scholarly journals Wheat Yield Estimation with NDVI Values Using a Proximal Sensing Tool

2020 ◽  
Vol 12 (17) ◽  
pp. 2749
Author(s):  
Marta Aranguren ◽  
Ander Castellón ◽  
Ana Aizpurua
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Crop Yields ◽  
Wheat Yield ◽  
Growing Season ◽  
N Fertilization ◽  
Wheat Crop ◽  
Growth Stages ◽  
Climate Conditions ◽  
N Status

Nitrogen (N) splitting is critical to achieving high crop yields without having negative effects on the environment. Monitoring crop N status throughout the wheat growing season is key to finding the balance between crop N requirements and fertilizer needs. Three soft winter wheat fertilization trials under rainfed conditions in Mediterranean climate conditions were monitored with a RapidScan CS-45 (Holland Scientific, Lincoln, NE, USA) instrument to determine the normalized difference vegetation index (NDVI) values at the GS30, GS32, GS37, and GS65 growth stages. The threshold NDVI values in the Cezanne variety were 0.7–0.75 at the GS32, GS37, and GS65 growing stages. However, for the GS30 growing stage, a threshold value could not be established precisely. At this stage, N deficiency may not affect wheat yield, as long as the N status increases at GS32 stage and it is maintained thereafter. Following the NDVI dynamic throughout the growing season could help to predict the yields at harvest time. Therefore, the ΣNDVI from GS30 to GS65 explains about 80% of wheat yield variability. Therefore, a given yield could be achieved with different dynamics in wheat NDVI values throughout the growing cycle. The determined ranges of the NDVI values might be used for developing new fertilization strategies that are able to adjust N fertilization to wheat crop needs.

Forecasting wheat yield in a Mediterranean-type environment from the NOAA satellite

1995 ◽  
Vol 46 (1) ◽  
pp. 113 ◽  
Author(s):  
RCG Smith ◽  
J Adams ◽  
DJ Stephens ◽  
PT Hick
Keyword(s):  
Spatial Variation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spatial Information ◽  
Stem Elongation ◽  
Wheat Yield ◽  
Growing Season ◽  
Wheat Crop ◽  
Canopy Development ◽  
Final Yield

This paper reports the relationship between the spatial variation in mean wheat yield/ha of 50 Local Government Areas in Western Australia and satellite measures of the Normalized Difference Vegetation Index (NDVI). Yield/ha was based on estimates of the area harvested and actual grain received by the Cooperative Bulk Handling Ltd. The study area covered 16.3 million ha, in which 2.9 million ha of wheat were sown and 4.66 million tonnes of grain harvested. This was 78% of the total Western Australian wheat crop. Spatial variations in NDVI in early July, at around stem elongation, accounted for 46% of the spatial variation in final yield. This increased to 56% of yield variance around the onset of anthesis at the end of August. It remained high until early November (48%) when crops were senescing or senescent. A combination of NDVI from late August and early November accounted for 70% of the yield variance. In comparison, total rainfall during the 1992 growing season from April to October, the main determinant of yield variations, accounted for 28% of the yield variation. The significant correlation of NDVI with final yield by the middle of the growing season 3 to 5 months before harvest indicates the feasibility of making useful yield forecasts from this time onwards. In addition, the NDVI could provide useful spatial information on the significance of the yield/canopy development/water use relationship which underlies this correlation.

scholarly journals Normalized Difference Vegetation Index as a Tool for Wheat Yield Estimation: A Case Study from Faisalabad, Pakistan

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Syeda Refat Sultana ◽  
Amjed Ali ◽  
Ashfaq Ahmad ◽  
Muhammad Mubeen ◽  
M. Zia-Ul-Haq ◽  
...  
Keyword(s):  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Experiments ◽  
Wheat Yield ◽  
Grain Filling ◽  
Research Area ◽  
Wheat Crop ◽  
Growth Stages ◽  
Wheat Cultivars

For estimation of grain yield in wheat, Normalized Difference Vegetation Index (NDVI) is considered as a potential screening tool. Field experiments were conducted to scrutinize the response of NDVI to yield behavior of different wheat cultivars and nitrogen fertilization at agronomic research area, University of Agriculture Faisalabad (UAF) during the two years 2008-09 and 2009-10. For recording the value of NDVI, Green seeker (Handheld-505) was used. Split plot design was used as experimental model in, keeping four nitrogen rates (N1= 0 kg ha−1,N2= 55 kg ha−1,N3=110 kg ha−1, andN4= 220 kg ha−1) in main plots and ten wheat cultivars (Bakkhar-2001, Chakwal-50, Chakwal-97, Faisalabad-2008, GA-2002, Inqlab-91, Lasani-2008, Miraj-2008, Sahar-2006, and Shafaq-2006) in subplots with four replications. Impact of nitrogen and difference between cultivars were forecasted through NDVI. The results suggested that nitrogen treatment N4(220 kg ha−1) and cultivar Faisalabad-2008 gave maximum NDVI value (0.85) at grain filling stage among all treatments. The correlation among NDVI at booting, grain filling, and maturity stages with grain yield was positive (R2 = 0.90;R2 = 0.90;R2 = 0.95), respectively. So, booting, grain filling, and maturity can be good depictive stages during mid and later growth stages of wheat crop under agroclimatic conditions of Faisalabad and under similar other wheat growing environments in the country.

scholarly journals Monitoring agricultural drought using geospatial techniques: a case study of Thal region of Punjab, Pakistan

2020 ◽  
Vol 11 (S1) ◽  
pp. 203-216 ◽  
Author(s):  
Muhammad Amin ◽  
Mobushir Riaz Khan ◽  
Sher Shah Hassan ◽  
Aftab Ahmad Khan ◽  
Muhammad Imran ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Wheat Yield ◽  
Standardized Precipitation Index ◽  
Weather Conditions ◽  
Agricultural Drought ◽  
Wheat Crop ◽  
Drought Risk ◽  
Risk Map ◽  
Agricultural Risk

Abstract The Thal region of Punjab often experiences dry weather conditions with extreme variability in rainfall on a spatiotemporal scale during Rabi cropping season. The current study assesses the impacts of agricultural drought on wheat crops for 2000–2015. MOD13Q1 and CHIRPS data were used for identifying and assessing variation in agricultural drought patterns and severity. Standardized Precipitation Index (SPI), Normalized Difference Vegetation Index (NDVI), Vegetation Condition Index (VCI), Stress Vegetation Index (STVI) and wheat crop yield anomalies were computed to characterize the gravity of drought across the Thal region. The results indicate that the wheat Rabi cropping seasons of the years 2000–2002 experienced extreme agricultural drought, with a spatial difference in severity level causing low and poor yield, while the years 2011 and 2014 were almost normal among all the years, leaving varied impacts on wheat yield. The combined agricultural risk map was generated by integrating the agricultural and meteorological droughts severity maps. The combined risk map generated using weighted overlay analysis of all the parameters indicate that the total Thal area can be classified into slight, moderate and no drought covering 28.12, 12.76, and 59.12% respectively of the total area. Hence an agricultural risk map would be extremely helpful as a tool to guide the decision-making process for monitoring drought risk on agricultural productivity.

Analyzing Temperature and Precipitation Influences on Yield Distributions of Canola and Spring Wheat in Saskatchewan

2017 ◽  
Vol 56 (4) ◽  
pp. 897-913 ◽  
Author(s):  
Ting Meng ◽  
Richard Carew ◽  
Wojciech J. Florkowski ◽  
Anna M. Klepacka
Keyword(s):  
Crop Yield ◽  
Spring Wheat ◽  
Climate Model ◽  
Crop Yields ◽  
Wheat Yield ◽  
Growing Season ◽  
Weather Data ◽  
Growth Stages ◽  
Yield Distribution ◽  
The Impact

AbstractThe IPCC indicates that global mean temperature increases of 2°C or more above preindustrial levels negatively affect such crops as wheat. Canadian climate model projections show warmer temperatures and variable rainfall will likely affect Saskatchewan’s canola and spring wheat production. Drier weather will have the greatest impact. The major climate change challenges will be summer water availability, greater drought frequencies, and crop adaptation. This study investigates the impact of precipitation and temperature changes on canola and spring wheat yield distributions using Environment Canada weather data and Statistics Canada crop yield and planted area for 20 crop districts over the 1987–2010 period. The moment-based methods (full- and partial-moment-based approaches) are employed to characterize and estimate asymmetric relationships between climate variables and the higher-order moments of crop yields. A stochastic production function and the focus on crop yield’s elasticity imply choosing the natural logarithm function as the mean function transformation prior to higher-moment function estimation. Results show that average crop yields are positively associated with the growing season degree-days and pregrowing season precipitation, while they are negatively affected by extremely high temperatures in the growing season. The climate measures have asymmetric effects on the higher moments of crop yield distribution along with stronger effects of changing temperatures than precipitation on yield distribution. Higher temperatures tend to decrease wheat yields, confirming earlier Saskatchewan studies. This study finds pregrowing season precipitation and precipitation in the early plant growth stages particularly relevant in providing opportunities to develop new crop varieties and agronomic practices to mitigate climate changes.

scholarly journals A Sustainability Assessment of the Greenseeker N Management Tool: A Lysimetric Experiment on Barley

2020 ◽  
Vol 12 (18) ◽  
pp. 7303
Author(s):  
Carolina Fabbri ◽  
Marco Napoli ◽  
Leonardo Verdi ◽  
Marco Mancini ◽  
Simone Orlandini ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sustainability Assessment ◽  
Stem Elongation ◽  
N Fertilization ◽  
Management Tool ◽  
Growth Stages ◽  
Greenhouse Gasses ◽  
Crop Parameters ◽  
N Management

A preliminary study was conducted to analyze the sustainability of barley production through: (i) investigating sensor-based nitrogen (N) application on barley performance, compared with conventional N management (CT); (ii) assessing the potential of the Normalized Difference Vegetation Index (NDVI) at different growth stages for within-season predictions of crop parameters; and (iii) evaluating sensor-based fertilization benefits in the form of greenhouse gasses mitigation. Barley was grown under CT, sensor-based management (RF) and with no N fertilization (Control). NDVI measurements and RF fertilization were performed using a GreenSeeker™ 505 hand-held optical sensor. Gas emissions were measured using a static chamber method with a portable gas analyzer. Results showed that barley yield was not statistically different under RF and CF, while they both differed significantly from Control. Highly significant positive correlations were observed between NDVI and production parameters at harvesting from the middle of stem elongation to the medium milk stage across treatments. Our findings suggest that RF is able to decrease CO2 emission in comparison with CF. The relationship between N fertilization and CH4 emission showed high variability. These preliminary results provide an indication of the benefits achieved using a simple proximal sensing methodology to support N fertilization.

scholarly journals Resilience of UK crop yields to changing climate extremes

2021 ◽  
Author(s):  
Louise J. Slater ◽  
Chris Huntingford ◽  
Richard F. Pywell ◽  
John W. Redhead ◽  
Elizabeth J. Kendon
Keyword(s):  
Crop Yields ◽  
Wheat Yield ◽  
Climate Extremes ◽  
Climate Impacts ◽  
Future Climate ◽  
Extreme Weather Events ◽  
Growth Stages ◽  
Climate Projections ◽  
Climate Conditions ◽  
Changing Climate

Abstract. Recent extreme weather events have had severe impacts on UK crop yields, and so there is concern that a greater frequency of extremes could affect crop production in a changing climate. Here we investigate potential future impacts of climate projections on wheat, the most widely grown cereal crop globally, in a temperate country with currently favourable wheat-growing conditions. Past and projected climate conditions are considered for key wheat growth stages (Foundation, Construction and Production). Historically, following the plateau of UK wheat yields since the 1990s, we find there has been a recent significant increase in wheat yield volatility, which is partially explained by seasonal metrics of temperature and precipitation, including mean, extremes, and intra-seasonal variability. Strong associations between climate and yield anomalies occur during years with cumulative climate impacts across growth stages, when climate extremes ‘escape’ the ability of farmers to adapt through agronomic means. We then analyse the latest 2.2 km UK Climate Projections for the UK’s three main wheat-growing regions. Climate projections indicate that on average across the three regions, the Foundation growth stage (broadly 1st October to 9th April) is likely to become warmer and wetter, while the Construction (10th April to 10th June) and Production (11th June to 26th July) stages are likely to become warmer and slightly drier. An analogue approach, comparing historical climate conditions with future climate projections, reveals a mixed picture of future climate conditions for UK crop yields. Projected warmer winter night temperatures are likely to prove beneficial in the Foundation stage, but concurrent increases in heavy rain may be detrimental. Similarly, warmer and drier mean conditions may enhance yields during the Production stage, but increases in high temperatures and heat variability may increase plant stress, while decreases in rainfall may also threaten adequate water supply. Since future climatic conditions are likely to move outside the historically observed range, there may be challenges for agriculture to adapt management practices to realise any potential benefits.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

scholarly journals Coastal Mangrove Response to Marine Erosion: Evaluating the Impacts of Spatial Distribution and Vegetation Growth in Bangkok Bay from 1987 to 2017

2020 ◽  
Vol 12 (2) ◽  
pp. 220 ◽  
Author(s):  
Han Xiao ◽  
Fenzhen Su ◽  
Dongjie Fu ◽  
Qi Wang ◽  
Chong Huang
Keyword(s):  
Spatial Distribution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mangrove Ecosystem ◽  
Early Growth ◽  
Growth Stages ◽  
Human Disturbances ◽  
Vegetation Growth ◽  
Two Parameters ◽  
The Impact

Long time-series monitoring of mangroves to marine erosion in the Bay of Bangkok, using Landsat data from 1987 to 2017, shows responses including landward retreat and seaward extension. Quantitative assessment of these responses with respect to spatial distribution and vegetation growth shows differing relationships depending on mangrove growth stage. Using transects perpendicular to the shoreline, we calculated the cross-shore mangrove extent (width) to represent spatial distribution, and the normalized difference vegetation index (NDVI) was used to represent vegetation growth. Correlations were then compared between mangrove seaside changes and the two parameters—mangrove width and NDVI—at yearly and 10-year scales. Both spatial distribution and vegetation growth display positive impacts on mangrove ecosystem stability: At early growth stages, mangrove stability is positively related to spatial distribution, whereas at mature growth the impact of vegetation growth is greater. Thus, we conclude that at early growth stages, planting width and area are more critical for stability, whereas for mature mangroves, management activities should focus on sustaining vegetation health and density. This study provides new rapid insights into monitoring and managing mangroves, based on analyses of parameters from historical satellite-derived information, which succinctly capture the net effect of complex environmental and human disturbances.

scholarly journals Analysis of Drought Characteristics in Xilingol Grassland of Northern China Based on SPEI and Its Impact on Vegetation

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Siqin Tong ◽  
Yuhai Bao ◽  
Rigele Te ◽  
Qiyun Ma ◽  
Si Ha ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Pearson Correlation ◽  
Growing Season ◽  
Vegetation Coverage ◽  
Semiarid Region ◽  
Linear Regression Method ◽  
Upward Trend ◽  
Drought Effect ◽  
Positive Correlation

This research is based on the standardized precipitation evapotranspiration index (SPEI) and normalized difference vegetation index (NDVI) which represent the drought and vegetation condition on land. Take the linear regression method and Pearson correlation analysis to study the spatial and temporal evolution of SPEI and NDVI and the drought effect on vegetation. The results show that (1) during 1961–2015, SPEI values at different time scales showed a downward trend; SPEI-12 has a mutation in 1997 and the SPEI value significantly decreased after this year. (2) During 2000–2015, the annual growing season SPEI has an obvious upward trend in time and the apparent wetting spatially. (3) In the recent 16 years, the growing season NDVI showed an upward trend and more than 80% of the total area’s vegetation increased in Xilingol. (4) Vegetation coverage in Xilingol grew better in humid years and opposite in arid years. SPEI and NDVI had a significant positive correlation; 98% of the region showed positive correlation, indicating that meteorological drought affects vegetation growth more in arid and semiarid region. (5) The effect of drought on vegetation has lag effect, and the responses of different grassland types to different scales of drought were different.

scholarly journals Phenology-Based Residual Trend Analysis of MODIS-NDVI Time Series for Assessing Human-Induced Land Degradation

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3676 ◽  
Author(s):  
Hao Chen ◽  
Xiangnan Liu ◽  
Chao Ding ◽  
Fang Huang
Keyword(s):  
Trend Analysis ◽  
Land Degradation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Growing Season ◽  
Practical Method ◽  
Large Area ◽  
Modis Ndvi ◽  
Daily Precipitation Data ◽  
Improved Model

Land degradation is a widespread environmental issue and an important factor in limiting sustainability. In this study, we aimed to improve the accuracy of monitoring human-induced land degradation by using phenological signal detection and residual trend analysis (RESTREND). We proposed an improved model for assessing land degradation named phenology-based RESTREND (P-RESTREND). This method quantifies the influence of precipitation on normalized difference vegetation index (NDVI) variation by using the bivariate linear regression between NDVI and precipitation in pre-growing season and growing season. The performances of RESTREND and P-RESTREND for discriminating land degradation caused by climate and human activities were compared based on vegetation-precipitation relationship. The test area is in Western Songnen Plain, Northeast China. It is a typical region with a large area of degraded drylands. The MODIS 8-day composite reflectance product and daily precipitation data during 2000–2015 were used. Our results showed that P-RESTREND was more effective in distinguishing different drivers of land degradation than the RESTREND. Degraded areas in the Songnen grasslands can be effectively detected by P-RESTREND. Therefore, this modified model can be regarded as a practical method for assessing human-induced land degradation.

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