scholarly journals Seasonal forecasting of green water components and crop yield of summer crops in Serbia and Austria

2018 ◽  
Vol 156 (5) ◽  
pp. 658-672 ◽  
Author(s):  
B. Lalić ◽  
A. Firanj Sremac ◽  
J. Eitzinger ◽  
R. Stričević ◽  
S. Thaler ◽  
...  
Keyword(s):  
Crop Yield ◽  
Spring Barley ◽  
Water Productivity ◽  
Weather Forecast ◽  
Seasonal Forecasting ◽  
Green Water ◽  
Weather Data ◽  
Medium Range Weather Forecast ◽  
Probabilistic Forecasts ◽  
Aquacrop Model

AbstractA probabilistic crop forecast based on ensembles of crop model output estimates, presented here, offers an ensemble of possible realizations and probabilistic forecasts of green water components, crop yield and green water footprints (WFs) on seasonal scales for selected summer crops. The present paper presents results of an ongoing study related to the application of ensemble forecasting concepts in crop production. Seasonal forecasting of crop water use indicators (evapotranspiration (ET), water productivity, green WF) and yield of rainfed summer crops (maize, spring barley and sunflower), was performed using the AquaCrop model and ensemble weather forecast, provided by The European Centre for Medium-range Weather Forecast. The ensemble of estimates obtained was tested with observation-based simulations to assess the ability of seasonal weather forecasts to ensure that accuracy of the simulation results was the same as for those obtained using observed weather data. Best results are obtained for ensemble forecast for yield, ET, water productivity and green WF for sunflower in Novi Sad (Serbia) and maize in Groß-Enzersdorf (Austria) – average root mean square error (2006–2014) was <10% of observation-based values of selected variables. For variables yielding a probability distribution, capacity to reflect the distribution from which their outcomes will be drawn was tested using an Ignorance score. Average Ignorance score, for all locations, crops and variables varied from 1.49 (spring barley ET in Groß-Enzersdorf) to 3.35 (sunflower water productivity in Groß-Enzersdorf).

Probabilistic fire-danger forecasting: A framework for week-two forecasts using statistical post-processing techniques and the Global ECMWF Fire Forecast System (GEFF)

2021 ◽  
Author(s):  
Rochelle P. Worsnop ◽  
Michael Scheuerer ◽  
Francesca Di Giuseppe ◽  
Christopher Barnard ◽  
Thomas M. Hamill ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Reanalysis Data ◽  
Post Processing ◽  
Medium Range Weather Forecast ◽  
Skill Scores ◽  
Probabilistic Forecasts ◽  
Systematic Biases ◽  
Processing Techniques

AbstractWildfire guidance two weeks ahead is needed for strategic planning of fire mitigation and suppression. However, fire forecasts driven by meteorological forecasts from numerical weather prediction models inherently suffer from systematic biases. This study uses several statistical-postprocessing methods to correct these biases and increase the skill of ensemble fire forecasts over the contiguous United States 8–14 days ahead. We train and validate the post-processing models on 20 years of European Centre for Medium-range Weather Forecast (ECMWF) reforecasts and ERA5 reanalysis data for 11 meteorological variables related to fire, such as surface temperature, wind speed, relative humidity, cloud cover, and precipitation. The calibrated variables are then input to the Global ECMWF Fire Forecast (GEFF) system to produce probabilistic forecasts of daily fire-indicators which characterize the relationships between fuels, weather, and topography. Skill scores show that the post-processed forecasts overall have greater positive skill at Days 8–14 relative to raw and climatological forecasts. It is shown that the post-processed forecasts are more reliable at predicting above- and below-normal probabilities of various fire indicators than the raw forecasts and that the greatest skill for Days 8–14 is achieved by aggregating forecast days together.

scholarly journals Impact Assessment of Block Level Agro Advisories for Saving Input Cost of Farmers under Old Gangetic Plains of West Bengal - A Case Study in Malda

2020 ◽  
pp. 86-96
Author(s):  
Debjyoti Majumder ◽  
Rakesh Roy ◽  
F. H. Rahman ◽  
B. C. Rudra
Keyword(s):  
West Bengal ◽  
Monsoon Season ◽  
India Meteorological Department ◽  
Weather Forecast ◽  
Economic Benefits ◽  
Weather Data ◽  
New Delhi ◽  
Medium Range Weather Forecast ◽  
Net Returns ◽  
Block Level

Biweekly block level Agromet bulletins were disseminated based on medium range weather forecast with an objective to assess the effectiveness and usefulness of Block level Agro Advisory Services (AAS) and quantify the economic benefits through adopting the micro scale agromet advisory in their day to day agricultural operations at Malda, West Bengal. Two farmers groups were considered for the study on the basis of adoption and non-adoption of the agro-met advisories. Crop situation of these farmers were compared with nearby fields having the same crops where forecast were not adopted among non AAS farmers. The entire cost incurred along with yield and net returns were calculated from sowing to marketing of goods. Similarly, the weather forecast and actual weather data received from India Meteorological Department, New Delhi were compared to verify the accuracy of rainfall forecast for the year 2019-20 at GKMS centre, Malda KVK, West Bengal. It was apparent that the value of ratio score was higher during winter (84%) than pre-monsoon (80%), post-monsoon (79%) and monsoon (74%). However, the value of threat score was also found maximum during pre-monsoon season (79%). Statistical analysis like correlation coefficient, RMSE values of wind direction were found too high in all the four seasons to accept any homogeneity in the predicted and observed values. Blockwise verification of rainfall over the year showed the range of accuracy forecast for rainfall in between 67–76%. This forecast directly had a significant role in profit generation among the AAS adaptive farmers whose additional profit enhancement for maize cultivation was between 12% and 19% only towards cost of irrigation as compared to non-adaptive farmers. The study also showcased that the AAS adaptive farmers had a better livelihood as compared to non-AAS adaptive farmers.

scholarly journals Improving efficiency of block-level agrometeorological advisory system by exploiting reuse: A study in Telangana

2021 ◽  
Vol 23 (3) ◽  
pp. 330-339
Author(s):  
MAMATHA ALUGUBELLY ◽  
KRISHNA REDDY POLEPALLI ◽  
BALAJINAIK BANOTH ◽  
SREENIVAS GADE ◽  
ANIRBAN MONDAL ◽  
...  
Keyword(s):  
Weather Condition ◽  
India Meteorological Department ◽  
Weather Forecast ◽  
Weather Data ◽  
Spatial Reuse ◽  
Medium Range Weather Forecast ◽  
Weather Situation ◽  
Block Level ◽  
Block Based ◽  
Level Medium

India Meteorological Department (IMD) has started block-level level agromet advisory (AA) service from the year 2015 and is currently operating in a few blocks of each state across India. In a block-level AA service, on every Tuesday and Friday, AA is being prepared for each block based on the block-level Medium Range weather Forecast (MRF). In this paper, we propose a framework to improve the preparation of blocklevel AA by modeling a weather situation as “Category-based Weather Condition (CWC)” and exploiting both “temporal reuse” and “spatial reuse” of AA based on the similarity among CWCs. The weather data analysis for 12 blocks of Telangana by considering the phenophase-specific CWCs of Rice crop showed that there is a scope to improve the efficiency of block-level AA bulletin preparation process by exploiting reuse.

scholarly journals Modeling the effect of planting date on Maize (Zea mays L.) crop in Sabour region of Bihar using DSSATv3.5 crop simulation model

MAUSAM ◽  
2021 ◽  
Vol 61 (1) ◽  
pp. 75-80
Author(s):  
P. K. SINGH ◽  
L. S. RATHORE ◽  
K. K. SINGH ◽  
A. K. BAXLA ◽  
R. K. MALL
Keyword(s):  
Grain Yield ◽  
Weather Forecast ◽  
Sowing Date ◽  
Weather Data ◽  
Maximum Temperature ◽  
Local Conditions ◽  
Medium Range Weather Forecast ◽  
Physiological Maturity ◽  
Grain Yields ◽  
Medium Range

CERES-Maize model calibrated for local conditions of Sabour has been used to evaluate the relevance medium range weather forecast relative to the maize crop growth period. The procedure is to place the reference year's daily weather into the model up to the time the yield prediction is to be made and sequences of historical data (one sequence per year) after that time until the end of growing season to give yield estimates. A procedure that makes use of historical weather data, medium range weather forecast (mrwf) and current weather data in conjunction with the CERES-Maize model was developed to arrive at a probable distribution of predicted yields. The lower temperature and more solar radiation in tassel emergence to dough stage silk emergence to physiological maturity phase and lower maximum temperature are found favorable to contribute more in increasing the grain yields. The CERES- Maize model correlated for the genetic coefficient predicts the silking dates and physiological maturity very well. Kharif maize gave the highest grain yield of 3490 kg/ha in 1999 and the lowest of 2474 kg/ha in 1979. Among eight different sowing dates the lowest average grain yield was 3190 kg/ha for the last sowing date and the highest average grain yield was 3313 kg/ha in 2nd sowing date. The 25 percentiles were less than the mean grain yields and also 75 percentiles.  

Validation of Aquacrop for Different Irrigation Regimes of Onion (Allium Cepa) in Bontanga Irrigation Scheme

2018 ◽  
Vol 1 (1) ◽  
pp. 1-12
Author(s):  
Y. K. Agbemabiese ◽  
A-G Shaibu ◽  
V. D. Gbedzi
Keyword(s):  
Allium Cepa ◽  
Crop Yield ◽  
Water Productivity ◽  
Leaf Biomass ◽  
Crop Water ◽  
Irrigation Scheme ◽  
Crop Water Productivity ◽  
Irrigation Regimes ◽  
Aquacrop Model ◽  
Mean Square Errors

Crop water productivity models are important tools in evaluating the effect of different irrigation regime on crop yield. AquaCrop model is a crop water productivity model adopted by the Land and Water Division of FAO in the year 2009. It simulates yield response to water for herbaceous crops, and it is particularly suitable in addressing conditions where water is a key limiting factor in crop production such as in northern Ghana. The objective of this study was to calibrate the AquaCrop model for different irrigation regimes for onion (Allium cepa), to determine its effect on crop growth and yield parameters of the crop at the Bontanga irrigation scheme. To achieve these, the Randomised Complete Block Design (RCBD) was used on Red Creole onion variety. RCBD was made up of four irrigation treatment regimes, 117%, 100%, 80% and 60% crop water requirements (CWR) of onion, with five replicates. Results indicated that there was no significant variation in yield, dry bulb biomass and total biomass, but there was difference for dry leaf biomass of onion at 0.05 significance level. The AquaCrop model simulated satisfactorily the crop yield, biomass and evapotranspiration water productivity of onion. There was a strong correlation and a significant linear relation between the simulated and measured crop yield, biomass and evapotranspiration water productivity. Validation of AquaCrop model using Nash-Sutcliffe efficiency (E), Root mean square errors (RMSE) and index of agreement (d) showed that, AquaCrop model can be used to simulate CWR of bulb crops, such as onion.

scholarly journals Seasonal forecasting of green water components and crop yields of winter wheat in Serbia and Austria

2017 ◽  
Vol 156 (5) ◽  
pp. 645-657 ◽  
Author(s):  
B. Lalić ◽  
A. Firanj Sremac ◽  
L. Dekić ◽  
J. Eitzinger ◽  
D. Perišić
Keyword(s):  
Winter Wheat ◽  
Crop Yields ◽  
Plant Protection ◽  
Crop Model ◽  
Ensemble Forecasting ◽  
Green Water ◽  
Seasonal Forecasts ◽  
Medium Range Weather Forecast ◽  
Probabilistic Forecasts ◽  
Seasonal Weather

AbstractA probabilistic crop forecast based on ensembles of crop model output (CMO) estimates offers a myriad of possible realizations and probabilistic forecasts of green water components (precipitation and evapotranspiration), crop yields and green water footprints (GWFs) on monthly or seasonal scales. The present paper presents part of the results of an ongoing study related to the application of ensemble forecasting concepts for agricultural production. The methodology used to produce the ensemble CMO using the ensemble seasonal weather forecasts as the crop model input meteorological data without the perturbation of initial soil or crop conditions is presented and tested for accuracy, as are its results. The selected case study is for winter wheat growth in Austria and Serbia during the 2006–2014 period modelled with the SIRIUS crop model. The historical seasonal forecasts for a 6-month period (1 March-31 August) were collected for the period 2006–2014 and were assimilated from the European Centre for Medium-range Weather Forecast and the Meteorological Archival and Retrieval System. The seasonal ensemble forecasting results obtained for winter wheat phenology dynamics, yield and GWF showed a narrow range of estimates. These results indicate that the use of seasonal weather forecasting in agriculture and its applications for probabilistic crop forecasting can optimize field operations (e.g., soil cultivation, plant protection, fertilizing, irrigation) and takes advantage of the predictions of crop development and yield a few weeks or months in advance.

scholarly journals Evolution of Crop Water Productivity in the Nile Delta over Three Decades (1985–2015)

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1168 ◽  
Author(s):  
Samia M. El-Marsafawy ◽  
Atef Swelam ◽  
Ashraf Ghanem
Keyword(s):  
Crop Yield ◽  
Water Consumption ◽  
Nile Delta ◽  
Water Productivity ◽  
Climatic Conditions ◽  
Weather Data ◽  
Crop Water ◽  
Field Crops ◽  
Crop Water Productivity ◽  
Winter Vegetables

Estimating crop water productivity (CWP) for spatially variable climatic conditions in Egypt is important for the redistribution of crop planting to optimize production per unit of water consumed. The current paper aims to estimate maximum CWP trends under conditions of the Northern Nile Delta over three decades to choose crops that exhibit a higher productivity per unit of water and positive trends in the CWP. The Kafr El Sheikh Governorate was selected to represent the Northern Nile Delta Region, and mean monthly weather data for the period of 1985 to 2015 were collected to calculate standardized reference evapotranspiration and crop water use for a wide array of crops grown in the region using the CROPWAT8.0 model. The CWP was then calculated by dividing crop yield by seasonal water consumption. The CWP data range from 0.69 to 13.79 kg·m−3 for winter field crops, 3.40 to 10.69 kg·m−3 for winter vegetables, 0.29 to 6.04 kg·m−3 for summer field crops, 2.38 to 7.65 kg·m−3 for summer vegetables, 1.00 to 5.38 kg·m−3 for nili season crops (short-season post summer), and 0.66 to 3.35 kg·m−3 for orchards. The crops with the highest CWP values (kg·m−3) over three decades in descending order are: sugar beet (13.79), potato (w2) (10.69), tomato (w) (10.58), eggplant (w) (10.05), potato (w1) (9.98), cucumber (w) (9.81), and cabbage (w) (9.59). There was an increase in CWP of 41% from the first to the second and 22% from the second to the third decade. The CWP increase is attributed to a small decrease in water consumption and to a considerable increase in crop yield. The yield increases are attributed mainly to the planting of higher yielding varieties and/or the application of better agronomic practices.

scholarly journals Estimating Changes in the Green Water Productivity of Cropping Systems in Northern Shaanxi Province in China’s Loess Plateau

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1198 ◽  
Author(s):  
Jinping Wang ◽  
Jinzhu Ma ◽  
Afton Clarke-Sather ◽  
Jiansheng Qu
Keyword(s):  
Loess Plateau ◽  
Hydrological Modeling ◽  
Meteorological Data ◽  
Water Productivity ◽  
Experimental Studies ◽  
Northern Region ◽  
Green Water ◽  
Shaanxi Province ◽  
Cropping Patterns ◽  
Northern Shaanxi

Water shortages limit agricultural production in the world’s arid and semi-arid regions. The Northern region of China’s Shaanxi Province, in the Loess Plateau, is a good example. Raising the water productivity of rainfed grain production in this region is essential to increase food production and reduce poverty, thereby improving food security. To support efforts to increase crop water productivity (CWP), we accounted for limitations of most existing studies (experimental studies of specific crops or hydrological modeling approaches) by using actual field data derived from statistical reports of cropping patterns. We estimated the CWPs of nine primary crops grown in four counties in Northern Shaanxi from 1994 to 2008 by combining statistics on the cultivated area and yields with detailed estimates of evapotranspiration based on daily meteorological data. We further calculated both the caloric CWP of water (CCWP) and the CWP of productive water (i.e., water used for transpiration). We found that regional CWP averaged 6.333 kg mm–1 ha–1, the CCWP was 17,683.81 cal mm–1 ha–1, the CWP of productive green water was 8.837 kg mm–1 ha–1, and the CCWP of productive green water was 24,769.07 cal mm–1 ha–1. Corn, sorghum, and buckwheat had the highest CWP, and although potatoes had the largest planted area and relatively high CWP, they had a low CCWP.

scholarly journals Effect of Row Spacing and Seeding Rate on Russian Thistle (Salsola tragus) in Spring Barley and Spring Wheat

Plants ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 126
Author(s):  
Judit Barroso ◽  
Nicholas G. Genna
Keyword(s):  
Seed Production ◽  
Crop Yield ◽  
Spring Wheat ◽  
Plant Biomass ◽  
Integrated Management ◽  
Spring Barley ◽  
Management Strategies ◽  
Crop Productivity ◽  
Row Spacing ◽  
Seeding Rate

Russian thistle (Salsola tragus L.) is a persistent post-harvest issue in the Pacific Northwest (PNW). Farmers need more integrated management strategies to control it. Russian thistle emergence, mortality, plant biomass, seed production, and crop yield were evaluated in spring wheat and spring barley planted in 18- or 36-cm row spacing and seeded at 73 or 140 kg ha−1 in Pendleton and Moro, Oregon, during 2018 and 2019. Russian thistle emergence was lower and mortality was higher in spring barley than in spring wheat. However, little to no effect of row spacing or seeding rate was observed on Russian thistle emergence or mortality. Russian thistle seed production and plant biomass followed crop productivity; higher crop yield produced higher Russian thistle biomass and seed production and lower crop yield produced lower weed biomass and seed production. Crop yield with Russian thistle pressure was improved in 2018 with 18-cm rows or by seeding at 140 kg ha−1 while no effect was observed in 2019. Increasing seeding rates or planting spring crops in narrow rows may be effective at increasing yield in low rainfall years of the PNW, such as in 2018. No effect may be observed in years with higher rainfall than normal, such as in 2019.

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