High temporal resolution of leaf area data improves empirical estimation of grain yield

Waldner, François; Horan, Heidi; Yang, Chang-Ihn; Hochman, Zvi

doi:10.1038/s41598-019-51715-7

Cited by 43 publications

(25 citation statements)

References 77 publications

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“…Monitoring crop yield from space using VIs sounds promising, and several studies have already shown the potential of VI-derived crop yield estimates [2,[4][5][6][9][10][11][12]. Empirical or mechanistic modeling strategies are used to derive crop yield from VIs [13].…”

Section: Introductionmentioning

confidence: 99%

Estimating Farm Wheat Yields from NDVI and Meteorological Data

Vannoppen

Gobin

2021

Agronomy

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Information on crop yield at scales ranging from the field to the global level is imperative for farmers and decision makers. The current data sources to monitor crop yield, such as regional agriculture statistics, are often lacking in spatial and temporal resolution. Remotely sensed vegetation indices (VIs) such as NDVI are able to assess crop yield using empirical modelling strategies. Empirical NDVI-based crop yield models were evaluated by comparing the model performance with similar models used in different regions. The integral NDVI and the peak NDVI were weak predictors of winter wheat yield in northern Belgium. Winter wheat (Triticum aestivum) yield variability was better predicted by monthly precipitation during tillering and anthesis than by NDVI-derived yield proxies in the period from 2016 to 2018 (R2 = 0.66). The NDVI series were not sensitive enough to yield affecting weather conditions during important phenological stages such as tillering and anthesis and were weak predictors in empirical crop yield models. In conclusion, winter wheat yield modelling using NDVI-derived yield proxies as predictor variables is dependent on the environment.

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Section: Introductionmentioning

confidence: 99%

Estimating Farm Wheat Yields from NDVI and Meteorological Data

Vannoppen

Gobin

2021

Agronomy

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“…This will be true, especially when heterogeneity in phenology between fields and seasons is large due to differences in farm management practices, crop varietal choices, and weather conditions. We also found that insurance index performance can be improved further by combining LAI and weather predictor variables, which we attribute to the ability of weather data (in particular temperature predictors) to capture crop yield losses associated with deficient grain filling or pollination that would not be fully captured by changes in LAI alone [56].…”

Section: Discussionmentioning

confidence: 76%

Improving the Performance of Index Insurance Using Crop Models and Phenological Monitoring

et al. 2021

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Extreme weather events cause considerable damage to the livelihoods of smallholder farmers globally. Whilst index insurance can help farmers cope with the financial consequences of extreme weather, a major challenge for index insurance is basis risk, where insurance payouts correlate poorly with actual crop losses. We analyse to what extent the use of crop simulation models and crop phenology monitoring can reduce basis risk in index insurance. Using a biophysical process-based crop model (Agricultural Production System sIMulator (APSIM)) applied for rice producers in Odisha, India, we simulate a synthetic yield dataset to train non-parametric statistical models to predict rice yields as a function of meteorological and phenological conditions. We find that the performance of statistical yield models depends on whether meteorological or phenological conditions are used as predictors and whether one aggregates these predictors by season or crop growth stage. Validating the preferred statistical model with observed yield data, we find that the model explains around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level, outperforming vegetation index-based models that were trained directly on the observed yield data. Our methods and findings can guide efforts to design smart phenology-based index insurance and target yield monitoring resources in smallholder farming environments.

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“…This finding agrees with previous studies [37][38][39] and shows that, due to seasonal and spatial variability, any one metric has limited ability for yield estimation within a field. Waldner et al [40] quantified the positive relationship between temporal resolution of VI sequences and accuracy of empirical estimation on grain yield based on crop modelling. We also found that the position of missing values in a VI sequence matters when the sequence is integrated to estimate yield in fields.…”

Section: Discussionmentioning

confidence: 99%

“…Crop yield is a result of interactions from crop genotype (G), farm management (M), and environmental factors (E). Integrated NDVI metrics obtained from sequences of remotely-sensed NDVI capture part of the G×M×E effects on yield [40]. However, there are many G×M×E effects that cannot be measured from space (e.g., evapotranspiration, ratio of aboveground biomass to grain yield, grain quality and crop diseases) that remain sources of uncertainty when estimating yield using remote sensing.…”

Section: Discussionmentioning

confidence: 99%

The Potential of Landsat NDVI Sequences to Explain Wheat Yield Variation in Fields in Western Australia

Shen

Evans

2021

Remote Sensing

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Long-term maps of within-field crop yield can help farmers understand how yield varies in time and space and optimise crop management. This study investigates the use of Landsat NDVI sequences for estimating wheat yields in fields in Western Australia (WA). By fitting statistical crop growth curves, identifying the timing and intensity of phenological events, the best single integrated NDVI metric in any year was used to estimate yield. The hypotheses were that: (1) yield estimation could be improved by incorporating additional information about sowing date or break of season in statistical curve fitting for phenology detection; (2) the integrated NDVI metrics derived from phenology detection can estimate yield with greater accuracy than the observed NDVI values at one or two time points only. We tested the hypotheses using one field (~235 ha) in the WA grain belt for training and another field (~143 ha) for testing. Integrated NDVI metrics were obtained using: (1) traditional curve fitting (SPD); (2) curve fitting that incorporates sowing date information (+SD); and (3) curve fitting that incorporates rainfall-based break of season information (+BOS). Yield estimation accuracy using integrated NDVI metrics was further compared to the results using a scalable crop yield mapper (SCYM) model. We found that: (1) relationships between integrated NDVI metrics using the three curve fitting models and yield varied from year to year; (2) overall, +SD marginally improved yield estimation (r = 0.81, RMSE = 0.56 tonnes/ha compared to r = 0.80, RMSE = 0.61 tonnes/ha using SPD), but +BOS did not show obvious improvement (r = 0.80, RMSE = 0.60 tonnes/ha); (3) use of integrated NDVI metrics was more accurate than SCYM (r = 0.70, RMSE = 0.62 tonnes/ha) on average and had higher spatial and yearly consistency with actual yield than using SCYM model. We conclude that sequences of Landsat NDVI have the potential for estimation of wheat yield variation in fields in WA but they need to be combined with additional sources of data to distinguish different relationships between integrated NDVI metrics and yield in different years and locations.

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High temporal resolution of leaf area data improves empirical estimation of grain yield

Cited by 43 publications

References 77 publications

Estimating Farm Wheat Yields from NDVI and Meteorological Data

Estimating Farm Wheat Yields from NDVI and Meteorological Data

Improving the Performance of Index Insurance Using Crop Models and Phenological Monitoring

The Potential of Landsat NDVI Sequences to Explain Wheat Yield Variation in Fields in Western Australia

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