Search claims · PlantClaims

Method / resourceobservedoriginal resultnegativemeasured

Claim · model-written summary

Prediction correlations declined for percentiles below 20% and above 85%.

Taxon: winter wheatTriticum aestivume1
Outcome: correlationse0
Direction: decreasee0
Value: 20 %e0
Comparator: percentiles in betweene0

Verbatim

e0
“correlations were found to decrease for percentiles below 20 % and above 85 %, respectively, but to be relatively stable for percentiles in between”
✓ located verbatim in the source · abstract, characters 940–1087

e1
“GY prediction of winter wheat”
✓ located verbatim in the source · abstract, characters 822–851

Title

Evaluation of plot level aggregation statistics for spectral grain yield prediction

Lukas Prey; Johannes Seidl-Schulz; Patrick Ole Noack European Journal of Agronomy 2025. doi:10.1016/j.eja.2025.127733

Other claims from this paper (3)

Method / resource Correlations between spectral aggregation and wheat grain yield varied with spectral data, dates, and trials.
Method / resource Mean and median aggregation had no substantial difference in correlations.
Recommendation The authors recommend mean or median aggregation and avoiding extreme percentiles.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The model accurately predicted increased cucumber fruit yield with supplemental lighting.

Taxon: cucumberCucumis sativuse0
Treatment: supplemental lightinge0
Outcome: yielde0
Direction: increasee0
Value: increasee0

Verbatim

e0
“The model also accurately predicted the decrease in cucumber fruit yield under low-sunlight conditions (shading treatments) and the increase in yield due to supplemental lighting.”
✓ located verbatim in the source · abstract, characters 1249–1428

Title

Estimation of Cucumber Fruit Yield Cultivated Under Different Light Conditions in Greenhouses

Inseo Hong; Jin Yu; Seung Jae Hwang; et al. Horticulturae 2024. doi:10.3390/horticulturae10101117

Other claims from this paper (3)

Method / resource The improved model predicted cucumber yield under control and shading conditions with R2 greater than 0.98.
Method / resource With supplemental lighting, the model predicted cucumber yield with R2 greater than 0.99.
Method / resource The model accurately predicted reduced cucumber fruit yield under shading.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Calculated dry matter yield agreed acceptably with experiments when stand closure and growth decline dates were known.

Taxon: riceOryza sativat0
Outcome: total DM yieldse0
Qualifier: provided that the dates when a closed stand was obtained and when the growth rate started to decrease were knowne0
Comparator: experimental resultse0

Verbatim

e0
“Calculated total DM yields, grain yields and grain:straw ratios were in acceptable agreement with the experimental results, provided that the dates when a closed stand was obtained and when the growth rate started to decrease were known”
✓ located verbatim in the source · abstract, characters 351–587

t0
“rice”
✓ located verbatim in the source · title, characters 27–31

Title

A calculation of potential rice yields.

A. van Ittersum Netherlands Journal of Agricultural Science 1972. doi:10.18174/njas.v20i1.17294

Other claims from this paper (2)

Method / resource Calculated grain yields agreed acceptably with experiments under the stated timing condition.
Method / resource Calculated grain to straw ratios agreed acceptably with experiments under the stated timing condition.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

NDVI plus days to heading reduced prediction RMSE by 10%.

Taxon: wheatTriticum aestivumt0
Outcome: root mean square errore0
Direction: decreasee0
Value: 10%e0

Not reportedComparator

Verbatim

e0
“models including NDVI and days to heading providing a 50% increase in the prediction accuracy and a 10% decrease in the root mean square error.”
✓ located verbatim in the source · abstract, characters 1064–1207

t0
“wheat”
✓ located verbatim in the source · title, characters 20–25

Title

Improving in-season wheat yield prediction using remote sensing and additional agronomic traits as predictors

Adrian Gracia-Romero; Rubén Rufo; David Gómez-Candón; et al. Frontiers in Plant Science 2023. doi:10.3389/fpls.2023.1063983

Other claims from this paper (6)

Method / resource Larger training sets moderately improved grain yield predictions.
Method / resource Adding days to heading, ear density or height generally lowered BIC relative to NDVI alone.
Method / resource NDVI plus days to heading raised prediction accuracy by 50% when NDVI saturated.
Method / resource NDVI and added traits were unreliable yield predictors in wheat landraces.
Recommendation Conventional yield quantification was recommended for wheat landraces.
Mechanism NDVI saturation and underestimation may reflect yield component differences it cannot detect.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

PCA selected soil compactibility and total nitrogen as best yield predictors.

Taxon: Crope1
Outcome: best yield predictorse0
Comparator: soil compactibility and total Ne0

Verbatim

e0
“Principal component analysis (PCA) selected soil compactibility and total N as the best yield predictors”
✓ located verbatim in the source · abstract, characters 1325–1429

e1
“Crop yield increased with the CC‐induced decrease”
✓ located verbatim in the source · abstract, characters 1034–1083

Title

Summer Cover Crops Fix Nitrogen, Increase Crop Yield, and Improve Soil–Crop Relationships

Humberto Blanco‐Canqui; M. M. Claassen; D. R. Presley Agronomy Journal 2012. doi:10.2134/agronj2011.0240

Other claims from this paper (9)

Intervention effect Summer cover crops increased crop yields, especially under low nitrogen.
Intervention effect Sunn hemp raised sorghum yield 1.18 to 1.54 times at zero nitrogen.
Intervention effect Sunn hemp raised wheat yield 1.60 times in the first year at zero nitrogen.
Intervention effect At 66 kg nitrogen sunn hemp had no effect on sorghum yield.
Intervention effect At 66 kg nitrogen sunn hemp increased wheat yield in two of three years.
Intervention effect Cover crops increased soil total nitrogen pool by 270 kg per hectare.
Association Yield increased as soil compactibility and temperature decreased.
Association Yield increased with soil aggregation, carbon, nitrogen and water.
Observation Summer legume cover crops in no till fixed nitrogen and improved soil crop relationships.

Method / resourceobservedoriginal resultno effectsimulated

Claim · model-written summary

At the warmest Brazilian location, some models simulated no yield effect.

Taxon: soybeanGlycine maxt0
Outcome: yielde0
Direction: no effecte0
Comparator: modelse0
Setting: Brazile0

Verbatim

e0
“In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield”
✓ located verbatim in the source · abstract, characters 1109–1262

t0
“soybean”
✓ located verbatim in the source · title, characters 4–11

Title

Are soybean models ready for climate change food impact assessments?

Kritika Kothari; Rafael Battisti; Kenneth J. Boote; et al. European Journal of Agronomy 2022. doi:10.1016/j.eja.2022.126482

Other claims from this paper (10)

Method / resource Models varied considerably in simulated soybean yield responses to temperature and CO2.
Method / resource At the coolest Argentine location, models simulated a 24% yield reduction under 3 °C warming.
Method / resource At the coolest Argentine location, other models simulated yield increases up to 29%.
Method / resource At the warmest Brazilian location, simulated yield losses reached 38% under 3 °C warming.
Method / resource Raising CO2 from 360 to 540 ppm produced simulated yield increases of 6% to 31%.
Method / resource Calibration did not reduce variation across models.
Method / resource Calibration altered temperature yield responses in some models.
Method / resource Ensemble means reduced uncertainty in soybean yield simulations.
Method / resource Ensemble mean temperature and CO2 responses resembled literature results.
Recommendation Further model development using elevated CO2 and temperature experiments was recommended.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Forecast error and variability decreased through the season.

Taxon: cropt0
Outcome: forecast error and its variabilitye0
Direction: decreasee0
Comparator: growing seasone0

Verbatim

e0
“Forecasts generally improve during the growing season; both the forecast error and its variability tend to progressively decrease.”
✓ located verbatim in the source · abstract, characters 1445–1575

t0
“crop”
✓ located verbatim in the source · title, characters 24–28

Title

Performance of the MARS-crop yield forecasting system for the European Union: Assessing accuracy, in-season, and year-to-year improvements from 1993 to 2015

M. van der Velde; L. Nisini Agricultural Systems 2018. doi:10.1016/j.agsy.2018.06.009

Other claims from this paper (13)

Method / resource France had the lowest median MAPE across crops, 3.73%.
Method / resource Portugal had the highest median MAPE, 14.37%.
Method / resource Yield forecasts generally underestimated reported yields.
Method / resource Soft wheat yield forecasts were systematically low across member states.
Method / resource Seasonal forecast improvements were lower for oilseeds.
Method / resource EU-12 rapeseed yield forecasts improved significantly from 1993 to 2015.
Method / resource EU-12 potato yield forecasts improved significantly from 1993 to 2015.
Method / resource EU-12 sugar beet yield forecasts improved significantly from 1993 to 2015.
Method / resource Other crops showed no evidence of long term forecast improvement.
Method / resource No crop forecast improved significantly at EU-27 level in 2006–2015.
Observation Yield growth slowed or plateaued in several member states.
Observation Volatility in yield statistics increased.
Recommendation Authors call for renewed operational forecasting efforts.

Method / resourceobservedoriginal resultprojected

Claim · model-written summary

Geographical probability maps were provided for future mean yield decreases and yield variability increases.

Taxon: paddy ricet0
Outcome: geographical mapse0

Not reportedComparatorValue

Verbatim

t0
“paddy rice”
✓ located verbatim in the source · title, characters 54–64

e0
“We also provided geographical maps of the probability that the future 20-year mean yield will decrease and that the future standard deviation of yield for 20 years will increase.”
✓ located verbatim in the source · abstract, characters 1689–1867

Title

Probabilistic evaluation of climate change impacts on paddy rice productivity in Japan

Toshichika Iizumi; Masayuki Yokozawa; Motoki Nishimori Climatic Change 2010. doi:10.1007/s10584-010-9990-7

Other claims from this paper (6)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Reducing yield map resolution to 40 m reduced mapping error.

Taxon: winter wheat (Triticum aestivum L.)Triticum aestivume1
Outcome: error in the yield mapse0
Direction: reducede0
Value: 40 me0
Setting: southern Swedene1

Verbatim

e0
“a decrease in map resolution to 40 m reduced the error in the yield maps obtained”
✓ located verbatim in the source · abstract, characters 701–782

e1
“winter wheat (Triticum aestivum L.) yield could be mapped in 18 fields in southern Sweden”
✓ located verbatim in the source · abstract, characters 420–509

Title

Easy yield mapping for precision agriculture

Omran Alshihabi; Kristin Persson; Mats Söderström Acta Agriculturae Scandinavica Section B - Soil & Plant Science 2024. doi:10.1080/09064710.2024.2411950

Other claims from this paper (5)

Method / resource NDWI was the most efficient vegetation index for yield mapping.
Method / resource Combining imagery from booting and milk development stages performed slightly better than other stage combinations.
Method / resource The best within-field model had mean absolute error of 0.40 tonnes ha−1 at 40-m resolution.
Method / resource The model had MAE of 0.75 tonnes ha−1 for field means on 69 farms compared with farmer reported yields.
Recommendation The authors propose on-demand satellite yield maps for stakeholders if yield data are available.

Method / resourceobservedoriginal resultmeasured

Claim · model-written summary

Mortality rate was an earlier indicator of decline than yield loss.

Taxon: grapevineVitist0
Outcome: mortality ratee0
Direction: earliere0
Comparator: yield losse0

Verbatim

e0
“mortality rate and the normalised difference vegetation index (NDVI) indicators were shown to be earlier indicators of grapevine decline than yield loss (yield achievement ratio, YAR)”
✓ located verbatim in the source · abstract, characters 1436–1619

t0
“grapevine”
✓ located verbatim in the source · title, characters 23–32

Title

A systemic approach to grapevine decline diagnosed using three key indicators: plant mortality, yield loss and vigour decrease

Anne MEROT; Guillaume Coulouma; Nathalie Smits; et al. OENO One 2023. doi:10.20870/oeno-one.2023.57.1.5575

Other claims from this paper (9)

Association Farmers’ perceptions agreed with field measures of decline.
Observation Vine mortality spread progressively over years.
Association The survey and historical analysis found no direct link between decline and yield loss.
Observation Yield fluctuated annually without systematically declining.
Method / resource NDVI was an earlier indicator of decline than yield loss.
Method / resource Ground based NDVI indicators were proposed for accessible decline diagnosis.
Method / resource An aerial photograph image analysis method was proposed for accessible decline diagnosis.
Association NDVI indicators correlated linearly with yield achievement ratio.
Association NDVI indicators correlated linearly with mortality rate.

“yield decrease”

Claim types

Direction of the findings

Over time

Claim type

Research area

Polarity

Statistics