Cotton yield estimation based on NOAA/AVHRR produced NDVI

Dalezios, N. R.; Domenikiotis, C.; Λουκάς, Αθανάσιος; Tzortzios, S.T.; Kalaitzidis, Chariton

doi:10.1016/s1464-1909(00)00247-1

Cited by 49 publications

(23 citation statements)

References 4 publications

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“…NDVI data is mapped on a roughly 1 km 2 grid covering the earth, thus fitting in nicely with the profit-function surfaces. Numerous studies have shown that higher NDVI scores are a reliable predictor of higher crop yields (Daleziosl et al, 2000;Seiler et al, 1999;Bastiaanssena and Alib, 2003). Using these data, 1 Dry sheep equivalents (DSE).…”

Section: Mapping Yieldmentioning

confidence: 99%

Costing yield loss from acidity, sodicity and dryland salinity to Australian agriculture

Hajkowicz

Young

2005

Land Degrad Dev

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Salinity, sodicity and acidity are three major soil constraints that limit crop and pasture yields in Australia. In this paper estimates are made of the potential benefits arising from their treatment by measuring and mapping their impact on agricultural profit. This is achieved by estimating the increase in profit for Australia's main commodities that would occur if the three soil constraints were costlessly ameliorated. These estimates reveal the upper achievable limit on investment returns. They are also indicative of each soil constraint's economic significance to Australian agriculture. It was found that costless removal of salinity would increase annual profits by A$187 million, sodicity by A$1034Á6 million and acidity by A$1584Á5 million. This equates to 2Á9 per cent, 15Á8 per cent and 24Á2 per cent of total net economic return. It was also found that worsening salinity extent and severity over 2000-2020 has a present value of A$496-A$712 million. Although soil salinity is currently the focus of much public attention, this analysis suggests that from a production viewpoint the correction of sodic and acidic soils may create greater private economic benefit. Opportunities vary considerably among industries. In particular, there is considerable opportunity for the horticultural and viticultural sector to address acidity issues. Whether gross benefits translate into net benefits is a complex question requiring access to context and location-specific information.

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Section: Mapping Yieldmentioning

confidence: 99%

Costing yield loss from acidity, sodicity and dryland salinity to Australian agriculture

Hajkowicz

Young

2005

Land Degrad Dev

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“…Zhao et al (2007) mentioned correlation values between NDVI and cotton yield ranging from 0.54 to 0.76, based on a two-year evaluation. Dalezios et al (2001) analyzed orbital images of various locations in a three-year period to estimate cotton yield on a regional scale, and they obtained a correlation of 0.62 between NDVI and yield. The authors concluded that the correlation degree between these two factors depended on the field and the year, although, the variability over the absolute average values of NDVI reflected the variability in regional yield during the three growing seasons (DALEZIOS et al, 2001).…”

Section: Resultsmentioning

confidence: 99%

“…Dalezios et al (2001) analyzed orbital images of various locations in a three-year period to estimate cotton yield on a regional scale, and they obtained a correlation of 0.62 between NDVI and yield. The authors concluded that the correlation degree between these two factors depended on the field and the year, although, the variability over the absolute average values of NDVI reflected the variability in regional yield during the three growing seasons (DALEZIOS et al, 2001). The correlation between yield variability and the vegetation index improved from 0.37 to 0.78 when CV-NDVI and cotton yield was considered ( Figure 1B).…”

Section: Resultsmentioning

confidence: 99%

“…The yield crop potential is linked to specific soil and weather conditions that occur since the plant's establishment (OLLINGER, 2011). The condition, distribution, structure and the development of the vegetation through the phenological stages can affect the relation between yield and NDVI (DALEZIOS et al, 2001). Koo and Cindy (2015) noted that the yield is impacted by the manner in which daily precipitation is distributed during the crop cycle.…”

Section: Introductionmentioning

confidence: 99%

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Relationship between cotton productivity and variability of NDVI obtained by landsat images

Baio¹,

Neves²,

Campos³

et al. 2018

Biosci. J.

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The availability of satellite images has generated a large number of regional and global studies on vegetation mapping. Such studies have related the growth parameters, nutrient status, physiological responses, and water resources to the yield of agricultural crops or native vegetation. The NDVI (Normalized Difference Vegetation Index) is associated with parameters of growth and yield with readings at several moments of the crop cycle. The objective of this work was to correlate the yield and variability of the NDVI in cotton fields by analyzing Landsat satellite images acquired over nine growing seasons. The study involved the analysis of 101 cotton production fields located in West-Central Brazil. One Landsat image was used during each crop cycle, and the average yield was computed based on total fiber harvested at each field. The fiber yield ranged from 393 to 2,030 kg ha -1, and its correlation with NDVI was 0.37. The coefficient of variation (CV) had a negative correlation with yield, approximating -58.1 kg ha -1 for every one percent increment of the CV. The CV explained the yield variability over the cotton fields more accurately than the average absolute NDVI value.

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“…To better capture this spatial heterogeneity, we reapportion the primary product yield of grid cells of each commodity type within each SLA according to a greenness index called normalised difference vegetation index (NDVI). NDVI has been shown to be a good indicator of agricultural yield [6,28] for a variety of crops including maize [5], cotton [14,15], wheat [17,19], nuts [18], soybeans [20] and a variety of other crops [26].…”

Section: Yieldmentioning

confidence: 99%

Mapping Economic Returns to Agriculture for Informing Environmental Policy in the Murray–Darling Basin, Australia

Bryan

Hajkowicz

Marvanek

et al. 2008

Environ Model Assess

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Cotton yield estimation based on NOAA/AVHRR produced NDVI

Cited by 49 publications

References 4 publications

Costing yield loss from acidity, sodicity and dryland salinity to Australian agriculture

Costing yield loss from acidity, sodicity and dryland salinity to Australian agriculture

Relationship between cotton productivity and variability of NDVI obtained by landsat images

Mapping Economic Returns to Agriculture for Informing Environmental Policy in the Murray–Darling Basin, Australia

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