Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

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Not AvailableABSTRACT: Soil quality was assessed under long-term soil and nutrient management treatments being practiced at Phulbani center of All India Coordinated Research Project (AICRPDA). In experiment 1 under rice-horse-gram system, the RSQI (Relative Soil Quality Index) values as influenced by soil-nutrient management ranged from 0.53 (100% RDF) to 1.0 (50% RDF+50% FYM), which confirmed the importance of conjunctive use of organic and inorganic sources of nutrients. In Pigeon pea (Asha (ICPL-87119) + Rice (ZHU 11-26)) system, among the set of 9 nutrient management treatments, 30 kg N through gliricidia (GL)+15 kg N through chemical fertilisers (CF) resulted in highest RSQI of 1.00 and proved superior. In this set, other treatments which maintained RSQI more than 0.80 and indicated considerable aggrading effect on soil quality were: 30 kg N farm yard manure (FYM) +15 kg N (CF) (0.88), 20 kg N (GL) + 25 kg N (CF) (0.82), 20 kg N (FYM) + 25 kg N (CF) (0.82), Organic (GL) 45 kg N (0.82). In experiment 3, while evaluating the INM treatments for rice, blackgram and horsegram, it was found that the RSQI across the treatments varied from 0.55 in 25 kg N (FYM) to 1.00 (15 kg N (FYM)+20 kg N (inorganic)). In experiment 4, a combination of 15 kg N (FYM) + 10 kg N (GL) gave RSQI as high as 0.70. The RSQI across the treatments varied from 0.57 to 1.00, the highest being in 50% (CF) + 50% N (GL) treatment (1.00) followed by 100% RDF (CF) (80:60:80 N-P2O5-K2O ha-1) (0.70). In experiment 5, the RSQI values as influenced by soil and nutrient management treatments varied from 0.56 LT + interculture+ herbicide +100% N (CF) to 1.0 (CT+ Interculture + 100 % N organic). Other equally important aggrading treatments were found to be LT+ Interculture+ herbicide +100% N organic (0.98), LT+ interculture + herbicide + 50% N (CF) + 50 % N organic (0.88) and CT+ Interculture + 50% N (CF) + 50 % N organic (0.87). Thus, the findings of the present study would help in screening the best soil and nutrient management treatments for different crops and cropping systems from the perspective of overall soil quality and productivity improvement.Not Availabl

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Not AvailableABSTRACT: To evaluate the long term influence of existing soil and nutrient management practices on soil quality using Relative Soil Quality Index (RSQI) approach, two ongoing long term integrated nutrient management experiments in medium black soil (Typic Chromustert) of Bijapur centre were adopted for the study. First experiment was initiated in 1984-85 under rabi sorghum-safflower crop rotation with T1: control, T2: Sorghum- 50 kg N: 25 kg P2O5: 0 kg K2O ha- 1 recommended dose of fertilizer (RDF), Safflower- 37.5 kg N: 50 kg P2O5 :15 kg K2O ha-1 (RDF), T3: 50% N (FYM), T4: 50 % N sunhemp and T5: 50% RDF + 15 kg ha-1 ZnSO4 as soil-nutrient management treatments. Second experiment was initiated in 1998-99 under rabi sorghum with T1: Control, T2: 100% N urea; T3: 25 kg N (compost); T4: 15 kg N (compost) + 20 kg N (inorganic), T5: 15 kg N (sunhemp) + 20 kg N (inorganic) and T6: 15 kg N (compost) + 10 kg N (sunhemp) as management treatments. Soil samples collected from these two experiments during 2005 were processed and analyzed for 16 soil quality indicators and Relative Soil Quality Index (RSQI) was computed. In the first set, the order of treatments from the view point of RSQI was 50% RDF + 15 kg ha-1 ZnSO4 (1.00) > 50% N (FYM) (0.97) > 50 kg N: 25 kg P2O5: 0 kg K2O ha-1 (RDF for Sorghum) - 37.5 kg N: 50 kg P2O5 :15 kg K2O ha-1 (RDF for Safflower) (0.84) > 50 % N sunhemp (0.76). Application of 50% RDF + 15 kg ha-1 ZnSO4 to sorghum and safflower was found superior most. In the second set, application of 15 kg N (compost) + 10 kg N (sunhemp) (RSQI 1.00) was found most promising. Based on the RSQI, the general ranking was 15 kg N (compost) + 10 kg N (sunhemp) (1.00) > 15 kg N (compost) + 20 kg N (inorganic) (0.86) > 100% N urea (0.78) > 25 kg N compost (0.64) > 15 kg N (sunhemp) + 20 kg N (inorganic) (0.62). The full paper deals with the method of soil quality assessment and computation in detailNot Availabl

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Not AvailableLong-term effects of the different combinations of nutrient-management treatments were studied on crop yields of sorghum + cowpea in rotation with cotton + black gram. The effects of rainfall, soil temperature, and evaporation on the status of soil fertility and productivity of crops were also modeled and evaluated using a multivariate regression technique. The study was conducted on a permanent experimental site of rain-fed semi-arid Vertisol at the All-India Coordinated Research Project on Dryland Agriculture, Kovilpatti Centre, India, during 1995 to 2007 using 13 combinations of nutrient-management treatments. Application of 20 kg nitrogen (N) (urea) + 20 kg N [farmyard manure (FYM)] + 20 kg phosphorus (P) ha−1 gave the greatest mean grain yield (2146 kg ha−1) of sorghum and the fourth greatest mean yield (76 kg ha−1) of cowpea under sorghum + cowpea system. The same treatment maintained the greatest mean yield of cotton (546 kg ha−1) and black gram (236 kg ha−1) under a cotton + cowpea system. When soil fertility was monitored, this treatment maintained the greatest mean soil organic carbon (4.4 g kg−1), available soil P (10.9 kg ha−1), and available soil potassium (K) (411 kg ha−1), and the second greatest level of mean available soil N (135 kg ha−1) after the 13-year study. The treatments differed significantly from each other in influencing soil organic carbon (C); available soil N, P, and K; and yield of crops attained under sorghum + cowpea and cotton + black gram rotations. Soil temperature at different soil depths at 07:20 h and rainfall had a significant influence on the status of soil organic C. Based on the prediction models developed between long-term Received 7 April 2010; accepted 17 July 2011. Address correspondence to K. L. Sharma, Division of Resource Management, Central Research Institute for Dryland Agriculture, Santhoshnagar, P. O. Saidabad, Hyderabad 500 059, India. E-mail: [email protected] 756 Effect of Nutrient Treatments on Crop Productivity 757 yield and soil fertility variables, 20 kg N (urea) + 20 kg N (FYM) + 20 kg P ha−1 could be prescribed for sorghum + cowpea, and 20 kg N (urea) + 20 kg N (FYM) could be prescribed for cotton + black gram. These combinations of treatments would provide a sustainable yield in the range of 1681 to 2146 kg ha−1 of sorghum, 74 to 76 kg ha−1 of cowpea, 486 to 546 kg ha−1 of cotton, and 180 to 236 kg ha−1 of black gram over the years. Beside assuring greater yields, these soil and nutrient management options would also help in maintaining maximum soil organic C of 3.8 to 4.4 g kg−1 soil, available N of 126 to 135 kg ha−1, available soil P of 8.9 to 10.9 kg ha−1, and available soil K of 392 to 411 kg ha−1 over the years. These prediction models for crop yields and fertility status can help us to understand the quantitative relationships between crop yields and nutrients status in soil. Because black gram is unsustainable, as an alternative, sorghum + cowpea could be rotated with cNot Availabl

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Not AvailableLong-term field experiments were conducted at Agra, Solapur and Hisar from 2000 to 2008 to identify efficient tillage and nutrient management practices and to develop predictive models that would describe the relationship between crop yields and monthly rainfall for rainfed pearl millet grown on arid and semi-aridInceptisol, Vertisol and Aridisol soils. Nine treatments comprising a factorial combination of three tillage practices, viz., conventional tillage (CT), low tillage + interculture (LT1) and low tillage + herbicide (LT2) and three fertilizer treatments viz., 100% N from an organic source (F1), 50% organic N + 50% inorganic N (F2) and 100% inorganic N (F3) were tested in a split-plot design at the three locations. Studies revealed that tillage and fertilizer treatments, and their interactions, significantly influenced pearl millet grain yields at the three locations. Prediction models describing the relation between grain yield and monthly rainfall indicated that rainfall occurring in June, July and August at Agra; June and July at Solapur; and June and August at Hisar significantly influenced pearl millet grain yield attained by different treatments. The R2 values of the model ranged from 0.64 to 0.81 at Agra; 0.63 to 0.92 at Solapur, and 0.75 to 0.89 at Hisar. When averaged over all the treatment combinations, mean pearl millet grain yields varied from 1590 to 1744 kg ha−1 at Agra; 1424 to 1786 kg ha−1 at Solapur; and 1675 to 1766 kg ha−1 atHisar while their corresponding sustainability yield indice (SYI) varied from 35.4 to 42.2%, 19.9 to 45.6% and 64.1 to 68.3%, respectively. At Agra (Inceptisol), CTF3 resulted in significantly higher mean net returns (Rs 11 439 ha−1), benefit-cost ratio (2.33), rainwater use efficiency (RWUE) (3.52 kg ha−1 mm−1) and the second best SYI (39.9%). At Solapur (Vertisol), the LT1F3 resulted in significantly higher net returns (Rs 12 818 ha−1), benefit-cost ratio (3.52), RWUE (3.89 kg ha−1 mm−1) and the fourth best SYI (42.6%). At Hisar (Aridisol), the LT1F3 treatment gave higher net returns (Rs 3866 ha−1), benefit-cost ratio (1.26), RWUE (5.05 kg ha−1 mm−1) and the fourth best SYI (67.8%). These treatment combinations can be recommended for their respective locations to achieve maximum RWUE, productivity and profitability.Not Availabl

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Not AvailableInceptisols in the submountainous region of Indo-Gangetic Plains in India are known as low productive areas due to several constraints like decline in soil organic matter and fertility, deterioration of soil physical and biological properties. The present study was conducted with tillage as main treatments and integrated nutrient management as subtreatments to improve soil quality and to identify the key indicators of soil quality after 5 years of experimentation in maize–wheat cropping system at Ballowal Saunkhri. Conventional tillage (CT) + interculture (IC) maintained significantly higher soil quality indices (SQI) of 1.12 which was at par with 50% CT + IC + chemical weed control (CWC) (1.08). Application of nitrogen (N) through 50% (organic) + 50% (inorganic) maintained higher soil quality with SQI of 1.10 followed by application of 100% N through organics (1.08). The results indicated that reduction in the intensity of tillage to 50% with interculture practices and combined use of organic and inorganic fertilizers maintained higher soil quality in these degraded Inceptisols. The methods of principal component analysis and computation of SQI adopted will be highly useful to future researchers, land managers, and students at locations across the world having similar climatic and edaphic conditions.Not Availabl

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Not AvailableSoils in the hot, arid topical regions are low in organic matter and fertility and are structurally poor. Consequently, these soils suffer on account of poor physical, chemical, and biological soil quality traits, leading to miserably low crop yields. Long-term use of conjunctive nutrient management and conservation tillage practices may have a profound effect on improving the quality of these soils. Therefore, the objective of this study was to identify the key soil quality indicators, indices, and the best soiland nutrient-management practices that can improve soil quality on long-term basis for enhanced productivity under a pearl millet–based system. The studies were conducted for the Hissar Centre of All-India Coordinated Research Project at the Central Research Institute for Dryland Agriculture, Hyderabad. Conjunctive nutrient-use treatments and conservation tillage significantly influenced the majority of the soil quality parameters in both the experiments. In experiment 1, the key soil quality indicators that significantly contributed to soil quality in a rainfed pearl millet–mung bean system were available nitrogen (N, 35%), available zinc (Zn; 35%), available copper (Cu; 10%), pH (10%), available potassium (K; 5%), and dehydrogenase assay (5%). The three best conjunctive nutrient-use treatments in terms of soil quality indices (SQI) were T3, 25 kg N (compost) (1.52) >T6, 15 kg N (compost) + 10 kg N (inorganic) + biofertilizer (1.49) >T5, 15 kg N (compost) + 10 kg N (green leaf manure) (1.47). In experiment 2, under a rainfed pearl millet system, the key indicators and their percentage contributions were electrical conductivity (15%), available N (19%), exchangeable magnesium (Mg; 18%), available manganese (Mn; 13%), dehydrogenase assay (19%), microbial biomass carbon (C; 5%), and bulk density (11%). The three best tillage +nutrient treatments identified from the viewpoint of soil quality were T1, conventional tillage (CT) + two intercultures (IC) + 100% N (organic source/compost) (1.74) >T3, CT + two IC + 100% N (inorganic source) (1.74) >T4, low tillage + two IC + 100% N (organic source/compost) (1.70). The findings of the present study as well as the state-of-the-art methodology adopted could be of much interest and use to the future researchers including students, land managers, state agricultural officers, growers/farmers, and all other associated stakeholders. The prediction function developed between long-term pearl millet crop yields (y) and soil quality indices (x) in this study could be of much use in predicting the crop yields with a given change in soil quality index under similar situations.Not Availabl