Ceramic Micropalaeontology

Microfossils found in archaeological ceramics include representatives of kingdoms Fungi, Protista, Plantae, and Animalia and are composed of calcite, silica, or resistant organic compounds capable of withstanding firing. Methods by which microfossils are isolated for study vary considerably, but the best results involve the disaggregation of potsherds into their individual grains or by cutting petrological thin sections. Microfossils can be related directly to the age and depositional environment of the source materials (clays, temper, and slip) used in the manufacturing process, although the introduction of contaminants at the time of construction must also be recognized. When incorporated into an integrated analysis, the microfossils may demonstrate provenance; contribute to a better understanding of the local environment and landscape; identify transportation routes; contribute to an understanding of the technology used, including construction methods and firing; and elucidate the use to which the vessels were put

An integrated research framework combining genomics, systems biology, physiology, modelling and breeding for legume improvement in response to elevated CO2 under climate change scenario

How unprecedented changes in climatic conditions will impact yield and productivity of some crops and their response to existing stresses, abiotic and biotic interactions is a key global concern. Climate change can also alter natural species’ abundance and distribution or favor invasive species, which in turn can modify ecosystem dynamics and the provisioning of ecosystem services. Basic anatomical differences in C3 and C4 plants lead to their varied responses to climate variations. In plants having a C3 pathway of photosynthesis, increased atmospheric carbon dioxide (CO2) positively regulates photosynthetic carbon (C) assimilation and depresses photorespiration. Legumes being C3 plants, they may be in a favorable position to increase biomass and yield through various strategies. This paper comprehensively presents recent progress made in the physiological and molecular attributes in plants with special emphasis on legumes under elevated CO2 conditions in a climate change scenario. A strategic research framework for future action integrating genomics, systems biology, physiology and crop modelling approaches to cope with changing climate is also discussed. Advances in sequencing and phenotyping methodologies make it possible to use vast genetic and genomic resources by deploying high resolution phenotyping coupled with high throughput multi-omics approaches for trait improvement. Integrated crop modelling studies focusing on farming systems design and management, prediction of climate impacts and disease forecasting may also help in planning adaptation. Hence, an integrated research framework combining genomics, plant molecular physiology, crop breeding, systems biology and integrated crop-soil-climate modelling will be very effective to cope with climate change

Multi-criteria analysis and ex-ante assessment to prioritize and scale up climate smart agriculture in semi–arid tropics, India

The strategies that integrate food security, adaptation and mitigation options in agriculture are of high importance to manage the increasing risk of climate change in vulnerable semi-arid regions for the livelihood security of poor agriculture-dependent people. To address the growing problems of food security and climate change, multiple institutions and programs have demonstrated evidences for developing Climate-Smart Villages (CSVs) across regions which can act as a sustainable model for adapting to changing climate and improve farmers’ welfare. However, it remain a major challenge to upscale CSV approach. This paper presents a framework and evidence based designing of a strategy for scaling up Climate Smart Agriculture (CSA) in Telangana State of India. Climate risk and vulnerability mapping at disaggregate level; Inventory of CSA practices and respective technical coefficients; multicriteria analysis for participatory prioritization of location specific CSA practices and identification of barriers and incentives; ex-ante impact analysis of potential adoption and investment and infrastructure needs to implement CSA practices at local level and strategy for CSA integration into district level plans have been the key steps of this CSV approach. Local level vulnerability assessments and participatory prioritization based on index calculated for climate smartness and ease of adoption for each proposed practice, formed the basis of prioritizing CSA interventions suitable for particular location. Further the ex-ante impact analysis of selected climate smart interventions in different regions of Telangana was the next step. We also generated relevant geospatial maps for irrigated as well as rainfed major crops under vertisols and light soils. These maps helped in identifying context specificity of CSA interventions. Based on participatory prioritization, five CSA practices such as Ridges and Furrows, Broad bed and furrow for soil and moisture conservation and drainage, Farm pond for critical/supplemental irrigation, Crop residue management (cotton) and drip irrigation system were considered for ex-ante assessment considering district wise actual area and yields of major crops and rainfall level for 5 years from 2010-11 to 2014-15. The proposed framework and different tools help to understand the district wise potential for promotion of CSA practices/technologies, public and private investment needs, economic impacts of the interventions to enable informed decision making for climate smart agriculture. Stakeholders’ consultations during different stages of this process was very important for integrating their perspective and creating ownership. Piloting of evidence based scientific framework guides investments and policy making decisions on scaling up CSA in Telangana state

Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud

The South Asia (India, Pakistan, Bangladesh, Nepal, Sri Lanka and Bhutan) has a staggering 900 million people (~43% of the population) who face food insecurity or severe food insecurity as per United Nations, Food and Agriculture Organization’s (FAO) the Food Insecurity Experience Scale (FIES). The existing coarse-resolution (≥250-m) cropland maps lack precision in geo-location of individual farms and have low map accuracies. This also results in uncertainties in cropland areas calculated fromsuch products. Thereby, the overarching goal of this study was to develop a high spatial resolution (30-m or better) baseline cropland extent product of South Asia for the year 2015 using Landsat satellite time-series big-data and machine learning algorithms (MLAs) on the Google Earth Engine (GEE) cloud computing platform. To eliminate the impact of clouds, 10 time-composited Landsat bands (blue, green, red, NIR, SWIR1, SWIR2, Thermal, EVI, NDVI, NDWI) were derived for each of the three timeperiods over 12 months (monsoon: Days of the Year (DOY) 151–300; winter: DOY 301–365 plus 1–60; and summer: DOY 61–150), taking the every 8-day data from Landsat-8 and 7 for the years 2013–2015, for a total of 30-bands plus global digital elevation model (GDEM) derived slope band. This 31-band mega-file big data-cube was composed for each of the five agro-ecological zones (AEZ’s) of South Asia and formed a baseline data for image classification and analysis. Knowledgebase for the Random Forest (RF) MLAs were developed using spatially well spread-out reference training data (N = 2179) in five AEZs. The classification was performed on GEE for each of the five AEZs using well-established knowledge-base and RF MLAs on the cloud. Map accuracies were measured using independent validation data (N = 1185). The survey showed that the South Asia cropland product had a producer’s accuracy of 89.9% (errors of omissions of 10.1%), user’s accuracy of 95.3% (errors of commission of 4.7%) and an overall accuracy of 88.7%. The National and sub-national (districts) areas computed from this cropland extent product explained 80-96% variability when compared with the National statistics of the South Asian Countries. The full-resolution imagery can be viewed at full-resolution, by zooming-in to any location in South Asia or the world, atwww.croplands. org and the cropland products of South Asia downloaded from The Land Processes Distributed Active Archive Center (LP DAAC) of National Aeronautics and Space Administration (NASA) and the United States Geological Survey (USGS): https://lpdaac.usgs.gov/products/gfsad30saafgircev001/

Monitoring rice fallows in India using MODIS time series data

Cereals and grain legumes are the most important part of human diet and nutrition. The rural population of low income groups in dry land areas of India depends on these staples. Expansion of grain legumes with improved productivity to cater the growing population’s nutritional security is of prime importance and need of the hour. Rice-fallows are best niche areas with residual moisture to grow short duration legumes there by achieving intensification. Identifying suitable areas for grain legumes and cereal grains is important in this region. In this context, the goal of this study was to map fallow lands followed by rainy season (kharif) rice cultivation or post rainy (rabi) fallows in rice growing environments for 2000-01 and 2010-11 using temporal moderate-resolution imaging Spectroradiometer (MODIS) data applying Spectral matching techniques. This study was conducted in India where different rice eco-systems exist. MODIS 16days normalized difference vegetation index (NDVI) at 250m spatial resolution and season wise intensive ground survey data were used to map rice systems and the fallows thereafter (rabi-fallows) in India. The rice maps were validated with independent ground survey data and compared with available sub-national level statistics. Overall accuracy and kappa coefficient estimated for rice classes were 81.5% and 0.79 respectively with ground survey data. The derived physical rice area and irrigated areas were highly correlated with the sub-national statistics with R2 values of 84% at the district level for the year 2000-01 and 2010-11. Results clearly show that rice-fallows areas increased from 2000 when compared 2010. The results show spatial distribution of rice-fallows in India which are identified as target domains for sustainable intensification of short duration grain legumes, fixing the soil nitrogen and increasing incomes of small holder farmers

Innovation platforms as vehicle to strengthen stakeholders capacity to innovate for improved livelihoods in drylands in Asia and Sub Saharan Africa

Agriculture is the engine for poverty reduction and economic development in the developing nations The sector employs over 50 of the population in South Asia SA and Sub Saharan Africa SSA and contributes significantly to their Gross Domestic Product GDP McCullough Pingali and Stamoulis 2008 Majority of agricultural lands in these regions are drylands and vulnerable to droughts of various intensities These threats are far more pronounced in the semiarid and arid regions Globally drylands occupy some 609 billion ha with a population of 21 billion people nearly half of which are the poorest and most vulnerable and marginalized in the world UN 2013 Despite the importance of dryland agriculture for the livelihood security of millions of rural people the level of innovations and technological change in the sector continues to be slow and patchy Access to and adoption of technologies and innovations remain very low resulting in low productivity resource degradation and persistent poverty Many developing countries are now working towards improving rural livelihoods of smallholder farmers However achieving this goal will require transforming the traditional top down technologydriven extension model to a more decentralized farmerled and marketdriven extension system Innovation has become a focus of dryland agriculture development and innovation systems are the centre piece of many development projects These Innovation systems IS approaches emphasize the collective dimension of innovation pointing to the need to effect necessary linkages and interaction among multiple actors IS thinking also pays attention to the coevolution of innovation processes arguing that successful innovation results from alignment of technical social institutional and organizational dimensions Hall 2005 Hall 2007 These insights are increasingly informing interventions that focus on supporting multistakeholder arrangements such as innovation platforms IPs as mechanisms for enhancing agriculture innovations

Limits of conservation agriculture to overcome low crop yields in sub-Saharan Africa

Conservation agriculture (CA) has become a dominant paradigm in scientific and policy thinking about the sustainable intensification of food production in sub-Saharan Africa. Yet claims that CA leads to increasing crop yields in African smallholder farming systems remain controversial. Through a meta-analysis of 933 observations from 16 different countries in sub-Saharan African studies, we show that average yields under CA are only slightly higher than those of conventional tillage systems (3.7% for six major crop species and 4.0% for maize). Larger yield responses for maize result from mulching and crop rotations/intercropping. When CA principles are implemented concomitantly, maize yield increases by 8.4%. The largest yield benefits from CA occur in combination with low rainfall and herbicides. We conclude that although CA may bring soil conservation benefits, it is not a technology for African smallholder farmers to overcome low crop productivity and food insecurity in the short term

Identifying Low Emissions Development Pathways – Synergies and Trade-offs: A Case Study of Mahbubnagar District, Telangana, India

This case study examines the opportunities for obtaining synergies between agricultural productivity, whole-farm profitability and greenhouse gas (GHG) mitigation and highlights where trade-offs exist. We explore how agricultural practices and systems can be designed and managed to balance the synergies and trade-offs for small-holder farmers in semi-arid India. We used data on farm-household characteristics and agricultural practices from 100 farm-households of Telangana state, India. Quantifying synergies and trade-offs between profitability, adaptation and mitigation we employed simulation modelling- crop, livestock and whole-farm simulation models, and Cool Farm Tool to estimate net GHG emissions. Our analysis reveals that specific plot-level crop management strategies and farm-level enterprise interventions can increase profitability as well as benefit climate change mitigation. It depict how farming systems can be managed to achieve synergies between profitability and mitigation outcomes and where, if any trade-offs exist. Combinations of reduced tillage, retaining crop-residue, improved nitrogen management, utilizing organic manure, improved livestock feeding practices, introducing agro-forestry could contribute to GHG abatement and improved profitability at our study site. Such multi-model systems analysis using participatory design and tools could help practitioners and policymakers to identify and promote use of management practices that can help achieve multiple objectives and guide investments towards synergistic climate smart agriculture strategies. Our study contributes empirical evidence to the debate surrounding integrated approaches to sustainable development goals and adaptation and mitigation objectives

Assessing the Potential for Zone-Specific Management of Cereals in Low-Rainfall South-Eastern Australia: Combining On-Farm Results and Simulation Analysis

In the low-rainfall region of south-eastern Australia, distinctive soil types reflecting the typical landscape of higher elevated dunes and swale zones at the bottom can be found within one field. Different soil characteristics cause consequently large variability in cropping productivity between soils and across seasons. To assess the possibilities for zone-specific management, five farmer fields were zoned into a dune, mid-slope and swale zone. For each site, zone yields were mapped over 2 years and soil properties were surveyed. This information was used to parameterize and validate the APSIM model for each zone. Field-measured PAWC increased from the dune to the swale zone. On-farm results and simulation analysis showed distinctive yield performance of the three designed zones. However, yield is not related to PAWC, it is rather a complex relationship between soil type, fertility and rainfall. While in high-rainfall years, the swale zones yielded higher due to higher soil organic carbon content and less drainage losses, the dune zones performed better in the low-rainfall years due to lower evaporation losses. This study emphasizes that in this specific environment where soil variation in texture and subsoil constraints strongly influence crop performance, mechanistic crop models and long-term field observations are necessary for better understanding of zone-specific performance, and simple linear relationships across years or sites are not useful

Estimating organic surface horizon depth for peat and peaty soils across a Scottish upland catchment using linear mixed models with topographic and geological covariates

In order to evaluate and protect ecosystem services provided by peat and peaty soils, accurate estimations for the depth of the surface organic horizon are required. This study uses linear mixed models (LMMs) to test how topographic (elevation, slope, aspect) and superficial geology parameters can contribute to improved depth estimates across a Scottish upland catchment. Mean (n = 5) depth data from 283 sites (representing full covariate ranges) were used to calibrate LMMs, which were tested against a validation dataset. Models were estimated using maximum likelihood, and the Akaike Information Criterion was used to test whether the iterative addition of covariates to a model with constant fixed effects was beneficial. Elevation, slope and certain geology classes were all identified as useful covariates. Upon addition of the random effects (i.e. spatial modelling of residuals), the RMSE for the model with constant‐only fixed effects reduced by 24%. Addition of random effects to a model with topographic covariates (fixed effects = constant, slope, elevation) reduced the RMSE by 13%, whereas the addition of random effects to a model with topographic and geological covariates (fixed effects = constant, slope, elevation, certain geology classes) reduced the RMSE by only 3%. Therefore, much of the spatial pattern in depth was explained by the fixed effects in the latter model. The study contributes to a growing research base demonstrating that widely available topographic (and also here geological) datasets, which have national coverage, can be included in spatial models to improve organic horizon depth estimations