Decadal Variations in NDVI and Food Production in India

Impact of aridity rise and arid lands expansion on carbon-storing capacity, biodiversity loss, and ecosystem services

Drylands, comprising semi-arid, arid, and hyperarid regions, cover approximately 41% of the Earth's land surface and have expanded considerably in recent decades. Even under more optimistic scenarios, such as limiting global temperature rise to 1.5°C by 2100, semi-arid lands may increase by up to 38%. This study provides an overview of the state-of-the-art regarding changing aridity in arid regions, with a specific focus on its effects on the accumulation and availability of carbon (C), nitrogen (N), and phosphorus (P) in plant-soil systems. Additionally, we summarized the impacts of rising aridity on biodiversity, service provisioning, and feedback effects on climate change across scales. The expansion of arid ecosystems is linked to a decline in C and nutrient stocks, plant community biomass and diversity, thereby diminishing the capacity for recovery and maintaining adequate water-use efficiency by plants and microbes. Prolonged drought led to a -3.3% reduction in soil organic carbon (SOC) content (based on 148 drought-manipulation studies), a -8.7% decrease in plant litter input, a -13.0% decline in absolute litter decomposition, and a -5.7% decrease in litter decomposition rate. Moreover, a substantial positive feedback loop with global warming exists, primarily due to increased albedo. The loss of critical ecosystem services, including food production capacity and water resources, poses a severe challenge to the inhabitants of these regions. Increased aridity reduces SOC, nutrient, and water content. Aridity expansion and intensification exacerbate socio-economic disparities between economically rich and least developed countries, with significant opportunities for improvement through substantial investments in infrastructure and technology. By 2100, half the world's landmass may become dryland, characterized by severe conditions marked by limited C, N, and P resources, water scarcity, and substantial loss of native species biodiversity. These conditions pose formidable challenges for maintaining essential services, impacting human well-being and raising complex global and regional socio-political challenges.

Unraveling the Multiple Drivers of Greening-Browning and Leaf Area Variability in a Socioeconomically Sensitive Drought-Prone Region

The complex attribution of climatic, hydrologic, and anthropogenic drivers to vegetation and agricultural production and their consequential societal impacts are not well understood, especially in socioeconomically sensitive states like Maharashtra, India. Here, we analyzed trends and variability in the MODIS leaf area index (LAI) time series, along with spatiotemporal patterns in precipitation, groundwater storage, agriculture statistics, and irrigation infrastructure, to identify their influences on the vegetation response and discuss their implications for farmers. The state showed greening in all biomes except forests, with a net gain of 17.478 × 103 km2 of leaf area during 2003–2019, where more than 70% of the trend in LAI is represented in croplands. Maximum greening was observed in irrigated croplands, attributable to increased crop productivity, whereas inadequate irrigation facilities with erratic rainfall patterns and droughts were primarily responsible for cropland browning. We discerned the dynamics and variability of vegetation response by incorporating a spectrum of synergistic feedbacks from multiple confounding drivers and found that uneven distribution of water availability across the administrative divisions governed the quantitative distinction in leaf area change. Despite the observed greening trends, the state witnessed a high number of farmer suicides related to droughts and agriculture failures hampering their socioeconomic security; therefore, improved irrigation infrastructure and comprehensive policy interventions are crucial for abatement of farmer distress.

Estimating Yield from NDVI, Weather Data, and Soil Water Depletion for Sugar Beet and Potato in Northern Belgium

Crop-yield models based on vegetation indices such as the normalized difference vegetation index (NDVI) have been developed to monitor crop yield at higher spatial and temporal resolutions compared to agricultural statistical data. We evaluated the model performance of NDVI-based random forest models for sugar beet and potato farm yields in northern Belgium during 2016–2018. We also evaluated whether weather variables and root-zone soil water depletion during the growing season improved the model performance. The NDVI integral did not explain early and late potato yield variability and only partly explained sugar-beet yield variability. The NDVI series of early and late potato crops were not sensitive enough to yield affecting weather and soil water conditions. We found that water-saturated conditions early in the growing season and elevated temperatures late in the growing season explained a large part of the sugar-beet and late-potato yield variability. The NDVI integral in combination with monthly precipitation, maximum temperature, and root-zone soil water depletion during the growing season explained farm-scale sugar beet (R2 = 0.84, MSE = 48.8) and late potato (R2 = 0.56, MSE = 57.3) yield variability well from 2016 to 2018 in northern Belgium.

Greening and Browning Trends of Vegetation in India and Their Responses to Climatic and Non-Climatic Drivers

It is imperative to know the spatial distribution of vegetation trends in India and its responses to both climatic and non-climatic drivers because many ecoregions are vulnerable to global climate change. Here we employed the NDVI3g satellite data over the span of 35 years (1981/82–2015) to estimate vegetation trends and corresponding climatic variables trends (i.e., precipitation, temperature, solar radiation and soil moisture) by using the Mann–Kendall test (τ) and the Theil–Sen median trend. Analysis was performed separately for the two focal periods—(i) the earlier period (1981/82–2000) and (ii) later period (2000–2015)—because many ecoregions experienced more warming after 2000 than the 1980s and 1990s. Our results revealed that a prominent large-scale greening trend (47% of area) of vegetation continued from the earlier period to the later period (80% of area) across the northwestern Plain and Central India. Despite climatologically drier regions, the stronger greening trend was also evident over croplands which was attributed to moisture-induced greening combined with cooling trends of temperature. However, greening trends of vegetation and croplands diminished (i.e., from 84% to 40% of area in kharif season), especially over the southern peninsula, including the west-central area. Such changes were mostly attributed to warming trends and declined soil moisture trends, a phenomenon known as temperature-induced moisture stress. This effect has an adverse impact on vegetation growth in the Himalayas, Northeast India, the Western Ghats and the southern peninsula, which was further exaggerated by human-induced land-use change. Therefore, it can be concluded that vegetation trend analysis from NDVI3g data provides vital information on two mechanisms (i.e., temperature-induced moisture stress and moisture-induced greening) operating in India. In particular, the temperature-induced moisture stress is alarming, and may be exacerbated in the future under accelerated warming as it may have potential implications on forest and agriculture ecosystems, including societal impacts (e.g., food security, employment, wealth). These findings are very valuable to policymakers and climate change awareness-raising campaigns at the national level.

Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia

Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics.

Assessment of Risk and Resilience of Terrestrial Ecosystem Productivity under the Influence of Extreme Climatic Conditions over India

Analysing the link between terrestrial ecosystem productivity (i.e., Net Primary Productivity: NPP) and extreme climate conditions is vital in the context of increasing threats due to climate change. To reveal the impact of changing extreme conditions on NPP, a copula-based probabilistic model was developed, and the study was carried out over 25 river basins and 10 vegetation types of India. Further, the resiliency of the terrestrial ecosystems to sustain the extreme disturbances was evaluated at annual scale, monsoon, and non-monsoon seasons. The results showed, 15 out of 25 river basins were at high risks, and terrestrial ecosystems in only 5 river basins were resilient to extreme climatic conditions. Moreover, at least 50% area under 4 out of 10 vegetation cover types was found to be facing high chances of a drastic reduction in NPP, and 8 out of 10 vegetation cover types were non-resilient with the changing extreme climate conditions.

Connecting Crop Productivity, Residue Fires, and Air Quality over Northern India

Northwestern India is known as the “breadbasket” of the country producing two-thirds of food grains, with wheat and rice as the principal crops grown under the crop rotation system. Agricultural data from India indicates a 25% increase in the post-monsoon rice crop production in Punjab during 2002–2016. NASA’s A-train satellite sensors detect a consistent increase in the vegetation index (net 21%) and post-harvest agricultural fire activity (net ~60%) leading to nearly 43% increase in aerosol loading over the populous Indo-Gangetic Plain in northern India. The ground-level particulate matter (PM_2.5) downwind over New Delhi shows a concurrent uptrend of net 60%. The effectiveness of a robust satellite-based relationship between vegetation index—a proxy for crop amounts, and post-harvest fires—a precursor of extreme air pollution events, has been further demonstrated in predicting the seasonal agricultural burning. An efficient crop residue management system is critically needed towards eliminating open field burning to mitigate episodic hazardous air quality over northern India.

Spatial variability of climate change impacts on yield of rice and wheat in the Indian Ganga Basin

Indian Ganga Basin (IGB), one of the most densely populated areas in the world, is facing a significant threat to food grain production, besides increased yield gap between actual and potential production, due to climate change. We have analyzed the spatial variability of climate change impacts on rice and wheat yields at three different locations representing the upper, middle and lower IGB. The DSSAT model is used to simulate the effects of climate variability and climate change on rice and wheat yields by analyzing: (i) spatial crop yield response to current climate, and (ii) impact of a changing climate as projected by two regional climate models, REMO and HadRM3, based on SRES A1B emission scenarios for the period 2011-2040. Results for current climate demonstrate a significant gap between actual and potential yield for upper, middle and lower IGB stations. The analysis based on RCM projections shows that during 2011-2040, the largest reduction in rice and wheat yields will occur in the upper IGB (reduction of potential rice and wheat yield respectively by 43.2% and 20.9% by REMO, and 24.8% and 17.2% by HadRM3). In the lower IGB, however, contrasting results are obtained, with HadRM3 based projections showing an increase in the potential rice and wheat yields, whereas, REMO based projections show decreased potential yields. We discuss the influence of agro-climatic factors; variation in temperature, length of maturity period and leaf area index which are responsible for modeled spatial variability in crop yield response within the IGB.