diff --git a/r_app/CI_report_dashboard_planet.Rmd b/r_app/CI_report_dashboard_planet.Rmd
index b6aa7d9..21fe4f1 100644
--- a/r_app/CI_report_dashboard_planet.Rmd
+++ b/r_app/CI_report_dashboard_planet.Rmd
@@ -274,104 +274,4 @@ print(" PRINT")
   #  cat("\n")
   })
 }
-```
-
-# Yield prediction
-The below table shows estimates of the biomass if you would harvest them now.
-
-```{r message=FALSE, warning=FALSE, include=FALSE}
-
-#CI_quadrant <-  readRDS(here(cumulative_CI_vals_dir,"All_pivots_Cumulative_CI_quadrant_year_v2.rds"))
-CI_quadrant <-  readRDS(here(cumulative_CI_vals_dir,"All_pivots_Cumulative_CI_quadrant_year_v2.rds")) %>%
-group_by(model) %>%       # Group by model or other relevant columns
-fill(field, sub_field, .direction = "downup") %>% # Fill down then up within each group
-ungroup()
-if (all(is.na(CI_quadrant$tonnage_ha))) {
-  log_message("Lacking historic harvest data, please provide for yield prediction calculation")
-  knitr::knit_exit()  # Exit the chunk if tonnage_ha is empty
-}
-
-harvesting_data <- harvesting_data %>% rename(season = year)
-
-CI_and_yield <- left_join(CI_quadrant , harvesting_data, by = c("field", "sub_field", "season")) %>% #filter(!is.na(tonnage_ha)) %>%
-  group_by(sub_field, season) %>% slice(which.max(DOY)) %>%
-  dplyr::select(field, sub_field, tonnage_ha, cumulative_CI, DOY, season, sub_area) %>%
-  mutate(CI_per_day = cumulative_CI/ DOY)
-
-predictors <- c(  "cumulative_CI" , "DOY" ,"CI_per_day"  )
-response <- "tonnage_ha"
-# CI_and_yield_test <- as.data.frame(CI_and_yield_test)
-CI_and_yield_test <- CI_and_yield %>% as.data.frame() %>% filter(!is.na(tonnage_ha))
-CI_and_yield_validation <- CI_and_yield_test
-prediction_yields <- CI_and_yield %>% as.data.frame() %>% filter(is.na(tonnage_ha))
-
-ctrl <- trainControl(method="cv",
-                     savePredictions = TRUE,
-                     allowParallel= TRUE,
-                     number = 5,
-                     verboseIter = TRUE)
-
-set.seed(202)
-model_ffs_rf <- ffs( CI_and_yield_test[,predictors],
-                     CI_and_yield_test[,response],
-                     method="rf" ,
-                     trControl=ctrl,
-                     importance=TRUE,
-                     withinSE = TRUE,
-                     tuneLength = 5,
-                     na.rm = TRUE
-)
-
-
-# Function to prepare predictions
-prepare_predictions <- function(predictions, newdata) {
-  return(predictions %>%
-           as.data.frame() %>%
-           rename(predicted_Tcha = ".") %>%
-           mutate(sub_field = newdata$sub_field,
-                  field = newdata$field,
-                  Age_days = newdata$DOY,
-                  total_CI = round(newdata$cumulative_CI, 0),
-                  predicted_Tcha = round(predicted_Tcha, 0),
-                  season = newdata$season) %>%
-           dplyr::select(field, sub_field, Age_days, total_CI, predicted_Tcha, season) %>%
-           left_join(., newdata, by = c("field", "sub_field", "season")))
-}
-# Predict yields for the validation dataset
-pred_ffs_rf <- prepare_predictions(predict(model_ffs_rf, newdata = CI_and_yield_validation), CI_and_yield_validation)
-
-# Predict yields for the current season
-pred_rf_current_season <- prepare_predictions(predict(model_ffs_rf, newdata = prediction_yields), prediction_yields) %>%
-  filter(Age_days > 300) %>%
-  mutate(CI_per_day = round(total_CI / Age_days, 1))
-```
-
-```{r yield_plaatjes, echo=FALSE }
-if (all(is.na(CI_quadrant$tonnage_ha))) {
-  log_message("Lacking historic harvest data, please provide for yield prediction calculation")
-  knitr::knit_exit()  # Exit the chunk if tonnage_ha is empty
-}
-ggplot(pred_ffs_rf, aes(y = predicted_Tcha, x = tonnage_ha)) +
-  geom_point(size = 2, alpha = 0.6) +  # Adjust point size and transparency
-  geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "red") +  # Reference line
-  scale_x_continuous(limits = c(0, 200)) +
-  scale_y_continuous(limits = c(0, 200)) +
-  labs(title = "Model Performance: \nPredicted vs Actual Tonnage/ha",
-       x = "Actual tonnage/ha (Tcha)", 
-       y = "Predicted tonnage/ha (Tcha)") +
-  theme_minimal() 
-
-ggplot(pred_rf_current_season, aes(x = Age_days, y = predicted_Tcha)) +
-  geom_point(size = 2, alpha = 0.6) +  # Adjust point size and transparency
-  labs(title = "Predicted Yields for Fields Over 300 Days \nOld Yet to Be Harvested",
-       x = "Age (days)", 
-       y = "Predicted tonnage/ha (Tcha)") +
- # facet_wrap(~sub_area) +
-  scale_y_continuous(limits = c(0, 200)) +  # Optional: Set limits for y-axis
-  theme_minimal() 
-
-knitr::kable(pred_rf_current_season, 
-             digits = 0,
-             caption = "Predicted Tonnage/ha for Fields Over 300 Days Old")
-```
-
+```
\ No newline at end of file