Estimation of the average district winter wheat yield based on satellite and ground meteorological data

2020 ◽  
Vol 3 ◽  
pp. 103-121
Author(s):  
A.D. Kleschenko ◽  
◽  
O.V. Savitskaya ◽  
S.A. Kosyakin ◽  
◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Wheat Yield ◽  
Condition Index ◽  
Area Index ◽  
The North ◽  
Independent Information ◽  
Winter Wheat Yield

The research results of the dependence of the average district winter wheat yield on satellite and ground meteorological information for the subjects of the North Caucasian and Volga UGMS are presented. The following satellite indices were used in the work: NDVI (Normalized Difference Vegetation Index), VCI (Vegetation Condition Index) and LAI (Leaf Area Index). The method of interpolation of inverse weighted squares of distances for obtain a set of meteorological parameters for districts there were no weather stations was used. Districts for taking into account agroclimatic conditions were combined into groups using Shashko's Agroclimatic Regionalization method. The selection of parameters that have the greatest impact on the yield was carried out using the correlation-regression analysis method. The corresponding regression models were obtained for the researched regions of the Russian Federation. Verification of the obtained models on dependent and independent information showed a fairly good result. Keywords: NDVI, LAI, interpolation, Shashko's Agroclimatic Regionalization, average district yield, meteorological information Tab. 5. Fig. 7. Ref. 20.

Comparing the Hedging Effectiveness of Weather Derivatives Based on Remotely Sensed Vegetation Health Indices and Meteorological Indices

2018 ◽  
Vol 11 (1) ◽  
pp. 33-48 ◽  
Author(s):  
Johannes Möllmann ◽  
Matthias Buchholz ◽  
Oliver Musshoff
Keyword(s):  
Winter Wheat ◽  
Wheat Yield ◽  
Condition Index ◽  
Remotely Sensed ◽  
Weather Derivatives ◽  
Basis Risk ◽  
Hedging Effectiveness ◽  
Winter Wheat Yield ◽  
Vegetation Health ◽  
Northeastern Germany

Abstract Weather derivatives are considered a promising agricultural risk management tool. Station-based meteorological indices typically provide the data underlying these instruments. However, the main shortcoming of these weather derivatives is an imperfect correlation between the weather index and the yield of the insured crop, called basis risk. This paper considers three available remotely sensed vegetation health (VH) indices, namely, the vegetation condition index (VCI), the temperature condition index (TCI), and the vegetation health index (VHI), as indices for weather derivatives in a German case study. We investigated the correlation and period of highest correlation with winter wheat yield. Moreover, we analyzed whether the use of remotely sensed VH indices for weather derivatives can reduce basis risk and thus improve the performance of weather derivatives. The two commonly used meteorological indices, precipitation and temperature sums, were employed as benchmarks. Quantile regression and index value simulation were used for the design and pricing of the weather derivatives. The analysis for the selected farms and corresponding counties in northeastern Germany revealed that, on average, the VHI resulted in the highest correlation with winter wheat yield, and VHI-based weather derivatives were also superior in terms of the hedging effectiveness. The total periods of the highest correlations ranged from the beginning of April to the end of July. VHI- and VCI-based weather derivatives led to statistically significant reductions of basis risk, compared to the benchmarks. Our results indicate that the VHI-based weather derivatives can be useful alternatives to meteorological indices, especially in regions with sparser weather station networks.

scholarly journals Monitoring Phenology in the Temperate Grasslands of China from 1982 to 2015 and Its Relation to Net Primary Productivity

2019 ◽  
Vol 12 (1) ◽  
pp. 12
Author(s):  
Chaobin Zhang ◽  
Ying Zhang ◽  
Zhaoqi Wang ◽  
Jianlong Li ◽  
Inakwu Odeh
Keyword(s):  
Primary Productivity ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Productivity ◽  
Meteorological Data ◽  
Temperate Grasslands ◽  
The North ◽  
Phenological Changes ◽  
Grass Growth ◽  
Global Inventory

Both vegetation phenology and net primary productivity (NPP) are crucial topics under the background of global change, but the relationships between them are far from clear. In this study, we quantified the spatial-temporal vegetation start (SOS), end (EOS), and length (LOS) of the growing season and NPP for the temperate grasslands of China based on a 34-year time-series (1982–2015) normalized difference vegetation index (NDVI) derived from global inventory modeling and mapping studies (GIMMS) and meteorological data. Then, we demonstrated the relationships between NPP and phenology dynamics. The results showed that more than half of the grasslands experienced significant changes in their phenology and NPP. The rates of their changes exhibited spatial heterogeneity, but their phenological changes could be roughly divided into three different clustered trend regions, while NPP presented a polarized pattern that increased in the south and decreased in the north. Different trend zones’ analyses revealed that phenology trends accelerated after 1997, which was a turning point. Prolonged LOS did not necessarily increase the current year’s NPP. SOS correlated with the NPP most closely during the same year compared to EOS and LOS. Delayed SOS contributed to increasing the summer NPP, and vice versa. Thus, SOS could be a predictor for current year grass growth. In view of this result, we suggest that future studies should further explore the mechanisms of SOS and plant growth.

scholarly journals Multispectral Image-Based Estimation of Drought Patterns and Intensity around Lake Chad, Africa

2019 ◽  
Vol 11 (21) ◽  
pp. 2534 ◽  
Author(s):  
Willibroad Gabila Buma ◽  
Sang-Il Lee
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Condition Index ◽  
Drought Monitoring ◽  
World Population ◽  
Lake Chad ◽  
In Situ Data

As the world population keeps increasing and cultivating more land, the extraction of vegetation conditions using remote sensing is important for monitoring land changes in areas with limited ground observations. Water supply in wetlands directly affects plant growth and biodiversity, which makes monitoring drought an important aspect in such areas. Vegetation Temperature Condition Index (VTCI) which depends on thermal stress and vegetation state, is widely used as an indicator for drought monitoring using satellite data. In this study, using clear-sky Landsat multispectral images, VTCI was derived from Land Surface Temperature (LST) and the Normalized Difference Vegetation Index (NDVI). Derived VTCI was used to observe the drought patterns of the wetlands in Lake Chad between 1999 and 2018. The proportion of vegetation from WorldView-3 images was later introduced to evaluate the methods used. With an overall accuracy exceeding 90% and a kappa coefficient greater than 0.8, these methods accurately acquired vegetation training samples and adaptive thresholds, allowing for accurate estimations of the spatially distributed VTCI. The results obtained present a coherent spatial distribution of VTCI values estimated using LST and NDVI. Most areas during the study period experienced mild drought conditions, though severe cases were often seen around the northern part of the lake. With limited in-situ data in this area, this study presents how VTCI estimations can be developed for drought monitoring using satellite observations. This further shows the usefulness of remote sensing to improve the information about areas that are difficult to access or with poor availability of conventional meteorological data.

Japanese Bindweed (Calystegia hederacea) Abundance and Response to Winter Wheat Seeding Rate and Nitrogen Fertilization in the North China Plain

2013 ◽  
Vol 27 (4) ◽  
pp. 768-777 ◽  
Author(s):  
Alexander Menegat ◽  
Ortrud Jäck ◽  
Jinwei Zhang ◽  
Kathrin Kleinknecht ◽  
Bettina U. Müller ◽  
...  
Keyword(s):  
Winter Wheat ◽  
North China Plain ◽  
North China ◽  
Wheat Yield ◽  
N Fertilization ◽  
Seeding Rate ◽  
The North ◽  
The North China Plain ◽  
Winter Wheat Yield ◽  
Tribenuron Methyl

Japanese bindweed was found to be one of the most abundant and most difficult-to-control weed species during a 2-yr weed survey in more than 100 winter wheat fields in the North China Plain region. Multivariate data analysis showed that Japanese bindweed is most abundant at sites with comparative low nitrogen (N) fertilization intensities and low crop densities. To gain deeper insights into the biology of Japanese bindweed under various N fertilization intensities, winter wheat seeding rates, herbicide treatments, and their interactions, a 2-yr field experiment was performed. In nonfertilized plots, a herbicide efficacy (based on density reduction) of 22% for 2,4-D, and of 25% for tribenuron-methyl was found. The maximum herbicide efficacy in Nmin-fertilized plots (target N value based on expected crop yield minus soil mineral nitrogen content,) was 32% for 2,4-D and 34% for tribenuron-methyl. In plots fertilized according to the farmer's practices, a maximum herbicide efficacy of 72% for 2,4-D and of 64% for tribenuron-methyl could be observed. Furthermore, medium and high seeding rates improved the herbicide efficacy by at least 39% for tribenuron-methyl and 44% for 2,4-D compared to the low seeding rate. Winter wheat yield was not significantly affected by seeding rate itself, whereas at low and medium seeding rates, Nminfertilization was decreasing winter wheat yield significantly compared to the farmer's usual fertilization practice. At the highest seeding rate, Nminfertilization resulted in equal yields compared to the farmer's practices of fertilization.

scholarly journals Responses of Winter Wheat Yield to Drought in the North China Plain: Spatial–Temporal Patterns and Climatic Drivers

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3094
Author(s):  
Jianhua Yang ◽  
Jianjun Wu ◽  
Leizhen Liu ◽  
Hongkui Zhou ◽  
Adu Gong ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Time Scale ◽  
North China Plain ◽  
North China ◽  
Climatic Factors ◽  
Wheat Yield ◽  
Response Patterns ◽  
The North ◽  
The North China Plain ◽  
Winter Wheat Yield

Understanding the winter wheat yield responses to drought are the keys to minimizing drought-related winter wheat yield losses under climate change. The research goal of our study is to explore the response patterns of winter wheat yield to drought in the North China Plain (NCP) and then further to study which climatic factors drive the response patterns. For this purpose, winter wheat yield was simulated by the Environmental Policy Integrated Climate (EPIC) crop model. Drought was quantified by standardized precipitation evapotranspiration index (SPEI), and the contributions of the various climatic factors were evaluated using predictive discriminant analysis (PDA) method. The results showed that the responses of winter wheat yield to different time-scale droughts have obvious spatial differences from the north part to the south part in the NCP. Winter wheat yield is more sensitive to the medium (6–9 months) and long (9–12 months) time-scale droughts that occurred in the key growth periods (April and May). The different response patterns of winter wheat yield to the different time-scale droughts are mainly controlled by temperature and water balance (precipitation minus potential evapotranspiration) in winter in the NCP. Compared with the water balance, temperature plays a more important role in driving the response pattern characteristics. These findings can provide a reference on how to reduce drought influences on winter wheat yield in the NCP.

Sign in / Sign up

Export Citation Format

Share Document