scholarly journals Water management in wheat using non-traditional techniques

MAUSAM ◽  
2022 ◽  
Vol 53 (3) ◽  
pp. 329-336
Author(s):  
S. D. ATTRI ◽  
ANUBHA KAUSHIK ◽  
L. S. RATHORE ◽  
B. LAL
Keyword(s):  
Water Availability ◽  
Agricultural Production ◽  
Simulation Models ◽  
Sustainable Yield ◽  
Management Options ◽  
Yield Level ◽  
Optimum Utilization ◽  
Computer Based ◽  
Weather Parameters ◽  
Tactical Decisions

Water is one of the most limiting resources for agricultural production. Due to uneven distribution of rainfall, supplemental irrigation is often required to produce sustainable yield level. Timing and frequency of irrigation is one of the most important tactical decisions, which a farmer has to make to maximize profit from limited water availability. Computer based dynamic simulation models have the capability to assess management options under different environments to help in decision making. In this study, CRESS-Wheat Model  V-3.5 has been utilized to quantify the optimum utilization of limited water for popular wheat genotypes of NW India for operational use in Agrometeorological Advisory services with routinely measured weather parameters.

Impact of Minor Irrigation on Agricultural Production: An Inter-Crop Analysis in Odisha

2021 ◽  
pp. 298-304
Keyword(s):  
Water Availability ◽  
Agricultural Production ◽  
The Other ◽  
Farm Households ◽  
Input Use ◽  
Irrigation Potential ◽  
Use Efficiency ◽  
The Impact ◽  
Upstream Control ◽  
Minor Irrigation

The study examined the impact of minor irrigation on agricultural production and evaluated the gap between IPC and IPU in the Keonjhar district of Odisha. For this rationale, data were collected from 210 farm households through the primary survey. In support of the analysis, the Cobb Douglas model and factor analysis were used. The results revealed that the input use efficiency had a positive and significant impact on paddy production the most in all the MIPs regions compared to the other crops. However, the study indicated that insufficient water availability was the major cause behind the gap between irrigation potential created and utilised. Thus, minor irrigation played a crucial role in enhancing agricultural production in hilly regions. With the enthusiastic participation of planners, effective working of Pani Panchayats, canals, and upstream control, NGOs' involvement can achieve selfsufficiency in agricultural production by encouraging minor irrigation projects in the hilly province.

scholarly journals ROOT SYSTEM IN MAIZE PLANTS CULTIVATED UNDER WATER DEFICIT AND APPLICATION OF CHITOSAN

2020 ◽  
Vol 19 ◽  
pp. 11
Author(s):  
LORENA GABRIELA ALMEIDA ◽  
EDER MARCOS DA SILVA ◽  
PAULO CÉSAR MAGALHÃES ◽  
DÉCIO KARAM ◽  
CAROLINE OLIVEIRA DOS REIS ◽  
...  
Keyword(s):  
Plant Growth ◽  
Root System ◽  
Water Deficit ◽  
Water Availability ◽  
Agricultural Production ◽  
Negative Effects ◽  
Flowering Stage ◽  
Specific Behavior ◽  
Maize Plants ◽  
Corn Plants

Low water availability is characterized as an abiotic stressthat limits the agricultural production. Due to the physical and chemicalcharacteristics of the chitosan (CHT), this substance might stimulatephysiological responses on plants to tolerate the water deficit. In this sense,we submitted corn plants to water deficit and application of chitosan on theleaves (140 mg/L) during pre flowering stage. It were analyzed two cornhybrids genotypes contrasting for water deficit tolerance: DKB 390 (tolerant)and BRS1010 (sensitive). Then, we performed evaluations on the rootsystem and production components. Corn plants submitted to the applicationof chitosan presented a specific behavior: when compared the hybrids,the tolerant one presented a root system that was more developed and anexpressive agronomical yield. These results highlight the fact that the chitosanstimulates plant growth, enhancing their root system and contributing toincrease the availability and absorption of water and nutrients. The chitosanpresents a potential to reduce the negative effects of water deficit on the rootsystems, without compromising the agronomical yield.

Biological processes in modelling soil functions – a balancing act between too complex and too simple

2021 ◽  
Author(s):  
Sara König ◽  
Ulrich Weller ◽  
Thomas Reitz ◽  
Bibiana Betancur-Corredor ◽  
Birgit Lang ◽  
...  
Keyword(s):  
Nutrient Cycling ◽  
Microbial Activity ◽  
Community Dynamics ◽  
Spatial Scales ◽  
Simulation Models ◽  
Microbial Processes ◽  
Biological Processes ◽  
Soil Functions ◽  
Management Options ◽  
The Impact

<p>Mechanistic simulation models are an essential tool for predicting soil functions such as nutrient cycling, water filtering and storage, productivity and carbon storage as well as the complex interactions between these functions. Most soil functions are driven or affected by soil organisms. Yet, biological processes are often neglected in soil function models or implicitly described by rate parameters. This can be explained by the high complexity of the soil ecosystem with its dynamic and heterogeneous environment, and by the range of temporal and spatial scales these processes are taking place at. On the other hand, the technical capabilities to explore microbial activity and communities in soil has greatly improved, resulting in new possibilities to understand soil microbial processes on various scales.</p><p>However, to integrate such biological processes in soil modelling, we need to find the right level of detail. Here, we present a systemic soil model approach to simulate the impact of different management options and changing climate on soil functions integrating biological activity on the profile scale. We use stoichiometric considerations to simulate microbial processes involved in different soil functions without explicitly describing community dynamics or functional groups. With this approach we are able to mechanistically describe microbial activity and its impact on the turnover of organic matter and nutrient cycling as driven by agricultural soil management.</p><p>Further, we discuss general challenges and ongoing developments to additionally consider, e.g., microbe-fauna-interactions or microbial feedback with soil structure dynamics.</p>

scholarly journals Optimizing Genetic Parameters of CSM-CERES Wheat and CSM-CERES Maize for Durum Wheat, Common Wheat, and Maize in Italy

Agronomy ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 665
Author(s):  
Mereu ◽  
Gallo ◽  
Spano
Keyword(s):  
Grain Yield ◽  
Durum Wheat ◽  
Common Wheat ◽  
Crop Yield ◽  
Agricultural Production ◽  
Genetic Parameters ◽  
Management Practices ◽  
Estimation Method ◽  
Trial And Error ◽  
Management Options

The expected increase in population and the pressure posed by climate change on agricultural production require the assessment of future yield levels and the evaluation of the most suitable management options to minimize climate risk and promote sustainable agricultural production. Crop simulation models are widely applied tools to predict crop development and production under different management practices and environmental conditions. The aim of this study was to parameterize CSM-CERES-Wheat and CSM-CERES-Maize models, implemented in the Decision Support System for Agrotechnology Transfer (DSSAT) software, to predict phenology and grain yield of durum wheat, common wheat, and maize in different Italian environments. A 10-year (2001–2010) dataset was used to optimize the genetic parameters for selected varieties of each species and to evaluate the models considering several statistical indexes. The generalized likelihood uncertainty estimation method, and trial and error approach were used to optimize the cultivar-specific parameters of these models. Results show good model performances in reproducing crop phenology and yield for the analyzed crops, especially with the parameters optimized with the trial and error procedure. Highly significant (p ≤ 0.001) correlations between observed and simulated data were found for both anthesis and yield in model calibration and evaluation (p ≤ 0.01 for grain yield of maize in model evaluation). Root mean square error (RMSE) values range from six to nine days for anthesis and from 1.1 to 1.7 t ha-1 for crop yield and index of agreement (d-index) from 0.96 to 0.98 for anthesis and from 0.8 to 0.87 for crop yield. The set of genetic parameters obtained for durum wheat, common wheat, and maize may be applied in further analyses at field, regional, and national scales to guide operational (farmers), strategic, and tactical (policy makers) decisions.

scholarly journals Integrated Assessment of Environmental Effects from Agricultural Production

1991 ◽  
Vol 20 (1) ◽  
pp. 61-67 ◽  
Author(s):  
John G. Lee ◽  
Stephen B. Lovejoy
Keyword(s):  
Environmental Quality ◽  
Resource Use ◽  
Agricultural Production ◽  
Simulation Models ◽  
Research Problem ◽  
Complex Interaction ◽  
Economic Consequences ◽  
Economic Research ◽  
Soil Productivity ◽  
Environmental Effectiveness

Agriculture's impact on the environment is a complex research problem. A challenge to future economic research is to account for the interrelationship between agricultural production activities, soil productivity, erosion, and water quality. It will become increasingly important to determine not only the economic consequences, but also the environmental effectiveness of alternative policies aimed at improving resource use and quality. The application of biophysical simulation models to environmental quality problems provides a means to better understand the complex interaction between agricultural production and environmental quality.

Systemic modelling of soil functions under the impact of agricultural management

2020 ◽  
Author(s):  
Sara König ◽  
Ulrich Weller ◽  
Birgit Lang ◽  
Mareike Ließ ◽  
Stefanie Mayer ◽  
...  
Keyword(s):  
Soil Structure ◽  
Water Distribution ◽  
Management Strategies ◽  
Simulation Models ◽  
Crop Growth ◽  
Soil Productivity ◽  
Soil Functions ◽  
Management Options ◽  
Soil Organic Matter Turnover ◽  
The Impact

<p>The increasing demand for food and bio-energy gives need to optimize soil productivity, while securing other soil functions such as nutrient cycling and buffer capacity, carbon storage, biological activity, and water filter and storage. Mechanistic simulation models are an essential tool to fully understand and predict the complex interactions between physical, biological and chemical processes of soil with those functions, as well as the feedbacks between these functions.</p><p>We developed a systemic soil model to simulate the impact of different management options and changing climate on the named soil functions by integrating them within a simplified system. The model operates on a 1d soil profile consisting of dynamic nodes, which may represent the different soil horizons, and integrates different processes including dynamic water distribution, soil organic matter turnover, crop growth, nitrogen cycling, and root growth.</p><p>We present the main features of our model by simulating crop growth under various climatic scenarios on different soil types including management strategies affecting the soil structure. We show the relevance of soil structure for the main soil functions and discuss different model outcome variables as possible measures for these functions.</p><p>Further, we discuss ongoing model extensions, especially regarding the integration of biological processes, and possible applications.</p>

DairyMod and EcoMod: biophysical pasture-simulation models for Australia and New Zealand

2008 ◽  
Vol 48 (5) ◽  
pp. 621 ◽  
Author(s):  
I. R. Johnson ◽  
D. F. Chapman ◽  
V. O. Snow ◽  
R. J. Eckard ◽  
A. J. Parsons ◽  
...  
Keyword(s):  
New Zealand ◽  
Nutrient Dynamics ◽  
Irrigation Management ◽  
Management Strategies ◽  
Simulation Models ◽  
Nutrient Status ◽  
Pasture Management ◽  
Management Options ◽  
Recent Developments ◽  
Fertiliser Application

DairyMod and EcoMod, which are biophysical pasture-simulation models for Australian and New Zealand grazing systems, are described. Each model has a common underlying biophysical structure, with the main differences being in their available management options. The third model in this group is the SGS Pasture Model, which has been previously described, and these models are referred to collectively as ‘the model’. The model includes modules for pasture growth and utilisation by grazing animals, water and nutrient dynamics, animal physiology and production and a range of options for pasture management, irrigation and fertiliser application. Up to 100 independent paddocks can be defined to represent spatial variation within a notional farm. Paddocks can have different soil types, nutrient status, pasture species, fertiliser and irrigation management, but are subject to the same weather. Management options include commonly used rotational grazing management strategies and continuous grazing with fixed or variable stock numbers. A cutting regime simulates calculation of seasonal pasture growth rates. The focus of the present paper is on recent developments to the management routines and nutrient dynamics, including organic matter, inorganic nutrients, leaching and gaseous nitrogen losses, and greenhouse gases. Some model applications are presented and the role of the model in research projects is discussed.

A fisheries risk-assessment framework to evaluate trade-offs among management options in the presence of time-varying productivity

2012 ◽  
Vol 69 (2) ◽  
pp. 209-223 ◽  
Author(s):  
Jeremy S. Collie ◽  
Randall M. Peterman ◽  
Brett M. Zuehlke
Keyword(s):  
Risk Assessment ◽  
Simulation Models ◽  
Quantitative Information ◽  
The Arctic ◽  
Oncorhynchus Keta ◽  
Time Varying ◽  
Assessment Framework ◽  
Management Options ◽  
Commercial Catch ◽  
Trade Offs

Empirically based simulation models can help fisheries managers make difficult decisions involving trade-offs between harvests and maintaining spawner abundance, especially when data contain uncertainties. We developed such a general risk-assessment framework and applied it to chum salmon ( Oncorhynchus keta ) stocks in the Arctic–Yukon–Kuskokwim region of Alaska, USA. These stocks experienced low abundance in the 1990s, which led to declarations of economic disaster and calls for changes in harvest strategies. Our stochastic model provides decision makers with quantitative information about trade-offs among commercial harvest, subsistence harvest, and spawner abundance. The model included outcome uncertainty (the difference between target and realized spawner abundances) in the subsistence and commercial catch modules. We also used closed-loop simulations to investigate the utility of time-varying management policies in which target spawner abundance changed in response to changes in the Ricker productivity parameter (a), as estimated with a Kalman filter. Time-varying policies resulted in higher escapements and catches and reduced risk across a range of harvest rates. The resulting generic risk-assessment framework can be used to evaluate harvest guidelines for most salmon stocks.

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