Phenotyping Chickpeas and Pigeonpeas for Adaptation to Drought

The chickpea and pigeonpea are protein-rich grain legumes used for human consumption in many countries. Grain yield of these crops is low to moderate in the semi-arid tropics with large variation due to high GxE interaction. In the Indian subcontinent chickpea is grown in the post-rainy winter season on receding soil moisture, and in other countries during the cool and dry post winter or spring seasons. The pigeonpea is sown during rainy season which flowers and matures in post-rainy season. The rainy months are hot and humid with diurnal temperature varying between 25 and 35°C (maximum) and 20 and 25°C (minimum) with an erratic rainfall. The available soil water during post-rainy season is about 200–250 mm which is bare minimum to meet the normal evapotranspiration. Thus occurrence of drought is frequent and at varying degrees. To enhance productivity of these crops cultivars tolerant to drought need to be developed. ICRISAT conserves a large number of accessions of chickpea (>20,000) and pigeonpea (>15,000). However only a small proportion (<1%) has been used in crop improvement programs mainly due to non-availability of reliable information on traits of economic importance. To overcome this, core and mini core collections (10% of core, 1% of entire collection) have been developed. Using the mini core approach, trait-specific donor lines were identified for agronomic, quality, and stress related traits in both crops. Composite collections were developed both in chickpea (3000 accessions) and pigeonpea (1000 accessions), genotyped using SSR markers and genotype based reference sets of 300 accessions selected for each crop. Screening methods for different drought-tolerant traits such as early maturity (drought escape), large and deep root system, high water-use efficiency, smaller leaflets, reduced canopy temperature, carbon isotope discrimination, high leaf chlorophyll content (drought avoidance), and breeding strategies for improving drought tolerance have been discussed

Satellite imagery and household survey for tracking chickpea adoption in Andhra Pradesh, India

The objective of this study was to map the temporal changes in chickpea cropped area over the last decade in Andhra Pradesh using remote-sensing imagery. Moderate Resolution Imaging Spectroradiometer (MODIS) data composited for every 16 days were used to map the spatial distribution of seasonal crop extent in Andhra Pradesh. MODIS derived 16 day normalized difference vegetation index (NDVI) and maximum value composite (MVC) with seasonal ground survey information for the years 2005–2006 and 2012–2013 were used. A subset of ground survey information was also used to assess the pixel-based accuracies of the MODIS-derived major cropland extent. Chickpea-growing areas were identified and mapped based on their characteristic growing periods during the post-rainy season. Significant growth in the chickpea-growing areas was observed in the four districts of Andhra Pradesh between 2001 and 2012. The area cropped to chickpea almost tripled from 0.22 million ha during 2000–2001 to 0.6 million ha by 2012–2013. Furthermore, survey data were also used to assess the accuracy of the MODIS estimates of chickpea-growing areas. When compared with ground survey, the 10 land-use and land-cover classes derived from the MODIS temporal imagery resulted in overall accuracies of 86% of actual. The accuracy of areas identified as cropped to chickpea was 94%. To complement this remote-sensing study, a state-level representative primary household survey was conducted to elicit information on the socio-economic characteristics of chickpea-growing farmers, the extent of adoption of improved cultivars, costs and returns from chickpea cultivation, competitiveness of chickpea with other post-rainy crops, etc. during 2012–13. The findings revealed that nearly 98% of the chickpea cropped area is now under improved cultivars, with an average increase in yield of 37% over yields achieved with unimproved varieties. The average annual per capita incomes have increased to US$ 1.89 day−1 with this silent chickpea revolution across the rain-fed areas of Andhra Pradesh

Ambient-aware continuous care through semantic context dissemination

Background: The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e. g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate. The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data. Methods: The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability. Results: A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered. Conclusions: The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results

Recent advances in Pigeonpea [Cajanus cajan (L.) Millspaugh) Research

Pigeonpea or red gram [Cajanus cajan (L.) Millspaugh] is an important food legume of the semi-arid tropics of Asia and Africa. It occupies a prime niche in sustainable farming systems of smallholder rainfed farmers. It occupies a prominent place in Indian rainfed agriculture. It is an integral component in various agro ecologies of the country mainly inter cropped with cereals, pulses, oilseeds and millets. It is the second most important pulse crop next to chickpea, covering an area of around 4.42 m ha (occupying about 14.5% of area under pulses) and production of 2.86 MT (contributing to 16% of total pulse production) and productivity of about 707 kg/ha. It is mainly consumed as dry split dhal throughout the country besides several other uses of various parts of pigeonpea plant. Enhancing the productivity of the crop assumes specific significance in India mainly to combat protein malnutrition as it is the main source of protein to the predominant vegetarian population. The productivity of pigeonpea has remained low and stagnant over the last few decades thus this prompted scientists to search for novel ways of crop improvement. To tackle this challenge, ICRISAT and IIPR are working on number of innovative ideas like, genome sequencing (Varshney et al. 2012), development of CGMS hybrids with 30 to 40 % yield advantage over traditional varieties, development of photo insensitive super early maturing lines, introgression of cleistogamous flower structure to maintain genetic purity of elite lines, use of obcordate leaf shape as NEP to assess genetic purity of hybrid parental lines and development of disease resistant hybrids and elite breeding lines. These aspects are described briefly in this paper..

Phenotyping chickpeas and pigeonpeas for adaptation to drought

Importance of chickpeas and pigeonpeas in the human diet Chickpeas (Cicer arietinum L) are the fourth largest grain legume crop in the world, with a total production of 9.2 million tons from an area of 11.2 million ha and a productivity of 0.82t ha1 (Food and Agriculture Organization of the United Nations (FAO), 2005). Large variations in chickpea yield are reported, ranging from 0.35t ha1 in Iran to 1.6t ha1 in Mexico. Chickpea productivity records in the last four decades reveal an interesting trend: productivity consistently increased in India and Mexico while it declined in Turkey, Pakistan and Iran..

Monitoring changes in the cultivation of pigeonpea and groundnut in Malawi using time series satellite imagery for sustainable food systems

Malawi, in south-eastern Africa, is one of the poorest countries in the world. Food security in the country hinges on rainfed systems in which maize and sorghum are staple cereals and groundnut and pigeonpea are now major grain legume crops. While the country has experienced a considerable reduction in forest lands, population growth and demand for food production have seen an increase in the area dedicated to agricultural crops. From 2010, pigeonpea developed into a major export crop, and is commonly intercropped with cereals or grown in double-up legume systems. Information on the spatial extent of these crops is useful for estimating food supply, understanding export potential, and planning policy changes as examples of various applications. Remote sensing analysis offers a number of efficient approaches to deliver spatial, reproducible data on land use and land cover (LULC) and changes therein. Moderate Resolution Imaging Spectroradiometer (MODIS) products (fortnightly and monthly) and derived phenological parameters assist in mapping cropland areas during the agricultural season, with explicit focus on redistributed farmland. Owing to its low revisit time and the availability of long-term period data, MODIS offers several advantages, e.g., the possibility of obtaining cloud-free Normalized Difference Vegetation Index (NDVI) profile and an analysis using one methodology applied to one sensor at regular acquisition dates, avoiding incomparable results. To assess the expansion of areas used in the production of pigeonpea and groundnut resulting from the release of new varieties, the spatial distribution of cropland areas was mapped using MODIS NDVI 16-day time-series products (MOD13Q1) at a spatial resolution of 250 m for the years 2010–2011 and 2016–2017. The resultant cropland extent map was validated using intensive ground survey data. Pigeonpea is mostly grown in the southern dry districts of Mulanje, Phalombe, Chiradzulu, Blantyre and Mwanza and parts of Balaka and Chikwawa as a groundnut-pigeonpea intercrop, and sorghum-pigeonpea intercrop in Mzimba district. By 2016, groundnut extent had increased in Mwanza, Mulanje, and Phalombe and fallen in Mzimba. The result indicates that the area planted with pigeonpea had increased by 29% (75,000 ha) from 2010–2011 to 2016–2017. Pigeonpea expansion in recent years has resulted from major export opportunities to Asian countries like India, and its consumption by Asian expatriates all over the world. This study provides useful information for policy changes and the prioritization of resources allocated to sustainable food production and to support smallholder farmer