Central Arid Zone Research Institute (CAZRI)

Nearly 61% of the total area of the Rajasthan state falls under the Great Indian ‘Thar’ desert. Recurrent droughts, low rainfall, high diurnal temperature variations, sandy soils low in fertility and water-holding capacity characterize the edapho-climatic conditions of this region. These edapho-climatic challenges in western Rajasthan have led to the evolution of traditional sustainable agriculture practices through generations. Field ploughing with animal-drawn desi ploughs, earthen embankment on the field boundaries as ‘Baad’ for conserving soil, mixed cropping, fallowing, the use of local landraces for seeds, manual interculture, etc., have traditionally been components of farming systems of the region. For soil fertility management, traditionally, animal manures, green manuring, wood ash, tank silt, etc. were used as sources of nutrients besides maintaining Khejri (Propsopis cineraria) trees on farms. With the advent of modern agricultural technologies, these centuries old survival strategies lagged behind. Integrating traditional agricultural practices with modern techniques is the only way to make farming practices sustainable and capable of meeting the demands of the population of this region. Selection of short-duration cultivars and their proper seed rate, use of proper sowing devices and the management of soil crust are integral components of this new approach. Indigenous technical knowledge and modern scientific technologies individually or by blending are capable of giving multiple benefits. Farm yard manure (FYM) application (@ 5.0 t/ha) over seed furrows retained 2.26% higher moisture in the surface layer besides its mulching effect and reduced the crust strength from 49 to 69 kPa during a week after sowing. Graded bunding can reduce the run-off from 20% to 4.8% and soil loss from 24 to 4.12 t/ha/year. The mixing of pond silt @ 76 t/ha increased the available water capacity by 7%, reduced the infiltration rate from 15 to 13.2 cm/h and hence increased the yield of pearl millet and mung by 35–50%. Inoculation of cluster bean with Rhizobium and PSB individually increased the yield by 17% and 20%, whereas their combined application increased the yield by 22.3%. Policy interventions are also needed to address the emerging challenges of groundwater depletion, wasteland management and drought management.

Ensemble species distribution modelling was compared to the two best-performing individual algorithms using climatic and non-climatic predictors for the critically endangered plant species Commiphora wightii, looking at habitat suitability, niche overlap, and IUCN categories like Extent of occurrence (EOO) and Area of occupancy (AOO) in India. We selected ensemble, Random Forest, and Support Vector Machine algorithms with current and two future climatic time frames (2050 and 2070) along with aspect and slope predictors based on the model quality tools. Using the ensemble methodology and the SVM technique, we found that the seasonality of precipitation had a stronger influence on the habitat suitability of this species in both the present and the 2070 time frames. The SVM was able to capture the magnitude of their effects better than the ensemble technique. However, for the 2050 climate projection, both the ensemble and SVM imply that the wettest quarter’s precipitation has a greater impact. This was found that Rajasthan’s flood-prone eastern plain, internal drainage dry zone, and irrigated north-western plains were less ideal places for this species to survive under the current climate.

Sorghum (Sorghum bicolor L.) ranks as the fifth most important cereal crop globally and thrives particularly well in dry and semi-arid climates. The advent of hybrid seed production has transformed sorghum cultivation by significantly boosting yields. This transformation is not limited to India but extends to other major sorghum-producing regions globally. Hybrid sorghum’s potential lies in combining advanced breeding methods with genetic diversity to produce varieties suited to specific environmental conditions and farmer needs. Hybrids with enhanced drought resistance can mitigate water scarcity effects, while those with improved disease resistance can reduce dependence on chemicals, promoting sustainability. Despite these benefits, hybrid sorghum production faces challenges, including maintaining genetic purity of parental lines, effective pollination control, high production costs, and socio-economic barriers affecting smallholder farmers. This chapter delves into the current state of hybrid sorghum seed production, highlighting key challenges such as male sterility, disease management, optimal field selection, and synchronized flowering. It also explores future opportunities, including advances in technology, harnessing genetic resources, and socio-economic and environmental benefits. By addressing these challenges and leveraging emerging opportunities, stakeholders can enhance the adoption and effectiveness of hybrid sorghum in diverse agricultural systems.

Pearl millet is a crucial coarse grain crop that serves as a primary food source for impoverished communities in arid and semi-arid regions of Africa and Asia. These regions account for 98% of the world’s pearl millet production and cultivation, thriving in warm, tropical dryland environments. Characterized by its protogynous inflorescence and chasmogamous flowers, pearl millet is highly allogamous through wind, making hybrid seed production a key aspect of its cultivation. The development of hybrids involves creating a variety of trait-specific parent lines through recurrent selection, focusing on attributes such as grain yield and disease resistance. The cytoplasmic genetic male sterility (CGMS) technique offers a genetic approach for the commercial production of pure single cross hybrid seeds from an A-line and R-line cross under open pollination in isolation. Maintainer lines, also known as B-lines, are the equivalent of A-lines and have fertile cytoplasm but the same nuclear gene makeup. Open-Pollinated Varieties (OPVs) are randomly mated populations maintained in isolation to preserve genetic integrity. For successful seed production, adherence to strict standards is essential, including maintaining isolation distances, managing crop layout and planting ratios, ensuring seed quality, and addressing flowering synchronization, pest and disease management, and harvesting practices.

The agricultural environment has seen a considerable transformation due to the relentless advancement of technology, which has brought newfound solutions to long-standing problems. This chapter examines how machine learning (ML) and artificial intelligence (AI) are revolutionizing pest and disease control in the agricultural sector. It is critical to integrate cutting-edge technologies into global agriculture as it faces unprecedented challenges in ensuring food security in the face of a rapidly changing climate. The conversation progresses through an analysis of cutting-edge models and algorithms that provide farmers with predictive analytics, empowering them to forecast and reduce any risks to crop health. Farmers can ensure maximum resource allocation, limit chemical usage, and minimize environmental impact by incorporating these technologies into precision agriculture systems. This chapter explores the many uses of AI and ML in agriculture, from precision farming methods to early disease and pest detection and diagnosis, hence promoting a paradigm shift in conventional agricultural practices. We discussed specific applications, such as image recognition for pest and disease diagnosis, predictive modelling for outbreak forecasting, and decision-making support systems for optimizing control strategies. Additionally, we address the challenges and limitations associated with AI adoption in agriculture, emphasizing the need for robust data infrastructure, ethical considerations, and farmer training for successful implementation. To help researchers, practitioners, and policymakers achieve sustainable and resilient agricultural systems, it provides a thorough grasp of how AI and ML can be used as effective tools. With an emphasis on the future of AI and ML in pest and disease control, this chapter concludes by presenting possible collaborations with cutting-edge technology.

Background Microbes within the rumen play a pivotal role in the digestion of feed ingested by the ruminants. Researchers have been investigating microbes within rumen to assess its genetic capabilities, which hold immense potential across various fields including agro-industrial advantages. Since rumen is preliminary an anaerobic sac, numerous anaerobic bacteria and fungi have been isolated and characterized, however facultative anaerobic bacteria yet not fully investigated. Methods and results In present study, we isolated, characterized and performed whole genome analysis of 101 facultative anaerobic bacteria from rumen, offering a unique perspective compared to metagenomic approaches. All assembled genomes were of high quality, i.e. completeness 100% (only seven were between 92 and 99.5%) and only two had contamination > 5%. We identified 9,542 sequences of Carbohydrate-Active Enzymes (CAZymes). Over 8,136 of these CAZymes were full-length sequences, with 2,048 harbouring signal peptides also. Xylan (n = 634), pectin (n = 604), and starch (n = 312) degrading enzyme sequences were dominant. Several isolates also harbour secondary metabolite biosynthesis gene clusters for various metabolites, including fengycin, lichenysin, bacillibactins, bacilysin etc. All the isolates have metabolic versatility, encompassing pathways such as carbohydrate, amino acid, lipid, and vitamin and cofactor metabolism. Intriguingly, lipoic acid metabolism was absent in most of these facultative bacterial isolates. Conclusion This comprehensive study sheds light on the genetic potential of culturable facultative rumen bacteria, emphasizing their pivotal roles in carbohydrate degradation, secondary metabolite production, and metabolic diversity. These findings hold promise for enhancing ruminant nutrition, advancing eco-friendly biomass conversion, and bolstering bioprospecting of industrially important biocules and enzymes biofuel production.

Food crops are highly susceptible to contamination with toxigenic fungi before and after harvest. This can be due to environmental factors or poor logistics during handling, processing and storage. Usually, biological approaches are used to identify and quantify fungi, while chemical analysis does the same for the metabolites of the fungi, mycotoxins. These approaches are time-consuming, skill-demanding, expensive and susceptible to sampling technique. Hyperspectral imaging (HSI) is a proven alternative among the emerging technologies as its deployment at any stage in the food production system is easy and rapid. This is a unique review wherein the HSI applications for rapid and non-destructive estimation and quantification of mycotoxin contamination studies and research findings in different food crops including cereals, pulses, oilseeds, fruits, nuts and dried fruits have been grouped and discussed systematically. This chapter presents different hypercube pre-processing techniques, traditional chemometrics in relation to machine learning approaches are also enumerated in relation to the groundwork for new HSI conceptual frameworks, exposes research inconsistencies, synthesizes diverse findings and provides other researchers with an overview of subject.

Proper pollination and bunch management are vital to improving date palm productivity and fruit quality. The present study was conducted on 6‑year-old tissue culture derived date palm cv. ‘ADP-1’ in hot-arid Indian conditions. The objective was to achieve higher yield and superior fruit quality by standardizing the right time for pollination, optimum crop load and bunch cover. Our findings revealed that pollination had no discernible negative effects on yield or yield attributing traits including fruit weight and pulp weight until 36 h following spathe splitting. However, it was found that the best time to pollinate was between 12–36 h following spathe splitting. The best combination for achieving a high marketable yield of better-quality fruits (total soluble solids, sugar, etc.) was 12 bunches per tree plus 25% strand thinning. Fruit quality attributes were also affected by bunch covering; nonwoven bagging resulted in the largest percentage (28.6%) of pind stage fruits and increased overall fruit yield by 35.8% as compared to the uncovered control.

Sixteen isolates of Macrophomina phaseolina isolated from different food legumes revealed substantial variability in respect of morphological features at genetic level. Out of sixteen isolates, isolate Mp3 (horse gram) and Mp4 (cowpea) were fast growing, whereas the isolates Mp7, Mp8 and Mp9 obtained from soybean were slow growing. The isolates originated from soybean showed the largest size of sclerotia, whereas, the smallest size of sclerotia was formed in the isolate obtained from horse gram. The maximum number of sclerotia was formed in the isolate Mp8 (soybean) and the minimum number in the isolate Mp3 (horse gram). The genetic variation among the isolates was detected using 19 SRAP primer combinations. The mungbean isolates (Mp5 and Mp6) exhibited a highest genetic similarity (90%), whereas the soybean isolates (Mp8 and Mp16) displayed the lowest genetic similarity (58%). SRAP marker analysis distinctly revealed genetic variation among isolates collected from various hosts and geographical regions. The isolates were grouped initially in two clusters, first cluster C1 having 14 isolates from different regions, whereas, Mp8 and Mp12 respectively from Madhya Pradesh and Delhi grouped in the second cluster C2. The results further demonstrated the applicability of SRAP marker in analysing genetic variation in natural population of M. phaseolina.

The pre-installation assessment criteria for solar energy parks have been simulated through a variety of machine learning algorithms, with predictors categorized into three different climatic time frames (present, 2050, and 2070 bio-climatic time frames) and four distinct Socio-Economic Emission Scenarios, namely, RCPs 2.6, 4.5, 6.0, and 8.5, which represent projections for future levels of radiative forcing and greenhouse gas emissions W/m2. A promising new location identification was speedily achieved through the development of an ensemble distribution model using a machine learning algorithm. The total capacity (in MW) and covered area of 78 different solar parks across India from various agro-climatic zones were examined (Sq. KM). Predictions about the future viability of existing solar parks are made in this study, and the best places for new ones are suggested. It was found that 2.08% of India’s total land area, or 68,369.69 sq. km, is optimum for solar parks, given the existing climatic, solar, and land cover characteristics. Across the board, the optimal locations were increased for RCPs 2.6 (3.87% of India’s total land area), 4.5 (2.72%), and 8.5 (4.47%) by 2050. Upward trends were similarly observed in the RCP 2.6 (3.40) and RCP 6.0 (2.27%) for 2070. Solar parks are considered ideal in the western half of the country, while more moderate locations are expected to emerge in the west, south-west, and central India. Keywords: Solar energy, Climate change, Machine learning, Ensemble modelling, Solar parks, Greenhouse gas, Land cover

As a vital component of the desert ecological protection system, the edge-locked forests of the Kubuqi Desert play a crucial role in mitigating wind erosion, stabilizing sand, maintaining soil and water, and restricting desert expansion. In this paper, six types of standard protection forests in the Kubuqi Desert, namely Salix psammophila (SL), Elaeagnus angustifolia (SZ), Salix matsudana (HL), Corethrodendron fruticosum+Salix psammophila (YC + SL), Caragana korshinskii + Populus simonii (XYY + NT), and Elaeagnus angustifolia + Salix matsudana (SZ + HL), were investigated. Notably, the vertical differentiation patterns of soil carbon (C), nitrogen (N), phosphorus (P), and ecological stoichiometric ratios, as well as soil particle size features within the 0–100-cm soil layer under protection forests with different allocation modes, were systematically and comprehensively analyzed. The study’s findings showed that: (1) Among the six configuration types, SZ, NT + XYY, and SL exhibited higher soil SOC and TN concentrations. Both soil SOC and TN content decreased with increasing soil depth, whereas soil TP content displayed no considerable variation among different stand types or soil depths. (2) Based on the N/P threshold hypothesis, N was the limiting nutrient element for the growth of edge-locked forests in the region. (3) The understory soils of different configurations of edge-locked forests mainly comprised sand. The silt and clay contents of SL and NT + XYY were substantially higher than those of the other four configurations. The vertical distribution patterns of particle size and parameter characteristics had variations. (4) Soil C, N, P, and stoichiometric characteristics are affected by vegetation type, soil depth, and soil texture. In conclusion, SZ and SL can be used as the dominant tree species in the edge-locked forests of the Kubuqi Desert, and the NT + XYY mixed forest configuration pattern displays the most apparent soil improvement effect. This study’s findings offer a scientific reference and foundation for restoring vegetation and enhancing the ecological environment in desert regions. In addition, they provide a theoretical foundation for establishing and managing edge-locked forests.

The study was carried out in the Jodhpur district of Rajasthan which represents a major part of India’s hot arid region and historically, a dryland practicing mainly rainfed agriculture. Authors used remote sensing techniques and GIS for a spatial analysis of irrigated croplands and ground water levels and mapped decadal changes for the years 2000, 2010 and 2020. Results showed a significant increase in the extent of irrigated crop area (7.56% in the year 2000–21.26% during 2020) and a lowering of ground water level in three of the most agriculturally prosperous tehsils; Osian (− 36.02 m), Shergarh (− 11.38 m) and Phalodi (− 44.85 m). Considering the fact that in Rajasthan, > 90% blocks are classified as “Groundwater dark zone”, such practices will have a broader implication in the seasonal cropping pattern.

Macrophomina phaseolina, a devastating soil and seed-borne fungus causing charcoal rot in soybean, poses a significant challenge to soybean production and breeding programs across all major soybean-growing regions of India. Fifty-five M. phaseolina isolates were collected from India's eight diverse soybean-growing agroecological regions. These isolates were examined for morpho-cultural, molecular, and pathogenic variability. All these isolates were pathogenic to the soybean and had significant variability for different Morpho-cultural characters. Principal component analysis (PCA) showed that most of Morpho-cultural traits are not having association with pathogenic traits. Cluster analysis showed that all these 55 isolates of M. phaseolina were classified into two major groups, and virulence characters did not separate based on origin. Group B showed more diversity and included the most virulent pathogen isolates. Phylogenetic analysis of the Internal Transcribed Spacer (ITS), a conserved rDNA region, revealed limited diversity among the 55 isolates. Irrespective of morpho-cultural and pathogenic characters, most isolates (n = 52) were clustered in a group. Pathogenic variability analysis has revealed region specific most virulent isolate from diverse agroecological regions of India. GGE biplot segregated the main effect of each component, cultivars (G), isolates (I), and G × I interactions with significant levels (p < 0.001). The virulence of isolates contributed 56.30 % of the total variation, followed by varieties (36.79 %) and G × I interaction (4.96 %). GGE biplot also provides information on two highly discriminative isolates. These isolates may be useful for screening genotypes and identifying quantitative trait loci (QTL) linked to soybean charcoal rot.

The maize weevil poses significant global concerns as it causes huge economic losses to stored grains. The chemical control methods are popular among farmers but raise serious concerns regarding human and environmental safety, highlighting the urgent need for novel and safe strategies. Plant extracts are seen as safe substitutes to toxic chemicals. Therefore, this study investigated the efficacy of methanolic plant extracts prepared from Azadirachta indica (seeds), Caralluma tuberculata (succulent fruits), Allium sativum (rhizomes), Curcuma longa (rhizomes), Citrullus colocynthis (succulent fruits) and Calotropis procera (leaves) against maize weevil under constant conditions of 27 ± 2 0C, 65% R.H). The experiments were carried out using a CRD design having five replications in the laboratory of the Entomology department, Gomal University, Dera Ismail Khan, Pakistan. The methanolic plant extracts were tested at 0.5, 1.0, 1.5, 2.0, 2.5 and 3.0% (v /w), concentrations, respectively. The parameters investigated, included, the number of days to F1 generation, emergence of F1 progenies, percent infestation and weight loss of grains, adult life span and sex ratio (male/female). Each methanolic plant extract was mixed with 20 g maize grains. Among the treatments, the A. indica and C. longa extracts showed greater effectiveness at the maximum concentration (3.0%), significantly delaying the emergence of F1 adults to 40.20 and 38.80 days, respectively, compared to the control group which emerged in 26.20 days. Least number of F1 adult emergence (11.60) was observed in A. indica extracts followed by C. longa having (18.00) while the highest number (45.80) was observed in C. procera treated grains at the maximum concentration of 3.0% compared to 81.20 in the untreated grains. The A. indica extracts showed the least infestation rate (2.14%) followed by C. longa (3.15%). Conversely, higher infestation rates were noted in C. tuberculata (8.38%) and C. procera (9.20%) treated grains at the maximum concentration of 3% compared to 35.20% in the control group. The minimum (1.10%) weight loss was observed in A. indica extracts treated grains whereas; maximum weight loss was observed in C. procera (5.11%) treated grains at the maximum concentration compared with control (27.50%). The minimum adult life span of 32.40 days was observed in maize grains treated with A. indica extracts followed by C. longa (34.20 days) and maximum adult life span was documented in C. procera (37.60 days) and C. tuberculata (37.00 days) treated grains at the maximum concentration compared with control (46.00 days). All the tested methanolic plant extracts had no significant effect on the sex ratio of the weevil adults. It is concluded from the results that the methanolic extracts of A. indica and C. longa could be used as allele-chemicals to control maize weevil under storage conditions.

Agroforestry is seen as a strategy to sustainably boost agricultural production by creating favorable microclimatic conditions. However, tree shade can significantly reduce crop yield, making it important to assess the balance between the positive and negative impacts of tree cover on food security, especially as climate change alters weather patterns. To understand this relationship, a trial was conducted to evaluate how tree canopy influences crop yield in degraded soils. This study examines how different levels of natural tree shade affect the physiological and biophysical constraints of soybean (Glycine max) in an Emblica officinalis-based agroforestry system. The study assessed the effects of shade intensities (S1-0%, S2-40%, S3-50%, and S4-60%) on physio-biochemical and yield traits of two soybean varieties: KDS-726 (V1) and MACS-1188 (V2). Increased shade led to significant reductions in net photosynthetic rate (16.21%, 25.32%, 40.08%), transpiration rate (6.45%, 21.14%, 39.61%), and stomatal conductance (22.86%, 39.79%, 55.91%) due to reduced light availability over control (S1-0%). Chlorophyll content and NDVI increased up to 50% shade but decreased beyond this, indicating limited photosynthesis. Higher shade levels also increased total phenol, proline, and other antioxidants, indicating increased stress. Soybean yield parameters decreased with increasing shade. The highest seed yield was in open conditions (2.15 t ha⁻¹), with reductions of 24.65%, 39.53%, and 59.53% under S2-40%, S3-50%, and S4-60% shade. KDS-726 produced 20% more seed yield than MACS-1188 (1.35 t ha⁻¹). Correlation analysis revealed that higher phenolic content and internal CO2 levels, indicators of stress, negatively impacted seed yield (− 0.51 and − 0.49, respectively) due to reduced photosynthesis. A Crop Status Index (CSI) was derived to identify the shade threshold level in agroforestry for the first time. The highest CSI was recorded under open conditions, statistically comparable to values under 40% and 50% shade, and lowest in 60% shade. This suggests that moderate shading (up to 50%) does not significantly affect the crop’s overall status, while higher shade levels (60%) impose severe stress. Understanding the shade threshold helps manage understory crops to maximize light and reduce stress. Additionally, it demonstrates the potential of fruit-based agroforestry to rehabilitate degraded lands, enhance crop yield, increase fruit production, improve the environment, and meet India’s GROW report commitments of land degradation neutrality by restoring 26 million hectares of degraded land by 2030.

Global warming has impacted water cycle, but not exist a global study of the changes at global scale of the impacts on water available for plants. Here, cloud-optimized monthly aggregated climate reanalysis from the European Centre for Medium-Range Weather Forecasts dataset indicates that from 1960 to 2023, 27.9% of the global land surface became significantly more arid, while 20.5% became significantly less arid. This indicates a shift towards drier climates, with humid, semi-humid, and semi-arid areas decreasing by 8.51, 1.45, and 0.53 million-km², respectively, and arid and hyper-arid areas increasing by 6.34 and 4.18 million-km², respectively. This total increase of 9.99 million km² in arid areas represents 5.9% of the global land surface, excluding Greenland and Antarctica. Accelerated aridification has occurred in already dry regions, such as South-west North-America, North-Brazil, the European-Basin, North-Africa, the Middle-East, the Sahel, and central-Asia, with central-Africa as a new hotspot. The main driver is the disproportionate increase in potential evapotranspiration relative to rainfall, attributed to rising atmospheric temperatures, which also reduces the land’s carbon sink capacity, potentially exacerbating climate warming.

Soybean in India is facing many production challenges in the form of biotic and abiotic stresses. Among biotic stresses, yellow mosaic disease (YMD) and charcoal rot disease are causing significant yield losses. Current study was undertaken to identify promising genotypes for yellow mosaic and charcoal rot diseases and higher yield under high disease pressure. A total of 78 genotypes were screened for resistance against yellow mosaic and charcoal rot diseases, and grain yield under sick plot conditions over three years (2021, 2022 and 2023). In addition, these genotypes were also evaluated for seedling charcoal rot resistance through artificial inoculation. Genotypes JS 94–67, EC 34372, JS 21–78, JS 21–73, JS 21–05 and PS 1024 were found to be promising donors for yellow mosaic disease, while JS 22–18, JS 22–12 and JS 21–05 showed stable field resistance against charcoal rot disease. Through artificial inoculation for charcoal rot resistance, least area under disease progress curve (AUDPC) was found in JS 22–10 followed by PS 1613, JS 22–12, JS 22–16, JS 2–05, JS 22–15, JS 22–18 and KDS 1073. Two genotypes, JS 94–67 and JS 21–05 were found to be superior resistance donors for both diseases, while JS 22–10 and PS 1613 were found to be the best resistance sources for seedling charcoal rot disease. JS 21–13 was the only genotype selected for YMD and charcoal rot resistance and grain yield based on three multi-trait selection indices viz., Multi-Trait Stability Index (MTSI), Multitrait Genotype-Ideotype Distance Index (MGIDI) and Factor analysis and genotype-ideotype distance (FAI BLUP). Genotypes identified for different traits will be used as parents in developing high-yielding, yellow mosaic and charcoal rot resistant wider adaptable cultivars to sustain soybean production in India.

Urban and peri-urban forests (UPFs) are critical in addressing climate change challenges and fostering urban sustainability. The world faces escalating climate threats, with human activities causing historic CO2 emissions and rising atmospheric CO2 concentration. Tree planting and reforestation are crucial to limit global warming to 1.5 °C, aligning with global efforts. As urbanization surges, UPFs emerge as essential components for climate change mitigation and adaptation, often called “city lungs. Recognized in Sustainable Development Goal-11”, UPFs offer diverse ecosystem services, contributing to urban quality of life. However, these forests face threats from unregulated urban development and inadequate management, often undervalued by decision-makers. Urban forests offer numerous benefits, addressing various urban issues such as food security, poverty, pollution, and climate change. They are pivotal in advancing Sustainable Development Goals (SDGs), impacting nine specific SDGs. Understanding urban ecosystem services (UES) becomes imperative as more than half the global population resides in cities. In this chapter, the concept of ecosystem services is divided into four categories: provisioning, regulating, supporting, and cultural. This paper focuses on provisioning and regulating services provided by urban trees, detailing their role in food security, pollution mitigation, and climate regulation. Specifically, it explores the regulating services of heat mitigation, dust retention, carbon sequestration, and noise reduction, emphasizing the impact of tree species, environmental factors, and their role in enhancing the urban quality of life. Recognizing and incorporating their diverse ecosystem services into comprehensive climate change mitigation strategies is crucial for building healthier, more resilient cities.

Background In the arid conditions of Thar desert, only the plants which are adapted to the extreme conditions can grow and reproduce. Rangelands are important fodder resources which are needed to be improved for their long-term productivity and sustainability through conservation and utilization of indigenous plant species (Lasiurus sindicus, Cenchrus ciliaris, Cenchrus setigerus, etc.). In this first ever study; we investigated the reproductive features of L. sindicus, which will assist in breeding related improvement programs of L. sindicus. The findings will also enhance our understanding about the survival strategies of L. sindicus in the extreme arid conditions. Results Flowers of L. sindicus are of both types, staminate and bisexual with off- white colored corolla. Results of outcrossing index (OCI), pollen-to-ovule (P/O) ratio, pollen count and different pollination treatments, indicated for cross- pollination mechanism in L. sindicus. Absence of nectar secreting tissues for nectar production and fragrance, suggested for wind-mediated pollination system. Lower grain germination rate of self-pollination than that of geitonogamous pollination and open pollination, further supported the prevalence of outcrossing in the breeding system. Conclusions Different aspects of reproductive biology of L. sindicus, were examined which provided insight into conservation and management of this unique plant species for rangeland management programs. Floral traits, such as large pollen count, high grain setting in open pollination treatment and absence of pollinators in L. sindicus indicated towards wind-mediated out-crossing. Our findings have laid a solid foundation for various genetic studies and improvement programs of L. sindicus.