scholarly journals Understanding on Climate Resilient Practices for Rainfed Rice-A Review

2020 ◽  
Author(s):  
Perves Ahmed ◽  
Mrinal Saikia
Keyword(s):  
Global Climate ◽  
Nutrient Content ◽  
Yield Potential ◽  
Sowing Time ◽  
Global Climate Changes ◽  
Rainfed Rice ◽  
Rice Area ◽  
Sowing Dates ◽  
Scientific Approach ◽  
Weather Parameters

Rice is one of the major staples feeding around 3.5 billion people in the world. Rainfed rice area account for 33% of the total world rice production. Its cultivation is vulnerable to changes in temperature and rainfall. Due to global climate changes, it is essential to analyze and characterize the weather parameters like rainfall, temperature, solar radiation etc., over a region for identifying location specific sowing time. A scientific approach based on appropriate understanding of weather resources and its application for efficient crop management can help in achieving higher productivity. Various studies revealed that changes in microclimate by altering sowing dates has great role to play during vegetative and reproductive stages of the crop and also nutrient content and its uptake which ultimately affects the yield potential. Studies also showed that delayed sowing and transplanting time can significantly affect on the infestation of pest and diseases.

scholarly journals Sowing time affecting the development of common bean cultivars in Lichinga, Province of Niassa, Mozambique

Revista CERES ◽  
2017 ◽  
Vol 64 (5) ◽  
pp. 532-539
Author(s):  
Maria da Conceição Santana Carvalho ◽  
Adriano Stephan Nascente ◽  
Gilvan Ferreira Barbosa ◽  
Celso Américo Pedro Mutadiua ◽  
José Eloir Denardin
Keyword(s):  
Grain Yield ◽  
Common Bean ◽  
Rainy Season ◽  
Agricultural Research ◽  
Yield Potential ◽  
Sowing Time ◽  
Growing Seasons ◽  
Sowing Dates ◽  
In The Beginning ◽  
And Control

ABSTRACT The demonstration of yield potential of crops depends on genetic factors, favorable conditions of envi ronment, and management. The sowing time can significantly affect the common bean grain yield. The aim of this research was to study the behavior of Brazilian cultivars and sowing times on the yield components and grain yield of common bean grown in the environmental conditions of Lichinga, Province of Niassa, Mozambique. The field trial was performed for two growing seasons, using the experimental as a randomized block in factorial 5 × 3 × 2, with four replications. The treatments consisted of the combination of five common bean cultivars (BRS Pontal, BRS Agreste, Perola, and BRS Requinte, developed by Brazilian Agricultural Research Corporation (Embrapa), and a local variety, Encarnada) with three sowing dates (beginning of the rainy season, and 15 and 30 days after), during two growing seasons. The Brazilian cultivar of common beans BRS Pontal was the most productive in all sowing times, followed by BRS Agreste, which was not the most productive only in the second sowing time of 2013/2014 growing season. The cultivar Encarnada, from Mozambique, was the less productive cultivar in all sowing times and in all growing seasons. The best sowing time for common bean cultivars is in the beginning of the rainy season. The use of technologies such as use of seeds of new cultivars, proper sowing time, fertilization, and control of weeds allow significant increase of common bean grain yield in Lichinga, Mozambique.

scholarly journals Genetic, Phenotypic, and Commercial Characterization of an Almond Collection from Sardinia

Plants ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 86 ◽  
Author(s):  
Emma Rapposelli ◽  
Maria Rigoldi ◽  
Daniela Satta ◽  
Donatella Delpiano ◽  
Sara Secci ◽  
...  
Keyword(s):  
Global Climate ◽  
Industrial Applications ◽  
Yield Potential ◽  
High Yield ◽  
Phenotypic Traits ◽  
Industrial Processing ◽  
High Oleic ◽  
Global Climate Changes ◽  
Content Ratio ◽  
Wide Range

Background: Recent nutritional and medical studies have associated the regular consumption of almonds with a wide range of health benefits. As a consequence, kernel quality has become an important goal for breeding, considering not only the chemical composition conferring a specific organoleptic quality but also physical traits related to industrial processing. Methods: We characterized an almond collection from Sardinia through analysis of 13 morpho-physiological traits and eight essential oil profiles. The genetic structure of the collection was studied by analyzing the polymorphism of 11 simple sequence repeats (SSR). Results: Both commercial and phenotypic traits showed wide ranges of variation. Most genotypes were early flowering with low yield potential. Several genotypes showed moderate to high yield and very interesting oil compositions of kernels. Based on 11 SSR profiles and Bayesian clustering, the Sardinian cultivars were assigned to groups which were differentiated for several agronomic and commercial traits. Conclusions: Several cultivars showed a high kernel oil content and high oleic to linoleic content ratio. Based on morphological traits, we propose that some of the analyzed cultivars could be interesting for industrial applications. Finally, we highlight the importance of characterizing early blooming cultivars for sites which are experiencing a rise in mean temperatures due to the effects of global climate changes.

scholarly journals FAO AquaCrop model for determining optimum wheat sowing date in Tarai region of Uttarakhand

MAUSAM ◽  
2022 ◽  
Vol 73 (1) ◽  
pp. 189-192
Author(s):  
NEHA PAREEK ◽  
SUMANA ROY ◽  
A.S. NAIN ◽  
SMITA GUPTA ◽  
GAURAVKUMAR CHATURVEDI
Keyword(s):  
Crop Yields ◽  
Wheat Yield ◽  
Field Research ◽  
Sowing Date ◽  
Yield Potential ◽  
Research Centre ◽  
Wheat Crop ◽  
Sowing Time ◽  
Sowing Dates ◽  
Aquacrop Model

The ideal sowing period is critical for maximizing the crop's yield potential under specific agroclimatic conditions (Nain, 2016; Patra et al., 2017). It influences the phenological stages of the crop's development and, as a result, the efficient conversion of biomass into economic yield. During rabi 2013-14, a field research was done at GBPUA&T's Borlaug Crop Research Centre to determine the best sowing dates for wheat crops employing Aquacrop model. Aquacrop model has been calibrated against vegetative and economic yield forthree sowing dates, viz., 3rd December, 18th December and 3rd January (Pareek et al., 2017). After calibrating the Aquacrop model, a set of conservative variables was obtained (Pareek et al., 2017). Afterward, the calibrated Aquacrop model was used to validate wheat yield and biomass for three years in a row, namely 2010-11, 2011-12 and 2012-13. The model subsequently used to simulate yield under different sowing dates. For all of the tested years, the simulation findings of the Aquacrop model reflected the observed crop yields and biomass of wheat. The model was used to simulate the optimum sowing week based on varying sowing dates and produced grain yield for a period of 10 years (Malik et al., 2013). The average and assured yield of wheat was worked out based on probability analysis (60, 75 and 90%). The optimum sowing time for Tarai region of Uttarakhand was suggested as first week of November followed by second week of November (Nain, 2016). In no case wheat should be sown during third week of November and beyond due to poor assured yield and average yield (Nain, 2016). The finding of the studies will help to increase productivity and production of wheat crop in Tarai region of Uttarakhand.  

Antarctic Climate History and Global Climate Changes

2017 ◽  
Author(s):  
Pontus Lurcock ◽  
Fabio Florindo
Keyword(s):  
Ocean Circulation ◽  
Climate Changes ◽  
Numerical Models ◽  
Global Climate ◽  
Sediment Cores ◽  
Ice Sheets ◽  
Global Ocean ◽  
Climate History ◽  
Global Climate Changes ◽  
Planetary Albedo

Antarctic climate changes have been reconstructed from ice and sediment cores and numerical models (which also predict future changes). Major ice sheets first appeared 34 million years ago (Ma) and fluctuated throughout the Oligocene, with an overall cooling trend. Ice volume more than doubled at the Oligocene-Miocene boundary. Fluctuating Miocene temperatures peaked at 17–14 Ma, followed by dramatic cooling. Cooling continued through the Pliocene and Pleistocene, with another major glacial expansion at 3–2 Ma. Several interacting drivers control Antarctic climate. On timescales of 10,000–100,000 years, insolation varies with orbital cycles, causing periodic climate variations. Opening of Southern Ocean gateways produced a circumpolar current that thermally isolated Antarctica. Declining atmospheric CO2 triggered Cenozoic glaciation. Antarctic glaciations affect global climate by lowering sea level, intensifying atmospheric circulation, and increasing planetary albedo. Ice sheets interact with ocean water, forming water masses that play a key role in global ocean circulation.

scholarly journals Future climate change will impact the size and location of breeding and wintering areas of migratory thrushes in South America

The Condor ◽  
2021 ◽  
Author(s):  
Natália Stefanini Da Silveira ◽  
Maurício Humberto Vancine ◽  
Alex E Jahn ◽  
Marco Aurélio Pizo ◽  
Thadeu Sobral-Souza
Keyword(s):  
Climate Change ◽  
South America ◽  
Migratory Birds ◽  
Global Climate ◽  
Future Climate ◽  
Future Climate Change ◽  
Ecological Niche Models ◽  
Global Climate Changes ◽  
The Andes ◽  
Wintering Areas

Abstract Bird migration patterns are changing worldwide due to current global climate changes. Addressing the effects of such changes on the migration of birds in South America is particularly challenging because the details about how birds migrate within the Neotropics are generally not well understood. Here, we aim to infer the potential effects of future climate change on breeding and wintering areas of birds that migrate within South America by estimating the size and elevations of their future breeding and wintering areas. We used occurrence data from species distribution databases (VertNet and GBIF), published studies, and eBird for 3 thrush species (Turdidae; Turdus nigriceps, T. subalaris, and T. flavipes) that breed and winter in different regions of South America and built ecological niche models using ensemble forecasting approaches to infer current and future potential distributions throughout the breeding and wintering periods of each species. Our findings point to future shifts in wintering and breeding areas, mainly through elevational and longitudinal changes. Future breeding areas for T. nigriceps, which migrates along the Andes Mountains, will be displaced to the west, while breeding displacements to the east are expected for the other 2 species. An overall loss in the size of future wintering areas was also supported for 2 of the species, especially for T. subalaris, but an increase is anticipated for T. flavipes. Our results suggest that future climate change in South America will require that species shift their breeding and wintering areas to higher elevations in addition to changes in their latitudes and longitude. Our findings are the first to show how future climate change may affect migratory birds in South America throughout the year and suggest that even closely related migratory birds in South America will be affected in different ways, depending on the regions where they breed and overwinter.

scholarly journals A Process-Oriented Approach to Identify Evolutions of Sea Surface Temperature Anomalies with a Time-Series of a Raster Dataset

2021 ◽  
Vol 10 (8) ◽  
pp. 500
Author(s):  
Lianwei Li ◽  
Yangfeng Xu ◽  
Cunjin Xue ◽  
Yuxuan Fu ◽  
Yuanyu Zhang
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Global Climate ◽  
Sea Surface ◽  
Temperature Anomalies ◽  
Global Climate Changes ◽  
Space And Time ◽  
Novel Approach ◽  
Sea Surface Temperature Anomalies

It is important to consider where, when, and how the evolution of sea surface temperature anomalies (SSTA) plays significant roles in regional or global climate changes. In the comparison of where and when, there is a great challenge in clearly describing how SSTA evolves in space and time. In light of the evolution from generation, through development, and to the dissipation of SSTA, this paper proposes a novel approach to identifying an evolution of SSTA in space and time from a time-series of a raster dataset. This method, called PoAIES, includes three key steps. Firstly, a cluster-based method is enhanced to explore spatiotemporal clusters of SSTA, and each cluster of SSTA at a time snapshot is taken as a snapshot object of SSTA. Secondly, the spatiotemporal topologies of snapshot objects of SSTA at successive time snapshots are used to link snapshot objects of SSTA into an evolution object of SSTA, which is called a process object. Here, a linking threshold is automatically determined according to the overlapped areas of the snapshot objects, and only those snapshot objects that meet the specified linking threshold are linked together into a process object. Thirdly, we use a graph-based model to represent a process object of SSTA. A node represents a snapshot object of SSTA, and an edge represents an evolution between two snapshot objects. Using a number of child nodes from an edge’s parent node and a number of parent nodes from the edge’s child node, a type of edge (an evolution relationship) is identified, which shows its development, splitting, merging, or splitting/merging. Finally, an experiment on a simulated dataset is used to demonstrate the effectiveness and the advantages of PoAIES, and a real dataset of satellite-SSTA is used to verify the rationality of PoAIES with the help of ENSO’s relevant knowledge, which may provide new references for global change research.

Application of physiological understanding in soybean improvement. I. Understanding phenological constraints to adaptation and yield potential

2011 ◽  
Vol 62 (1) ◽  
pp. 1 ◽  
Author(s):  
R. J. Lawn ◽  
A. T. James
Keyword(s):  
Seed Yield ◽  
Environmental Control ◽  
Thermal Environment ◽  
Sowing Date ◽  
Companion Paper ◽  
Interaction Effects ◽  
Yield Potential ◽  
Environment Interaction ◽  
Sowing Dates ◽  
Eastern Australia

The purpose of this paper and its companion1 is to describe how, in eastern Australia, soybean improvement, in terms of both breeding and agronomy, has been informed and influenced over the past four decades by physiological understanding of the environmental control of phenology. This first paper describes how initial attempts to grow soybean in eastern Australia, using varieties and production practices from the southern USA, met with limited success due to large variety × environment interaction effects on seed yield. In particular, there were large variety × location, variety × sowing date, and variety × sowing date × density effects. These various interaction effects were ultimately explained in terms of the effects of photo-thermal environment on the phenology of different varieties, and the consequences for radiation interception, dry matter production, harvest index, and seed yield. This knowledge enabled the formulation of agronomic practices to optimise sowing date and planting arrangement to suit particular varieties, and underpinned the establishment of commercial production in south-eastern Queensland in the early 1970s. It also influenced the establishment and operation over the next three decades of several separate breeding programs, each targeting phenological adaptation to specific latitudinal regions of eastern Australia. This paper also describes how physiological developments internationally, particularly the discovery of the long juvenile trait and to a lesser extent the semi-dwarf ideotype, subsequently enabled an approach to be conceived for broadening the phenological adaptation of soybeans across latitudes and sowing dates. The application of this approach, and its outcomes in terms of varietal improvement, agronomic management, and the structure of the breeding program, are described in the companion paper.

Regional model assessments of fire risks under global climate changes

2006 ◽  
Vol 411 (2) ◽  
pp. 1485-1488 ◽  
Author(s):  
I. I. Mokhov ◽  
A. V. Chernokulsky ◽  
I. M. Shkolnik
Keyword(s):  
Climate Changes ◽  
Global Climate ◽  
Regional Model ◽  
Global Climate Changes
Sign in / Sign up

Export Citation Format

Share Document