Multivariate genomic analysis and optimal contributions selection predicts high genetic gains in cooking time, iron, zinc, and grain yield in common beans in East Africa4
“Progeny were predicted by OCS to increase in mean GY by 12.4%, decrease in mean CKT by 9.3%, and increase in mean Fe and Zn content by 6.9 and 4.6%, respectively, with low achieved coancestry of 0.032.”
“Progeny were predicted by OCS to increase in mean GY by 12.4%, decrease in mean CKT by 9.3%, and increase in mean Fe and Zn content by 6.9 and 4.6%, respectively, with low achieved coancestry of 0.032.”
“Progeny were predicted by OCS to increase in mean GY by 12.4%, decrease in mean CKT by 9.3%, and increase in mean Fe and Zn content by 6.9 and 4.6%, respectively, with low achieved coancestry of 0.032.”
“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)”
“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)”
“Spatial expansion of S. hermonthica and decrease in millet yield in a field was slower, on average, when stochasticity of attachment of seedlings to the host was included and compared to the deterministic model”
e5
“Sensitivity analysis showed that only three out of eight life cycle parameters were of minor importance for seedbank dynamics and millet yield”
“Spatial expansion of S. hermonthica and decrease in millet yield in a field was slower, on average, when stochasticity of attachment of seedlings to the host was included and compared to the deterministic model”
The spatial patterns of emerged S. hermonthica plantse2
Comparator
distribution typically observed in farmers’ fieldse3
Verbatim
e1
“Spatial expansion of S. hermonthica and decrease in millet yield in a field was slower, on average, when stochasticity of attachment of seedlings to the host was included and compared to the deterministic model”
e2
“The spatial patterns of emerged S. hermonthica plants”
e3
“resembled the distribution typically observed in farmers’ fields”
“almost 40% decrease in water consumption is achieved in comparison to constant irrigation method of the DSSAT model without any significant decrease in yield and leaf area index.”
e1
“DSSAT (Decision Support System for Agro-technology Transfer) maize crop simulation model”
“almost 40% decrease in water consumption is achieved in comparison to constant irrigation method of the DSSAT model without any significant decrease in yield and leaf area index.”
e1
“DSSAT (Decision Support System for Agro-technology Transfer) maize crop simulation model”
“Using simple modular approach of DSSAT to calculate daily yield and leaf area index along with the proposed reinforcement learning controller, almost 40% decrease in water consumption is achieved in comparison to constant irrigation method of the DSSAT model”
e1
“DSSAT (Decision Support System for Agro-technology Transfer) maize crop simulation model”
“In the experiment, the proposed irrigation scheduling system is validated and compared with existing conventional irrigation systems to explore its performance.”
e1
“Implementation of this system leads to a decrease in water use by 59.61% and electrical energy consumption by 67.35%, while the crop yield increases by 22.58%.”
“In the experiment, the proposed irrigation scheduling system is validated and compared with existing conventional irrigation systems to explore its performance.”
e1
“Implementation of this system leads to a decrease in water use by 59.61% and electrical energy consumption by 67.35%, while the crop yield increases by 22.58%.”
“In the experiment, the proposed irrigation scheduling system is validated and compared with existing conventional irrigation systems to explore its performance.”
e1
“Implementation of this system leads to a decrease in water use by 59.61% and electrical energy consumption by 67.35%, while the crop yield increases by 22.58%.”
“Predicting crop yields remains a crucial challenge in agriculture”
e1
“The proposed stacked ensemble model achieves superior crop yield prediction performance, evidenced by a notable enhancement in accuracy and a significant decrease in RMSE, surpassing the predictive capabilities of traditional machine learning models”
“Predicting crop yields remains a crucial challenge in agriculture”
e1
“The proposed stacked ensemble model achieves superior crop yield prediction performance, evidenced by a notable enhancement in accuracy and a significant decrease in RMSE, surpassing the predictive capabilities of traditional machine learning models”
“Predicting crop yields remains a crucial challenge in agriculture”
e1
“The proposed stacked ensemble model achieves superior crop yield prediction performance, evidenced by a notable enhancement in accuracy and a significant decrease in RMSE, surpassing the predictive capabilities of traditional machine learning models”
“Experimental results from a maize UAV dataset comprising 12,074 annotated image patches demonstrate that SymbioMamba achieves 89.4% mAP@0.5 and an R2 of 0.915”
e1
“Compared to the industry-standard YOLOv11, the framework improves mAP@0.5:0.95 by 2.4% while reducing the parameter count to 6.2 M—a 50% decrease relative to monolithic state-space baselines”
“Experimental results from a maize UAV dataset comprising 12,074 annotated image patches demonstrate that SymbioMamba achieves 89.4% mAP@0.5 and an R2 of 0.915”
e1
“Compared to the industry-standard YOLOv11, the framework improves mAP@0.5:0.95 by 2.4% while reducing the parameter count to 6.2 M—a 50% decrease relative to monolithic state-space baselines”
“Experimental results from a maize UAV dataset comprising 12,074 annotated image patches demonstrate that SymbioMamba achieves 89.4% mAP@0.5 and an R2 of 0.915”
e1
“Compared to the industry-standard YOLOv11, the framework improves mAP@0.5:0.95 by 2.4% while reducing the parameter count to 6.2 M—a 50% decrease relative to monolithic state-space baselines”
“Maize ( Zea mays L.) is the most commonly used substrate for methane production through anaerobic fermentation and is gaining further importance in Germany”
e2
“Chemical composition models were always inferior to NIR spectroscopy models and showed a strong decrease in performance to predict MFY with ongoing fermentation time”
“Maize ( Zea mays L.) is the most commonly used substrate for methane production through anaerobic fermentation and is gaining further importance in Germany”
e2
“Chemical composition models were always inferior to NIR spectroscopy models and showed a strong decrease in performance to predict MFY with ongoing fermentation time”
each 1m2 area was sensed and treated independentlye0
Verbatim
e0
“This method of determining in-season fertilizer need has been shown to decrease large area N rates while also increasing wheat grain yields when each 1m2 area was sensed and treated independently.”
“This method of determining in-season fertilizer need has been shown to decrease large area N rates while also increasing wheat grain yields when each 1m2 area was sensed and treated independently.”
Lexical search (BM25). All words must match; if nothing does, any word. Organism names also match every spelling of the same organism. No semantic search yet.
Phaseolus vulgarist0
Vitis
Striga hermonthica
Zea mays
Zea mays subsp. mays
Triticum aestivum