scholarly journals Seasonal Net Carbon Exchange in Rotation Crops in the Temperate Climate of Central Lithuania

2019 ◽  
Vol 11 (7) ◽  
pp. 1966 ◽  
Author(s):  
Ligita Baležentienė ◽  
Ovidijus Mikša ◽  
Tomas Baležentis ◽  
Dalia Streimikiene
Keyword(s):  
Climate Change ◽  
Exchange Rate ◽  
Soil Respiration ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Co2 Emissions ◽  
Vegetation Period ◽  
Temperate Climate ◽  
Carbon Exchange ◽  
Area Index

Intelligent agricultural solutions require data on the environmental impacts of agriculture. In order for operationalize decision-making for sustainable agriculture, one needs to establish the corresponding datasets and protocols. Increasing anthropogenic CO2 emissions into the atmosphere force the choice of growing crops aimed at mitigating climate change. For this reason, investigations of seasonal carbon exchange were carried out in 2013–2016 at the Training Farm of the Vytautas Magnus University (former Aleksandras Stulginskis University), Lithuania. This paper compares the carbon exchange rate for different crops, viz., maize, ley, winter wheat, spring rapeseed and barley under conventional farming. This study focuses on the carbon exchange rate. We measure the emitted and absorbed CO2 fluxes by applying the closed chamber method. The biomass measurement and leaf area index (LAI) calculations at different plant growth stages are used to evaluate carbon exchange in different agroecosystems. The differences in photosynthetically assimilated CO2 rates were significantly impacted by the leaf area index (p = 0.04) during the plant vegetation period. The significantly (p = 0.02–0.05) strong correlation (r = 0.6–0.7) exists between soil respiration and LAI. Soil respiration composed only 21% of the agroecosystem carbon exchange. Plant respiration ranged between 0.034 and 3.613 µmol m−2 s−1 during the vegetation period composed of a negligible ratio (mean 16%) of carbon exchange. Generally, respiration emissions were obviously recovered by the gross primary production (GPP) of crops. Therefore, the ecosystems were acting as an atmospheric CO2 sink. Barley accumulated the lowest mean GPP 12.77 µmol m−2 s−1. The highest mean GPP was determined for ley (14.28 µmol m−2 s−1) and maize (15.68 µmol m−2 s−1) due to the biggest LAI and particular bio-characteristics. Due to the highest NEP, the ley (12.66 µmol m−2 s−1) and maize (12.76 µmol m−2 s−1) agroecosystems sank the highest C from the atmosphere and, thus, they might be considered the most sustainable items between crops. Consequently, the appropriate choice of crops and their area in crop rotations may reduce CO2 emissions and their impact on the environment and climate change.

Integrating remote sensing and climate data with process-based models to map forest productivity within west-central Alberta's boreal forest: Ecoleap-West

2006 ◽  
Vol 82 (2) ◽  
pp. 159-176 ◽  
Author(s):  
R J Hall ◽  
F. Raulier ◽  
D T Price ◽  
E. Arsenault ◽  
P Y Bernier ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Management Practices ◽  
Forest Productivity ◽  
Process Models ◽  
Growth And Yield ◽  
Area Index ◽  
Study Region

Forest yield forecasting typically employs statistically derived growth and yield (G&Y) functions that will yield biased growth estimates if changes in climate seriously influence future site conditions. Significant climate warming anticipated for the Prairie Provinces may result in increased moisture deficits, reductions in average site productivity and changes to natural species composition. Process-based stand growth models that respond realistically to simulated changes in climate can be used to assess the potential impacts of climate change on forest productivity, and hence can provide information for adapting forest management practices. We present an application of such a model, StandLEAP, to estimate stand-level net primary productivity (NPP) within a 2700 km2 study region in western Alberta. StandLEAP requires satellite remote-sensing derived estimates of canopy light absorption or leaf area index, in addition to spatial data on climate, topography and soil physical characteristics. The model was applied to some 80 000 stand-level inventory polygons across the study region. The resulting estimates of NPP correlate well with timber productivity values based on stand-level site index (height in metres at 50 years). This agreement demonstrates the potential to make site-based G&Y estimates using process models and to further investigate possible effects of climate change on future timber supply. Key words: forest productivity, NPP, climate change, process-based model, StandLEAP, leaf area index, above-ground biomass

scholarly journals Assessment of Climate Change Impact on Crop yield and Evaluation of Coping Strategy using Crop Growth Simulation Model

2020 ◽  
Vol 15 (1) ◽  
pp. 106-122
Author(s):  
J. Alam ◽  
R. K. Panda
Keyword(s):  
Climate Change ◽  
Grain Yield ◽  
Simulation Model ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Co2 Concentration ◽  
Maize Yield ◽  
Crop Growth ◽  
Climate Change Scenarios ◽  
Area Index

 Any change in climate will have implications for climate-sensitive systems such as agriculture, forestry and some other natural resources. Changes in solar radiation, temperature and precipitation will produce changes in crop yields and hence economics of agriculture. It is possible to understand the phenomenon of climate change on crop production and to develop adaptation strategies for sustainability in food production, using a suitable crop simulation model. CERES-Maize model of DSSAT v4.0 was used to simulate the maize yield of the region under climate change scenarios using the historical weather data at Kharagpur (1977-2007), Damdam (1974-2003) and Purulia (1986-2000), West Bengal, India. The model was calibrated using the crop experimental data, climate data and soil data for two years (1996-1997) and was validated by using the data of the year 1998 at Kharagpur. The change in values of weather parameters due to climate change and its effects on the maize crop growth and yield was studied. It was observed that increase in mean temperature and leaf area index have negative impacts on maize yield. When the maximum leaf area index increased, the grain yield was found to be decreased. Increase in CO2 concentration with each degree incremental temperature decreased the grain yield but increase in CO2 concentration with fixed temperature increased the maize yield. Adjustments were made in the date of sowing to investigate suitable option for adaptation under the future climate change scenarios. Highest yield was obtained when the sowing date was advanced by a week at Kharagpur and Damdam whereas for Purulia, the experimental date of sowing was found to be beneficial.

Leaf area durations and grain yields of two tropical rice varieties

1968 ◽  
Vol 8 (34) ◽  
pp. 587
Author(s):  
PC Owen
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Photosynthetic Efficiency ◽  
Temperate Climate ◽  
Growth Stages ◽  
Rice Varieties ◽  
Area Index ◽  
Grain Yields ◽  
Different Growth Stages

A series of differing leaf area index regimes during the growth of two tropical rice varieties was produced by partial defoliation at different growth stages. In addition, part of the crop was completely defoliated after panicle emergence. Comparison of the effects of the range of leaf area durations (D) thus produced showed that these rice varieties differed from temperate climate cereals. Grain yields were least associated with D after panicle emergence, but were most influenced by D before emergence. This effect is mainly via an influence upon the number of spikelets formed per panicle. Grain : leaf ratio, a measure of photosynthetic efficiency, was considerably lower than values reported for wheat.

Spatiotemporal evolution of drought events and its contribution on vegetation growth in the river source region

2020 ◽  
Author(s):  
Shuang Zhu
Keyword(s):  
Climate Change ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Spatiotemporal Evolution ◽  
Area Index ◽  
Vegetation Growth ◽  
Evolution Characteristics ◽  
The Standardized Precipitation Index

<p><span lang="EN-US"><span>Climate change has been proved to exacerbate drought events and further cause huge economic and ecological losses worldwide. Therefore, it is of great significance to study the long-term evolution characteristics of drought events and quantify the impact of drought events on typical ecological indexes. Based on the measured historical precipitation data, the standardized precipitation index of different time scales was extracted to measure water deficit. The leaf area index with wide range and high precision was generated based on the Modis remote sensing image and denoising processing to represent vegetation growth. Trend analysis and change point analysis were carried out to study the spatiotemporal evolution characteristics of the concerned drought indexes. Then, with hypothesis test, appropriate copula multivariate analysis method was innovatively introduced to construct joint distribution of the standardized precipitation index and leaf area index. The contribution of drought on vegetation growth was expected to be quantified by deriving the conditional copula and preset marginal distributions. The upper Yangtze River where biomass is extremely sensitive to climate change was taken as a study area. The results show that drought events in this region have significant spatial heterogeneity. The leaf area index is highly influenced by the meteorological drought index. From no drought to severe drought, the vegetation index is distributed more and more toward the low value. Copula is very potential to find the inner relationship of the standardized precipitation index and leaf area index. The study is useful to deepen the understanding of the internal mechanism of drought events and discuss reasonable disaster prevention and mitigation countermeasures.</span></span></p> <p> </p>

scholarly journals Investigating the leaf area index changes in response to climate change (case study: Kasilian catchment, Iran)

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Mohammad Reza Ramezani ◽  
Ali Reza Massah Bavani ◽  
Mostafa Jafari ◽  
Ali Binesh ◽  
Stefan Peters
Keyword(s):  
Climate Change ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Index Changes

scholarly journals MERIS GPP/NPP product for Estonia: II. Complex meteorological limiting factor and optimum leaf area index / MERIS’e GPP/NPP tulem Eesti jaoks: II. Kompleksne meteoroloogiline piirangutegur ja optimaalne lehepinnaindeks

2014 ◽  
Vol 61 (1) ◽  
pp. 5-26
Author(s):  
Tiit Nilson ◽  
Mattias Rennel ◽  
Mait Lang
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Meteorological Data ◽  
Vegetation Period ◽  
Meteorological Conditions ◽  
Limiting Factors ◽  
Carbon Gain ◽  
Limiting Factor ◽  
Area Index ◽  
Seasonal Course

Abstract. The merits and possible problems of the light use efficiency-concept based GPP/NPP models applied together with satellite images and meteorological data to quantitatively understand the role of different meteorological factors in forest productivity are analysed. A concept of the complex meteorological limiting factor for plant productivity is introduced. The factor includes the effects of incoming photosynthetically active radiation as well as the temperature and water limiting factors. Climatologically averaged seasonal courses of the complex meteorological limiting factor derived from the records of two contrasting meteorological stations in Estonia - inland Tartu/Tõravere and coastal Sõrve - are shown. Leaf phenology, here described via the seasonal course of leaf area index (LAI), is interpreted as a possible means to maximise the carbon gain under particular meteorological conditions. The equations for the optimum seasonal course of LAI as derived from the NPP model are presented. As the daily adjustment of plant LAI to sudden changes in meteorological conditions is not possible, several approximate strategies for LAI seasonal course to maximise the yearly NPP of vegetation are analysed. Typical optimal courses of LAI show some seasonal asymmetry resulting in lower values of LAI in the second half of the vegetation period due to higher air temperatures and respiration costs. Knowledge about optimum LAI courses has a cognitive value, but can also be used as the simulated LAI courses in several models when the measured LAI values are not available. As the considered GPP/NPP models fail to adequately describe the local trends in forest and agricultural productivity in Estonia, the ways to improve the model’s performance are shown.

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