scholarly journals Plant residues, beneficial microbes and integrated phosphorus management for improving hybrid maize (Zea mays L.) growth and total biomass

2021 ◽  
pp. 16-34
Author(s):  
Amanullah Amanullah ◽  
Asif Iqbal ◽  
Asim Muhammad ◽  
Abdel Rahman Altawaha ◽  
Azizullah Shah ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Faba Bean ◽  
Biomass Yield ◽  
Plant Residues ◽  
Total Biomass ◽  
Phosphate Solubilizing Bacteria ◽  
Area Index ◽  
Hybrid Maize ◽  
The Impact

Phosphorus (P) unavailability and lack of organic matter in calcareous soils in semiarid climates are the major reasons for low crop productivity. This field experiment was conducted at the Agronomy Research Farm of The University of Agriculture Peshawar, during the summer of 2015 to investigate the impact of plant residues (PR) (faba bean, garlic and paper mulberry residues) and phosphorous sources [(60 and 120kg ha-1) from single super phosphate (SSP) and poultry manure (PM)] with (+) and without (-) phosphate solubilizing bacteria (PSB) on the phenological development, growth and biomass yield of hybrid maize “CS-200”. Among the PR, application of faba bean residue was found to delay phenological development (days to tasseling, silking and physiological maturity), improved growth (taller plants, higher leaf area per plant and leaf area index) and produced the highest biomass yield (faba bean>garlic>paper mulberry residues). Application of P at the rate of 120kg ha-1 from the inorganic source (SSP) was more beneficial in terms of better growth and higher biomass yield (120-SSP≥120-PM>60-SSP>60-PM kg P ha-1). The plots with (+) PSB showed enhanced phenological development, produced significantly taller plants with higher leaf area per plant and leaf area index and produced the highest biomass yield. On the basis of these results we concluded that the application of faba bean residues, phosphorus at the rate of 120kg ha-1 either from organic or inorganic sources with the inoculation of seed with PSB improved the growth and total biomass of hybrid maize in the study area.

scholarly journals EVALUATION OF CERES-MAIZE MODEL FOR HYBRID MAIZE (Zea mays L.) PRODUCTION

2019 ◽  
Vol 13 (1) ◽  
pp. 96-110
Author(s):  
Amied Ali ◽  
Bashrat Ali
Keyword(s):  
Grain Yield ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Planting Dates ◽  
Sowing Time ◽  
Maize Crop ◽  
Area Index ◽  
Hybrid Maize ◽  
The Impact ◽  
Plot Design

The trial was conducted at Agronomic Research farm, University of Sargodha during spring growing season, 2015, to calibrate and evaluate CERES-Maize model for simulating the impact of different sowing time on maize crop. The experiment was laid out in split plot design having three replications, keeping planting dates (25th Feb, 6thMar and 14thMar) in main plots and hybrids i.e. (DK-9108, DK-6525 and DK-6142) in the sub plots. The Calibration of CSM-CERES-Maize model showed the best possible closeness between simulated and observed days to flowering and physiological maturity, leaf area index (LAI), Total dry matter (TDM), and grain yield with % error of 4.0, -1.5, 0.41, 0.07, 0.14 and 0.3% , respectively, when maize hybrid H1 (DK- 6142) was sown at firstsowing date (25th Feb). DSSAT,CERES- Maize model predicted the phenological traits like anthesis and maturity phase. Number of days to anthesis and maturity simulated by model were lesser to the observed values, where as, simulated grain yield was higher as compared to observed data for all the three cultivars. Model calculated the close similarity between experimental and computer-generated values for leaf area index.

scholarly journals Remote Sensing to Study Mangrove Fragmentation and Its Impacts on Leaf Area Index and Gross Primary Productivity in the South of Peninsular Malaysia

2021 ◽  
Vol 13 (8) ◽  
pp. 1427
Author(s):  
Kasturi Devi Kanniah ◽  
Chuen Siang Kang ◽  
Sahadev Sharma ◽  
A. Aldrie Amir
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Primary Productivity ◽  
Peninsular Malaysia ◽  
Area Index ◽  
Mangrove Area ◽  
The Mean ◽  
Shape Complexity ◽  
The Impact

Mangrove is classified as an important ecosystem along the shorelines of tropical and subtropical landmasses, which are being degraded at an alarming rate despite numerous international treaties having been agreed. Iskandar Malaysia (IM) is a fast-growing economic region in southern Peninsular Malaysia, where three Ramsar Sites are located. Since the beginning of the 21st century (2000–2019), a total loss of 2907.29 ha of mangrove area has been estimated based on medium-high resolution remote sensing data. This corresponds to an annual loss rate of 1.12%, which is higher than the world mangrove depletion rate. The causes of mangrove loss were identified as land conversion to urban, plantations, and aquaculture activities, where large mangrove areas were shattered into many smaller patches. Fragmentation analysis over the mangrove area shows a reduction in the mean patch size (from 105 ha to 27 ha) and an increase in the number of mangrove patches (130 to 402), edge, and shape complexity, where smaller and isolated mangrove patches were found to be related to the rapid development of IM region. The Moderate Resolution Imaging Spectro-radiometer (MODIS) Leaf Area Index (LAI) and Gross Primary Productivity (GPP) products were used to inspect the impact of fragmentation on the mangrove ecosystem process. The mean LAI and GPP of mangrove areas that had not undergone any land cover changes over the years showed an increase from 3.03 to 3.55 (LAI) and 5.81 g C m−2 to 6.73 g C m−2 (GPP), highlighting the ability of the mangrove forest to assimilate CO2 when it is not disturbed. Similarly, GPP also increased over the gained areas (from 1.88 g C m−2 to 2.78 g C m−2). Meanwhile, areas that lost mangroves, but replaced them with oil palm, had decreased mean LAI from 2.99 to 2.62. In fragmented mangrove patches an increase in GPP was recorded, and this could be due to the smaller patches (<9 ha) and their edge effects where abundance of solar radiation along the edges of the patches may increase productivity. The impact on GPP due to fragmentation is found to rely on the type of land transformation and patch characteristics (size, edge, and shape complexity). The preservation of mangrove forests in a rapidly developing region such as IM is vital to ensure ecosystem, ecology, environment, and biodiversity conservation, in addition to providing economical revenue and supporting human activities.

scholarly journals Selection of sowing date and biofertilization as alternatives to improve the yield and profitability of the F68 rice variety

2020 ◽  
Vol 38 (1) ◽  
pp. 61-72
Author(s):  
Yeison Mauricio Quevedo-Amaya ◽  
José Isidro Beltrán-Medina ◽  
José Álvaro Hoyos-Cartagena ◽  
John Edinson Calderón-Carvajal ◽  
Eduardo Barragán-Quijano
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Carbon Balance ◽  
Sowing Date ◽  
Dry Matter Accumulation ◽  
Phosphate Solubilizing Bacteria ◽  
Area Index ◽  
Use Of Resources ◽  
High Efficient ◽  
Selection Of

Multiple factors influence rice yield. Developing management practices that increase crop yield and an efficient use of resources are challenging to modern agriculture. Consequently, the aim of this study was to evaluate biological nitrogen fixation and bacterial phosphorous solubilization (biofertilization) practices with the selection of the sowing date. Three sowing dates (May, July and August) were evaluated when interacting with two mineral nutrition treatments using a randomized complete block design in a split-plot arrangement. Leaf carbon balance, leaf area index, interception and radiation use efficiency, harvest index, dry matter accumulation, nutritional status, and yield were quantified. Results showed that the maximum yield was obtained in the sowing date of August. Additionally, yield increased by 18.92% with the biofertilization treatment, reaching 35.18% of profitability compared to the local production practice. High yields were related to a higher carbon balance during flowering, which was 11.56% and 54.04% higher in August than in July and May, respectively, due to a lower night temperature. In addition, a high efficient use of radiation, which in August was 17.56% and 41.23% higher than in July and May, respectively, contributed to obtain higher yields and this behavior is related to the selection of the sowing date. Likewise, a rapid development of the leaf area index and an optimum foliar nitrogen concentration (>3%) were observed. This allowed for greater efficient use of radiation and is attributed to the activity of nitrogen-fixing and phosphate solubilizing bacteria that also act as plant growth promoters.

The Impact of Noah-MP Physical Parameterizations on Modeling Water Availability during Droughts in the Texas-Gulf Region

2021 ◽  
Author(s):  
Wen-Ying Wu ◽  
Zong-Liang Yang ◽  
Michael Barlage
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Water Availability ◽  
Water Storage ◽  
Rooting Depth ◽  
Surface Model ◽  
Terrestrial Water Storage ◽  
Area Index ◽  
Terrestrial Water ◽  
The Impact

AbstractTexas is subject to severe droughts, including the record-breaking one in 2011. To investigate the critical hydrometeorological processes during drought, we use a land surface model, Noah-MP, to simulate water availability and investigate the causes of the record drought. We conduct a series of experiments with runoff schemes, vegetation phenology, and plant rooting depth. Observation-based terrestrial water storage, evapotranspiration, runoff, and leaf area index are used to compare with results from the model. Overall, the results suggest that using different parameterizations can influence the modeled water availability, especially during drought. The drought-induced vegetation responses not only interact with water availability but also affect the ground temperature. Our evaluation shows that Noah-MP with a groundwater scheme produces a better temporal relationship in terrestrial water storage compared with observations. Leaf area index from dynamic vegetation is better simulated in wet years than dry years. Reduction of positive biases in runoff and reduction of negative biases in evapotranspiration are found in simulations with groundwater, dynamic vegetation, and deeper rooting zone depth. Multi-parameterization experiments show the uncertainties of drought monitoring and provide a mechanistic understanding of disparities in dry anomalies.

scholarly journals Development and Evaluation of a Leaf Disease Damage Extension in Cropsim-CERES Wheat

Agronomy ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 120 ◽  
Author(s):  
Georg Röll ◽  
William Batchelor ◽  
Ana Castro ◽  
María Simón ◽  
Simone Graeff-Hönninger
Keyword(s):  
Sensitivity Analysis ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Mean Squared Error ◽  
Septoria Tritici Blotch ◽  
Septoria Tritici ◽  
Extended Model ◽  
Simple Method ◽  
Area Index ◽  
The Impact

Developing disease models to simulate and analyse yield losses for various pathogens is a challenge for the crop modelling community. In this study, we developed and tested a simple method to simulate septoria tritici blotch (STB) in the Cropsim-CERES Wheat model studying the impacts of damage on wheat (Triticum aestivum L.) yield. A model extension was developed by adding a pest damage module to the existing wheat model. The module simulates the impact of daily damage on photosynthesis and leaf area index. The approach was tested on a two-year dataset from Argentina with different wheat cultivars. The accuracy of the simulated yield and leaf area index (LAI) was improved to a great extent. The Root mean squared error (RMSE) values for yield (1144 kg ha−1) and LAI (1.19 m2 m−2) were reduced by half (499 kg ha−1) for yield and LAI (0.69 m2 m−2). In addition, a sensitivity analysis of different disease progress curves on leaf area index and yield was performed using a dataset from Germany. The sensitivity analysis demonstrated the ability of the model to reduce yield accurately in an exponential relationship with increasing infection levels (0–70%). The extended model is suitable for site specific simulations, coupled with for example, available remote sensing data on STB infection.

scholarly journals The effect of different crops and slopes on runoff and soil erosion

2020 ◽  
Vol 15 (3) ◽  
pp. 773-780
Author(s):  
Jinhua Gao ◽  
Yu Bai ◽  
Haifeng Cui ◽  
Yu Zhang
Keyword(s):  
Soil Erosion ◽  
Field Experiment ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Environmental Issues ◽  
Area Index ◽  
The Impact

Abstract Runoff and soil erosion are serious environmental issues in farmland management. In a field experiment in Xingmu, China, data from nine plots with different slopes and crops were collected, and the crops' leaf area index (LAI) used to represent the impact of vegetation on runoff and soil erosion. The results show that slope and crop both have significant effects on runoff and soil erosion, and that the LAI can indicate the effects of different crops.

scholarly journals Contribution of Dynamic Vegetation Phenology to Decadal Climate Predictability

2014 ◽  
Vol 27 (22) ◽  
pp. 8563-8577 ◽  
Author(s):  
Martina Weiss ◽  
Paul A. Miller ◽  
Bart J. J. M. van den Hurk ◽  
Twan van Noije ◽  
Simona Ştefănescu ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Degrees Of Freedom ◽  
Reference Model ◽  
Coupled Model ◽  
Coupled System ◽  
Cold Bias ◽  
Area Index ◽  
Skill Scores ◽  
The Impact

Abstract In this study, the impact of coupling and initializing the leaf area index from the dynamic vegetation model Lund–Potsdam–Jena General Ecosystem Simulator (LPJ-GUESS) is analyzed on skill of decadal predictions in the fully coupled atmosphere–land–ocean–sea ice model, the European Consortium Earth System Model (EC-Earth). Similar to the impact of initializing the model with the observed oceanic state, initializing the leaf area index (LAI) fields obtained from an offline LPJ-GUESS simulation forced by the observed atmospheric state leads to a systematic drift. A different treatment of the water and soil moisture budget in LPJ-GUESS is a likely cause of this drift. The coupled system reduces the cold bias of the reference model over land by reducing LAI (and the associated evaporative cooling), particularly outside the growing season. The coupling with the interactive vegetation module implies more degrees of freedom in the coupled model, which generates more noise that can mask a portion of the extra signal that is generated. The forecast reliability improves marginally, particularly early in the forecast. Ranked probability skill scores are also improved slightly in most areas analyzed, but the signal is not fully coherent over the forecast interval because of the relatively low number of ensemble members. Methods to remove the LAI drift and allow coupling of other variables probably need to be implemented before significant forecast skill can be expected.

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