG 15.3.1 indicator Area in km 2 % age of total land area Total land area under study 3,421.1 100.00% Land area improved 2,776.66 81.16% Land area stable 239.42 7.00% Land area degraded 233.34 6.82% Land area with no data 171.68 5.02% 5.5 Integrated Normalized Difference Vegetation (NDVI) Index One widely used method to estimate NPP is by the mean of NDVI. The Normalized Difference Vegetation Index (NDVI) is a mathematical calculation that involves the comparison of near-infra-red (NIR) wavelengths, which are commonly reflected by healthy green plants, and red wavelengths that fall within the active spectrum and are generally absorbed by chlorophyll in healthy green plants. This index is a useful tool for remotely sensing vegetation health and monitoring changes in vegetation over time. The NDVI is suggested as the established vegetation index for evaluating SDG Indicator 15.3.1 when no evidence suggests that another index would provide greater accuracy in this context. It is widely recognized that the NDVI has proven to be a reliable and effective tool for assessing vegetation health and monitoring changes in land cover over time [ 17 ]. The integrated NDVI to time graph is plotted based on MODIS MOD13Q1 annually at a spatial resolution of 250m. A linear regression model is depicted in the graph, with the coefficient of determination value r 2 = 0.81, providing a visual representation of the upward trajectory observed in the integrated NDVI index spanning from the commencement year of 2001 to the culmination year of 2021, indicating a consistent growth trend over the analyzed period. Government policy approach toward sustainable tourism contributes toward improvement in SDG and integrated NDVI over the observed period. 6. Conclusion In the present study, we have studied the SDG Indicator 15.3.1 (Proportion of degraded land over the total land area) for the Indian state of Goa, as recommended by good practice guidelines of the United Nations Convention to Combat Desertification (UNCCD). The study was conducted using QGIS, an open-source software with the help of the “Trends.Earth” plugin for analysis. Three sub-indicators (Land cover, Land Productivity and Soil Organic Carbon stocks) were studied individually from 2001 to 2021. Data from different sources was utilised for this purpose, including the European Space Agency Climate Change Initiative land cover data (ESA CCI- LC) to study trends in land cover, Soil Grids 250m were used for SOC stocks, and an integrated NDVI was generated using MODIS MOD13Q1–250m. The overall SDG indicator 15.3.1 was calculated based on a one-out-all-out (1OAO) approach where degradation in an individual sub-indicator resulted in the decline of the SDG Indicator as a whole. The study concludes with overall SDG Indicator 15.3.1 for the state of Goa, depicting an improvement of 81.16% covering 2,776.66 sq km. However, a degraded area of 6.82% throughout the 233.34 sq km area was observed mostly near the coastal regions due to a shift in land use pattern and declining productivity over the years. Specifically, the state showed growth of 300% in artificial land cover from 42.12 sq km in the initial years to 168.43 sq km in the reporting year. Further, the major decline in grassland and cropland was observed due to varying land use patterns during the study period. Over the years, the state of Goa has shown an increase in productivity of 85.14%, with a minor portion of degraded productivity accounting for 2.53%. Carbon stocks depicted stable trends, with 94.91% of land area with stable soil organic carbon. The NDVI showed an increasing pattern from 2001 to 2021, which can result from sustainable development in the tourism sector, which positively impacted the economy and the environment. Declarations Funding : This research did not receive a specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Conflicts of Interest/Competing Interests: The authors declare that they have no known competing academic/financial interests or personal relationships that could have appeared to influence the work reported in this paper. Authors' contributions: 1) Data collection and analysis: Harshit Dubey 2) Organizing data and concluding results: Harshit Dubey and Naimish Bhatt 3) Paper writing and review: Harshit Dubey, Naimish Bhatt and Shobhit Chaturvedi 4) Idea and Paper verification: Naimish Bhatt and Ravi Kant Acknowledgements: The Authors would like to thank the support of the open-source software QGIS, which supported us with relevant data for conducting this research. References Desa, U. (2018). Transforming Our World: The 2030 Agenda for Sustainable Development. In Springer eBooks. https://doi.org/10.1891/9780826190123.ap02 . United Nations Statistics Division (n.d.). SDG Indicators — SDG Indicators. https://unstats.un.org/sdgs/indicators/indicators-list/ . Dooley, E. E. (2002). EHPNET: United Nations Convention to Combat Desertification. Environmental Health Perspectives , 110 (2). https://doi.org/10.1289/ehp.110-a77 . Markos, A., Sims, N., & Giuliani, G. (2022b). Beyond the SDG 15.3.1 Good Practice Guidance 1.0 using the Google Earth Engine platform: developing a self-adjusting algorithm to detect significant changes in water use efficiency and net primary production. Big Earth Data , 7 (1), 59–80. https://doi.org/10.1080/20964471.2022.2076375 . Montfort, F., Bégué, A., Leroux, L., Blanc, L., Gond, V., Cambule, A., Remane, I. D., & Grinand, C. (2020). From land productivity trends to land degradation assessment in Mozambique: Effects of climate, human activities and stakeholder definitions. Land Degradation & Development , 32 (1), 49–65. https://doi.org/10.1002/ldr.3704 . Cherif, I., Kolintziki, E., & Alexandridis, T. (2023). Monitoring of Land Degradation in Greece and Tunisia Using Trends.Earth with a Focus on Cereal Croplands. Remote Sensing , 15 (7), 1766. https://doi.org/10.3390/rs15071766 . Giuliani, G., Chatenoux, B., Benvenuti, A., Lacroix, P., Santoro, M., & Mazzetti, P. (2020). Monitoring land degradation at national level using satellite Earth Observation time-series data to support SDG15 – exploring the potential of data cube. Big Earth Data , 4(1), 3–22. https://doi.org/10.1080/20964471.2020.1711633 . Nath, A., & Nath, A. J. (2022). Assessing land degradation using SDG 15.3.1 Indicators: case study from Climate-Vulnerable Assam State of India. In Land Degradation Neutrality: Achieving SDG 15 by Forest Management (pp. 117–129). https://doi.org/10.1007/978-981-19-5478-8_7 . Sustainable development goals (2023). Press Information Bureau March 27). https://pib.gov.in/PressReleasePage.aspx?PRID=1911165 . Panandiker, A. P., Mello, L., Venkatesh, B., Kotha, M., Chachadi, A. G., & CLIMATE TRENDS OVER GOA: STATISTICAL ANALYSIS OF OBSERVATIONS AND REANALYSIS DATASETS FOR BETTER WATER MANAGEMENT. (2020).. J Indian Water Resour Soc , 40(3 & 4). https://iwrs.org.in/journal/juloct2020/2jul.pdf . Giuliani, G., Chatenoux, B., Benvenuti, A., Lacroix, P., Santoro, M., & Mazzetti, P. (Jan. 2020). Monitoring land degradation at national level using satellite Earth Observation time-series data to support SDG15 – exploring the potential of data cube, Big Earth Data , 4, 1, 3–22, doi: 10.1080/20964471.2020.1711633 . Markos, N., Sims, & Giuliani, G. (2023). Beyond the SDG 15.3.1 Good Practice Guidance 1.0 using the Google Earth Engine platform: developing a self-adjusting algorithm to detect significant changes in water use efficiency and net primary production, Big Earth Data, vol. 7, no. 1, pp. 59–80, Jan. 10.1080/20964471.2022.2076375 . Wulder, M. A., Coops, N. C., Roy, D. P., & White, J. C. (2018). Land cover 2.0. International Journal of Remote Sensing , 39 (12), 4254–4284. https://doi.org/10.1080/01431161.2018.1452075 . Sims, N. C., et al. (Feb. 2019). Developing good practice guidance for estimating land degradation in the context of the United Nations Sustainable Development Goals. Environmental Science & Policy , 92 , 349–355. 10.1016/j.envsci.2018.10.014 . Lorenz, K., Lal, R., & Ehlers, K. (2019). Soil organic carbon stock as an indicator for monitoring land and soil degradation in relation to United Nations’ Sustainable Development Goals. Land Degradation & Development , 30 (7), 824–838. https://doi.org/10.1002/ldr.3270 . Betkekar, M. (2019). Tourist status in Goa: economy and problems. EPRA International Journal of Economic and Business Review, Volume-7(Issue-1), e-ISSN: 2347–9671| p-ISSN : 2349 – 0187. https://eprajournals.com/IJES/article/7880/download . United Nation Convention to Combat Desertification (2021). Good Practice Guidance-SDG Indicator 15.3.1 (version 2.0). UNCCD. https://www.unccd.int/sites/default/files/relevant-links/2021-03/Indicator_15.3.1_GPG_v2_29Mar_Advanced-version.pdf . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 29 Jun, 2024 Submission checks completed at journal 28 Jun, 2024 First submitted to journal 28 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4652666","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":320464370,"identity":"41fdff87-7644-48ca-8483-f06f0d1b89af","order_by":0,"name":"Harshit Dubey","email":"","orcid":"","institution":"Pandit Deendayal Energy University","correspondingAuthor":false,"prefix":"","firstName":"Harshit","middleName":"","lastName":"Dubey","suffix":""},{"id":320464372,"identity":"bf725a76-6b65-4bc2-b1ab-d0ec30fc8aaa","order_by":1,"name":"Naimish 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study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4652666/v1/8fa91da056bc675db3b038d9.png"},{"id":60854802,"identity":"001521dd-8777-4cab-87a9-0571960995b1","added_by":"auto","created_at":"2024-07-22 21:57:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":266262,"visible":true,"origin":"","legend":"\u003cp\u003eOne out all out method for SDG 15.3.1\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4652666/v1/beb669d69ec9563502c93be7.png"},{"id":60854801,"identity":"5dc93e47-a732-4ac9-a5f7-45c88acf0de1","added_by":"auto","created_at":"2024-07-22 21:57:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":411153,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Land cover 2001 (b) Land cover 2021 (c) Land cover change 2001-2021\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4652666/v1/2da239527cafdbab5116f3ab.png"},{"id":60854315,"identity":"24d125bc-3780-4e11-98e1-fd8d06b73a73","added_by":"auto","created_at":"2024-07-22 21:49:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":576014,"visible":true,"origin":"","legend":"\u003cp\u003e(a) SOC 2001 (b) SOC 2021 (c) SOC change 2001-2021 tons/ ha\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4652666/v1/8243c333d1a5679d12b0c707.png"},{"id":60854927,"identity":"f105fd2c-2201-474b-89b9-911515628f5d","added_by":"auto","created_at":"2024-07-22 22:05:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":256120,"visible":true,"origin":"","legend":"\u003cp\u003eSDG Indicator 15.3.1 Change from 2001 – 2021\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4652666/v1/c1f84d6e743d5bda35a578cb.png"},{"id":60855146,"identity":"7608b698-f08c-4f0b-8c96-b74d0833347f","added_by":"auto","created_at":"2024-07-22 22:13:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":83161,"visible":true,"origin":"","legend":"\u003cp\u003eLand cover change in sq. km\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4652666/v1/dd13206a9bbb8924ed563a5e.png"},{"id":60854317,"identity":"1eacbdd2-d430-4373-b7a5-ef15479bc8f0","added_by":"auto","created_at":"2024-07-22 21:49:03","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":85351,"visible":true,"origin":"","legend":"\u003cp\u003eNDVI trends\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4652666/v1/52ebc3ade99a47479a7c0522.png"},{"id":60855274,"identity":"da455067-d450-4a01-b6a7-f39f032515e9","added_by":"auto","created_at":"2024-07-22 22:21:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3225525,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4652666/v1/9f24f2c7-aba2-4853-9bdb-da5179cf5180.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating the Challenges and Strategies for SDG.15.3: A Case Study of Goa State","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe development that tends to meet the present needs without compromising the needs of future generations is termed sustainable development. Members of The United Nations (UN) 2015 adopted the Agenda 2030 focusing on 17 Sustainable Development Goals (SDGs) encompassing action plans to counter poverty and hunger, elevate the level of education and health, preserve and protect the environment, tackle climate change all along with economic growth [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These 17 SDGs consist of targets which consist of indicators measured by various means to track the progress toward achieving the 2030 Agenda. There are 231 such Indicators related to 17 SDGs [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The United Nations Sustainable Development Goal (SDG) 15, known as 'Life on Land,' aims to safeguard, rehabilitate, and encourage sustainable utilization of terrestrial ecosystems and the sustainable management of forests. Moreover, it seeks to combat desertification, reverse land degradation, and terminate biodiversity loss. Specifically, Target 15.3 is focused on reducing desertification and reversing this degradation, including land affected by desertification, drought, and floods, and striving towards achieving a land degradation-neutral world by 2030.\u003c/p\u003e \u003cp\u003eThe definition of land degradation adopted by parties of the United Nations Convention to Combat Desertification (UNCCD) for SDG Indicator 15.3.1 demarcates the decline or depletion in the productivity and complexity of rain-fed cropland, irrigated cropland, or range, pasture, forest, and woodlands due to a mixture of pressures, including land use and management practices [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Based on this definition, Indicator 15.3.1 is calculated using three sub indicators, including changes in land cover, land productivity and carbon stocks represented as soil organic carbons [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOver the years, based on these guidelines, different studies have been conducted worldwide, identifying several causes leading to land degradation. A study of Mozambique in 2020 highlighted the assessment of land productivity change at a national scale. The major factors that governed the land degradation in Mozambique were significantly due to human activity, resulting in climate variability alone being responsible for a 19.7% decrease [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Also, these studies have been beneficial in identifying other important parameters. Study of Tunisia in 2023 Study for Tunisia made use of Plant Functional Type (PFT) classifications to assess the patterns of cereal croplands, improving the understanding of land degradation process and its dynamics that aid the identification of necessary actions by focusing on specific plant functional characteristics [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThough ample attempts have been made to study SDG indicator 15.3.1 worldwide, very little focus has been placed on assessing these indicators for individual Indian states. Also, studies have highlighted that developing countries lack periodic data on SDG Indicator 15.3.1 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In 2022, initial steps were taken to Assess land degradation factors for the Indian state of Assam. Suggested conduction of SDG 15.3.1 studies for other states of India, vulnerable to changes as a reason for development and climatic actions [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSimilarly, our study aims to study the indicator 15.3.1 goals in the framework of land neutrality in the State of Goa for 2001\u0026ndash;2021. This study enables a step forward in performing a micro-level analysis of the Indian subcontinent to determine land neutrality and suggest alternative shifts to prevent land degradation in upcoming years.\u003c/p\u003e"},{"header":"2. Area of study","content":"\u003cp\u003eThe region of study has been selected as the Goa state of India. Goa is amongst the top performers in the NITI Aayog National Indicator Framework (NIF) on Sustainable development goals (SDG), enabling the monitoring of the advancement of SDGs on a national scale [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Goa, located approximately between 14.53\u0026deg; N latitude and 74.12\u0026deg; E longitude, is situated on India's west coast and bordered by the Arabian Sea to the west and the Western Ghats to the east. The rivers in Goa have their source in the Western Ghats and are crucial for various activities in the region. These rivers are essential as they carry a significant volume of water, particularly during the monsoon season. The availability of water in these rivers is fundamental for the sustenance of all activities. With a local population of 14.5 lakhs, Goa also caters to an additional 28\u0026ndash;30 lakh tourists annually by providing water resources. Despite experiencing substantial rainfall from June to September, certain areas in Goa often encounter water scarcity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e"},{"header":"3. Data source and software","content":"\u003cp\u003e \u003c/p\u003e \u003cp\u003eThe study employs QGIS, which is open-source software, utilising the \u0026ldquo;Trends.Earth\u0026rdquo; plugin that works on the guidelines of UNCCD. The Global Environment Facility (GEF) is a consortium of various financial mechanisms to address the challenges posed by biodiversity decline, climate variability, and environmental contamination while promoting the sustainability of terrestrial and marine ecosystems. The GEF facility also funded the project \u0026ldquo;Enabling the use of global data sources to access the monitor land degradation at multiple scales\u0026rdquo;, which resulted in the creation of Trends. Earth facility. This allows it to directly fetch data from various international observatories, including the European Space Agency Climate Change data (ESA CCI \u0026ndash; LC) and Land Productivity Dynamics (LPD) of the Joint Research Centre (JRC) European Commission [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e"},{"header":"4. Methodology","content":"\u003cp\u003eThe study utilizes the methodology suggested by the good practice guidelines (GPG) of UNCCD to calculate the SDG 15.3.1 indicator. This includes binary quantification of sub-indicators for the base period and analysis for the reporting period. The overall SDG 15.3.1 indicator is achieved based on the One-out-all-out method (1OAO), where a negative trend in any sub-indicator results in overall degradation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e represents the method of calculating the land degradation, yielding the SDG 15.3.1 indicator. This indicator helps nations to regularly monitor their commitment toward achieving Land Neutrality.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Land Cover\u003c/h2\u003e \u003cp\u003eLand cover informs the functional relationship between terrain, climate, and soils, providing biophysical insights into the environment and drivers of change [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Land cover can be classified into 7 classes, for both the maps as tree covered (TC), grassland (GL), cropland (CL), wetland (WL), artificial area (AL), other land (BL), and water body (WB). A positive change depicts improvement (+\u0026thinsp;sign), a negative change depicts degradation (- sign), and no change in land condition depicts stability (zero). The alteration in land cover necessitates the utilisation of land cover maps encompassing the specified study region for the beginning and end years as an essential input. Both maps must exhibit high precision to generate reliable and comparable outcomes. Trends-Earth utilises the ESA CCI LC data; alternatively, Copernicus global land service cover at 100m (CGLS-LC 100) can be employed. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e generates the output for land cover change for each pixel in the map.\u003c/p\u003e \u003cp\u003e\u003cimg 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\" width=\"1086\" height=\"534\"\u003e\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Land Productivity\u003c/h2\u003e \u003cp\u003eLand productivity is the biological productive capacity of the land, the source of all the food, fibre and fuel that sustains humans [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The land productivity over a large area can be assessed using Net Primary Productivity (NPP), defined as the organic matter production rate after photosynthesis and autotropic respiration over a given period (kg/ha/yr.). Remote sensing can be a very useful means to obtain the NPP. Trends.Earth utilises Land Productivity Dynamics (LPD) data from the Joint Research Centre (JRC) European Commission to obtain NPP globally. Alternatively, the MODIS MOD13 Q1\u0026ndash;250m vegetation index data and Sentinel 2 Multispectral Instrument (MSI) can be used to prepare the Normalized Difference Vegetation Index (NDVI), which helps calculate NPP.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Carbon Stocks\u003c/h2\u003e \u003cp\u003eCarbon stocks are calculated based on metric Soil Organic Carbon (SOC), which refers to organic matter's carbon components up to the top 0\u0026ndash;30 cm layer present in soil [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The computation of change in SOC had certain limitations regarding spatial volatility in soil properties, time, and cost. Trends-Earth thus utilises the Land cover/SOC method based on data of Soil Grids 250m to locate the potentially degraded area. Land cover is similarly classified into seven components to calculate the change in carbon stocks. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e represents the carbon conversion coefficient for each global climatic region.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCarbon regeneration coefficient\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e \u003cp\u003eLand Cover categories at reporting period\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eLand cover categories at the base year.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/f\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1/f\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1/0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHere f represents different coefficient depending on regional climate mainly Temperate Dry (f\u0026thinsp;=\u0026thinsp;0.80), Temperate Moist (f\u0026thinsp;=\u0026thinsp;0.69), Tropical Dry (f\u0026thinsp;=\u0026thinsp;0.58), Tropical Moist (f\u0026thinsp;=\u0026thinsp;0.48), and Tropical Montane (f\u0026thinsp;=\u0026thinsp;0.64).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.4 SDG 15.3.1 Indicator\u003c/h2\u003e \u003cp\u003eThe combined result of these three indicators results in obtaining the SDG 15.3.1 indicator based on the one-out-all-out (1OAO) method. This means degradation or negative sign in any of the three indicators, namely land cover, land productivity, and soil organic carbon stocks, will result in output as overall degradation of SDG 15.3.1 indicator. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the aggregate process based on the 1OAO approach. The \u0026uarr; indicate improving condition of sub-indicator, = for stable and \u0026darr; for declining.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSDG 15.3.1 indicator calculation based on 1OAO approach\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand Cover\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand Productivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSOC stocks\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe combined output of SDG 15.3.1 Indicator\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026uarr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e=\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026darr;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003e\u0026uarr;\u003c/b\u003e Improvement \u003cb\u003e\u0026darr;\u003c/b\u003e Degradation\u0026thinsp;\u003cb\u003e=\u003c/b\u003e\u0026thinsp;No Change\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Result and Discussion","content":"\u003cp\u003eThe three-sub indicator i.e. Land cover, land productivity, and SOC stocks were analysed to study land degradation and ultimately report the SDG 15,3,1 indicator for the Indian state of Goa. \u0026ldquo;Trends.Earth, an open-source plugin of QGIS software, was used to perform the analysis over the period of 2001 to 2021, with the target year as 2021. The results for individual sub-indicators are discussed below.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Land Cover Shift\u003c/h2\u003e \u003cp\u003eFor the study period, the land cover shift shows degradation, stable, and improving trends for different components. The study depicts a positive shift in tree cover, with an improvement of 9%. An increase of 300% is noted in Artificial build-ups. Components like grasslands and cropland showed negative or declining trends. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the shift in land cover from 2001 to 2021.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe land cover trend from the initial period to the final period\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand cover categories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInitial area (sq. km)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinal area (sq. km)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChange in area (sq. km)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChange in the area (per cent)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,644.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,789.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-48.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-26%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,365.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,135.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-229.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-17%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e300%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e below represents the land cover map for the period between 2001 and 2021, which shows the major increase of artificial cover from 42.12 sq. km to 168.42 sq. km.\u003c/p\u003e \u003cp\u003eThe land cover transition is mostly along the coastal regions, which act as a major tourist attraction for the state of Goa. This transition is clearly depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLand Cover by Type of Land Cover Transition in km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e \u003cp\u003eLand Cover categories at reporting period\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eLand cover categories at the base year.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,612.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,118.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e111.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e149.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the land area by type of land cover transition for each component from the initial time of 2001 to the final reporting period of 2021. The artificial cover increase is the outcome of the growing tourism industry of Goa. The concentration of tourism growth in Goa is primarily focused on the regions of Bardez, Salcete, Tiswadi, and Marmagao. These areas account for approximately 66 per cent of Goa's Gross State Domestic Product (GSDP), making them the most economically developed regions in the state [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The infrastructure in these regions has played a crucial role in their development, enabling them to achieve high population densities and economic prosperity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe small-scale industry sector in these regions is particularly significant, as it encompasses 70% of small-scale industries, attracts 78.5% of capital investment in such industries, and provides employment opportunities for 68% of the workforce [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These regions' strategic positioning and focus on tourism and industry have contributed significantly to Goa's overall economic growth and development. Additionally, supportive infrastructure has further facilitated establishing and expanding businesses in these key areas, driving economic progress and creating employment opportunities for the local population. Overall, the regions of Bardez, Salcete, Tiswadi, and Marmagao stand out as vital hubs of economic activity and growth within the state of Goa, playing a crucial role in shaping its economic landscape.\u003c/p\u003e \u003cp\u003eOverall, land covers a maximum area of 3,088.05 sq. km, with stable land cover accounting for 90.26% of the total land area. Also, 5.20% of land showed improved land cover with an area of 178.02 sq. km. This data is represented in Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLand Cover change\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea in km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% age of total land area\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal land area under study\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,421.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLand area with improved land cover\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLand area with stable land cover\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,088.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.26%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLand area with degraded land cover\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.53%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Land productivity shift\u003c/h2\u003e \u003cp\u003eThe sub-indicator of land productivity has shown a significant improvement. The land cover transition is employed to study productivity individually as improving, stable, stressed, moderate decline, decline, and non-available data. SDG indicator areas are considered to be improved if they have \"Improving\" productivity, stable if they have \"stable\" or \"stressed\" productivity, and degraded if they are classified as in \"moderate decline\" or \"declining.\" The tables below present this individual output.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImproved productivity for land cover transition\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e \u003cp\u003eLand Cover categories at reporting period\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eLand cover categories at the base year.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,485.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1,510.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e158.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e972.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1,163.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e27.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e33.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,647.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e988.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e105.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e2,912.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStable productivity for land cover transition\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e \u003cp\u003eLand Cover categories at reporting period\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eLand cover categories at the base year.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e111.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e137.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e101.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e293.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStressed productivity for land cover transition\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e \u003cp\u003eLand Cover categories at reporting period\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eLand cover categories at the base year.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e4.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModerate declining productivity for land cover transition\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e \u003cp\u003eLand Cover categories at reporting period\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eLand cover categories at the base year.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e32.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e52.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDeclining productivity for land cover transition\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e \u003cp\u003eLand Cover categories at reporting period\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eLand cover categories at the base year.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003e5.12\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003e1.63\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003e21.60\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003e2.30\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003e2.23\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003e0.97\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003e6.09\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1.41\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e15.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e2.15\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e8.09\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003e0.00\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003e1.11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e33.85\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNo data of productivity for land cover transition\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e \u003cp\u003eLand Cover categories at reporting period\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eLand cover categlories at the base year.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eWB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e104.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e104.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e108.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e124.17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e represent change in productivity for the state of Goa with major area as improving with minor area as declining. The major trends of declining productivity are near Corlim region, consisting of land cover transition and an increase in artificial built-ups to support tourist accommodation and industrial estates. Further, the land area of Goa, with increased productivity, forms 85.14% of the total available land, which is nearly 2,912.83 sq km. This is represented in the table below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverall productivity change\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea in km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% age of total land area\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal land area under assessment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,421.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLand area with improved productivity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,912.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.14%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLand area with stable productivity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e297.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLand area with degraded productivity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.53%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Soil Organic Carbon (SOC) Stocks Trend\u003c/h2\u003e \u003cp\u003eSOC stocks are difficult to track due lack of data along with time and cost-consuming methods to study the change. SOC plays a fundamental role in the preservation of soil health, the functionality of ecosystems, and the guarantee of global food security. It is the primary measure to evaluate the above and below-ground carbon reserves. Compared to other indicators, alterations in SOC reserves linked to modifications in land utilisation and practices necessitate extended monitoring periods to assess the impact accurately. These variations, although significant, are relatively minor when juxtaposed with the vast quantities of carbon stored in the soil and the inherent fluctuations within these reserves. For individual class transition changes in SOC is tabulated.\u003c/p\u003e \u003cp\u003eBased on the difference, the change in SOC is plotted, representing a close association with changes in land cover trends and increased artificial built-ups. The overall SOC for the observed period shows stable behaviour, with more than 94.91%. However, there was a higher negative trend in comparison to improved SOC. The degraded SOC accounted for 4.27% against the improvement of 0.54%, and an overall negative change of -1.60% from the initial to the final year was observed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab14\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 14\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSOC trend from initial year to final year\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInitial soil organic carbon (tonnes/ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFinal soil organic carbon (tonnes/ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInitial area (sq. km)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFinal area (sq. km)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInitial soil organic carbon (tonnes)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFinal soil organic carbon (tonnes)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChange in soil organic carbon (tonnes)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eChange in soil organic carbon (percent)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,38,25,037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,50,23,510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11,98,472.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15,12,425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11,21,350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3,91,074.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-26%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,01,87,068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84,68,279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-17,18,788.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-17%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,05,543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3,50,402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44,858.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,02,399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,50,951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4,48,552.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e148%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTotal\u003c/em\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e3,270.22\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e3,267.32\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e2,61,32,472.05\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003e2,57,14,491.67\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003e-4,17,980.38\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab15\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 15\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSOC trend from initial to final year\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea in km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% age of total land area\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal land area\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,270.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLand area with improved soil organic carbon\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.54%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLand area with stable soil organic carbon\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,103.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.91%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLand area with degraded soil organic carbon\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.27%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLand area with no data for soil organic carbon\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePercent change in soil organic carbon storage from initial year to final year\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1.60%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurther, this change can be studied using the initial and final year map for SOC, which is represented below in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5.4 SDG 15.3.1 Indicator\u003c/h2\u003e \u003cp\u003eAs suggested by the Good Practice Guidelines (GPG) of UNCCD, the 1OAO approach, as stated earlier, is used to obtain the SDG 15.3.1 indicator. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the trends of the overall SDG 15.3.1 report from the initial to final years. This highlights the major degradation along the coastal regions associated with declining productivity and land cover change patterns.\u003c/p\u003e \u003cp\u003eA total of 2,776.66 sq. km of area was observed as improving, resulting in the heading of land degradation neutrality covering 81.16% of the total land. However, a degradation of 6.82% is observed for the area of 233.34 sq km, along with a stable area of 7% covering 239.42 sq km.\u003c/p\u003e \u003cp\u003eThe change in land use pattern is plotted below. The bar chart represents the land cover trend over the initial to final year.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab16\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 16\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSDG 15.3.1 indicator\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea in km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% age of total land area\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal land area under study\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,421.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLand area improved\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,776.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.16%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLand area stable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e239.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.00%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLand area degraded\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e233.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.82%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLand area with no data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.02%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e5.5 Integrated Normalized Difference Vegetation (NDVI) Index\u003c/h2\u003e \u003cp\u003eOne widely used method to estimate NPP is by the mean of NDVI. The Normalized Difference Vegetation Index (NDVI) is a mathematical calculation that involves the comparison of near-infra-red (NIR) wavelengths, which are commonly reflected by healthy green plants, and red wavelengths that fall within the active spectrum and are generally absorbed by chlorophyll in healthy green plants. This index is a useful tool for remotely sensing vegetation health and monitoring changes in vegetation over time. The NDVI is suggested as the established vegetation index for evaluating SDG Indicator 15.3.1 when no evidence suggests that another index would provide greater accuracy in this context. It is widely recognized that the NDVI has proven to be a reliable and effective tool for assessing vegetation health and monitoring changes in land cover over time [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The integrated NDVI to time graph is plotted based on MODIS MOD13Q1 annually at a spatial resolution of 250m.\u003c/p\u003e \u003cp\u003eA linear regression model is depicted in the graph, with the coefficient of determination value r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.81, providing a visual representation of the upward trajectory observed in the integrated NDVI index spanning from the commencement year of 2001 to the culmination year of 2021, indicating a consistent growth trend over the analyzed period. Government policy approach toward sustainable tourism contributes toward improvement in SDG and integrated NDVI over the observed period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eIn the present study, we have studied the SDG Indicator 15.3.1 (Proportion of degraded land over the total land area) for the Indian state of Goa, as recommended by good practice guidelines of the United Nations Convention to Combat Desertification (UNCCD). The study was conducted using QGIS, an open-source software with the help of the \u0026ldquo;Trends.Earth\u0026rdquo; plugin for analysis. Three sub-indicators (Land cover, Land Productivity and Soil Organic Carbon stocks) were studied individually from 2001 to 2021. Data from different sources was utilised for this purpose, including the European Space Agency Climate Change Initiative land cover data (ESA CCI- LC) to study trends in land cover, Soil Grids 250m were used for SOC stocks, and an integrated NDVI was generated using MODIS MOD13Q1\u0026ndash;250m. The overall SDG indicator 15.3.1 was calculated based on a one-out-all-out (1OAO) approach where degradation in an individual sub-indicator resulted in the decline of the SDG Indicator as a whole. The study concludes with overall SDG Indicator 15.3.1 for the state of Goa, depicting an improvement of 81.16% covering 2,776.66 sq km. However, a degraded area of 6.82% throughout the 233.34 sq km area was observed mostly near the coastal regions due to a shift in land use pattern and declining productivity over the years. Specifically, the state showed growth of 300% in artificial land cover from 42.12 sq km in the initial years to 168.43 sq km in the reporting year. Further, the major decline in grassland and cropland was observed due to varying land use patterns during the study period. Over the years, the state of Goa has shown an increase in productivity of 85.14%, with a minor portion of degraded productivity accounting for 2.53%. Carbon stocks depicted stable trends, with 94.91% of land area with stable soil organic carbon. The NDVI showed an increasing pattern from 2001 to 2021, which can result from sustainable development in the tourism sector, which positively impacted the economy and the environment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research did not receive a specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest/Competing Interests:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing academic/financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1) Data collection and analysis: Harshit Dubey\u003c/p\u003e\n\u003cp\u003e2) Organizing data and concluding results: Harshit Dubey and Naimish Bhatt\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3) Paper writing and review: Harshit Dubey, Naimish Bhatt and Shobhit Chaturvedi \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e4) Idea and Paper verification: Naimish Bhatt and Ravi Kant\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Authors would like to thank the support of the open-source software QGIS, which supported us with relevant data for conducting this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDesa, U. 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UNCCD. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.unccd.int/sites/default/files/relevant-links/2021-03/Indicator_15.3.1_GPG_v2_29Mar_Advanced-version.pdf\u003c/span\u003e\u003cspan address=\"https://www.unccd.int/sites/default/files/relevant-links/2021-03/Indicator_15.3.1_GPG_v2_29Mar_Advanced-version.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"asian-journal-of-civil-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Asian Journal of Civil Engineering](https://www.springer.com/journal/42107)","snPcode":"42107","submissionUrl":"https://submission.nature.com/new-submission/42107/3","title":"Asian Journal of Civil Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4652666/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4652666/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith increased anthropogenic activity and commitments toward various international organisations and countries to mitigate its ill effects, finding a sustainable way out is very important. The Sustainable Development Goals (SDGs) thus become an important part of our collective efforts. The present study aims at assessing SDG 15: Life on land (SDG 15.3.1) for India\u0026rsquo;s state of Goa following the good practices guidelines of the United Nations to Combat Desertification (UNCCD) by employing the \u0026ldquo;Trends. Earth\u0026rdquo; plug-in in QGIS software. The assessment of the indicator is based on three sub-indicators: soil carbon, productivity, and land cover. The study yields a total land area improvement of 81.16% to the overall area of the state. The individual sub-indicator of productivity depicted an improvement of 85.14%, whereas 8.70% area was with stable productivity. Other sub-indicators of land cover showed a larger portion of land as stable, with 90.26% and a minor improvement of 5.20%. The soil organic carbon depicted an improvement of 0.54% with a maximum stable soil organic carbon of 94.91%. Several factors, such as better resource utilisation, improved governance practices, and a more sustainable approach to development, have affected the overall improvement of the indicators. Over the years, these improvements have allowed the state to grow as a sustainable tourist attraction for Indian nationals and foreign visitors.\u003c/p\u003e","manuscriptTitle":"Evaluating the Challenges and Strategies for SDG.15.3: A Case Study of Goa State","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-22 21:48:58","doi":"10.21203/rs.3.rs-4652666/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-06-29T05:02:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-29T01:33:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Asian Journal of Civil Engineering","date":"2024-06-28T06:59:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"asian-journal-of-civil-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Asian Journal of Civil Engineering](https://www.springer.com/journal/42107)","snPcode":"42107","submissionUrl":"https://submission.nature.com/new-submission/42107/3","title":"Asian Journal of Civil Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0faaa9c9-315b-44c2-ae5f-9794ea2c88c3","owner":[],"postedDate":"July 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-07-22T21:48:59+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-22 21:48:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4652666","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4652666","identity":"rs-4652666","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}