Fixed typo

_quarto.yml

@@ -54,48 +54,49 @@ book:
         - basic_concepts/sql_database.ipynb
 
 
-    - part: "EXERCISES"
+    - part: "GENERAL EXERCISES"
       chapters:
         - exercises/meteogram.ipynb
         - exercises/group_least_variance.ipynb
-        - exercises/random_plots.ipynb
-        - exercises/curve_number.ipynb        
+        - exercises/random_plots.ipynb       
         - exercises/mixing_model.ipynb
         - exercises/mass_volume_relationships.ipynb
         - exercises/soil_textural_class.ipynb
         - exercises/distribution_daily_precipitation.ipynb
         - exercises/high_resolution_rainfall_events.ipynb
         - exercises/first_and_last_frost.ipynb
-        - exercises/evapotranspiration.ipynb
-        - exercises/thermal_time.ipynb
         - exercises/central_dogma.ipynb
         - exercises/error_metrics.ipynb
         - exercises/request_web_data.ipynb
+        - exercises/soil_water_storage.ipynb
+        - exercises/plant_available_water.ipynb
+        - exercises/photoperiod.ipynb
+
+    - part: "MODELS"
+      chapters:
+        - exercises/curve_number.ipynb 
+        - exercises/air_temperature_model.ipynb
+        - exercises/soil_temperature_model.ipynb
+        - exercises/thermal_time.ipynb
+        - exercises/evapotranspiration.ipynb
+        - exercises/photoperiod.ipynb
+        - exercises/wheat_potential_yield.ipynb
+        - exercises/autoregressive_model.ipynb
+
+    - part: "CURVE FITTING"
+      chapters:
         - exercises/anscombe_quartet.ipynb 
         - exercises/neutron_probe_calibration.ipynb 
         - exercises/sorghum_yields.ipynb         
         - exercises/proctor_test.ipynb
         - exercises/optimal_nitrogen_rate.ipynb
         - exercises/maximum_return_to_nitrogen.ipynb
-        - exercises/air_temperature_model.ipynb
-        - exercises/frontier_function.ipynb
-        - exercises/atmospheric_carbon_dioxide.ipynb
-        - exercises/autoregressive_model.ipynb
-        - exercises/soil_water_storage.ipynb
-        - exercises/plant_available_water.ipynb
-        - exercises/photoperiod.ipynb
         - exercises/solar_radiation.ipynb
-        - exercises/wheat_potential_yield.ipynb
-        - exercises/soil_temperature_model.ipynb
         - exercises/soil_water_retention_curve.ipynb
-        - exercises/count_seeds.ipynb
-        - exercises/canopy_cover.ipynb
-        - exercises/soil_moisture_monitoring_stations.ipynb
-        - exercises/field_random_samples.ipynb
-        - exercises/yield_monitor_clean.ipynb
-        - exercises/yield_monitor_zones.ipynb
-        - exercises/largest_empty_circle.ipynb
-
+        - exercises/frontier_function.ipynb
+        - exercises/atmospheric_carbon_dioxide.ipynb
+        - exercises/drydowns.ipynb
+
     - part: "NUMERICAL MODELS"
       chapters:
         - exercises/newton_law_cooling.ipynb
@@ -108,10 +109,24 @@ book:
         - exercises/weed_population.ipynb
         - exercises/rainfall_generator.ipynb
 
+    - part: "IMAGE ANALYSIS"
+      chapters:
+        - exercises/count_seeds.ipynb
+        - exercises/canopy_cover.ipynb
+
+    - part: "MAPPING AND SPATIAL ANALYSIS"
+      chapters:
+        - exercises/soil_moisture_monitoring_stations.ipynb
+        - exercises/field_random_samples.ipynb
+        - exercises/yield_monitor_clean.ipynb
+        - exercises/yield_monitor_zones.ipynb
+        - exercises/largest_empty_circle.ipynb
+
     - part: "MACHINE LEARNING"
       chapters:
         - exercises/estimate_daily_vapor_pressure.ipynb
 
+
 
     - part: "GOOGLE EARTH ENGINE"
       chapters:

index.qmd

@@ -2,7 +2,7 @@
 
 Welcome to a journey at the intersection of programming, data science, and agronomy. This book presents hands-on coding exercises designed to address common tasks in crop and soil sciences. 
 
-Coding is an essential component of modern scientific research that enables more creative solutions, in-depth analyses, and ensures reproducibility of findings. This material is part of an introductory graduate level course offered to students in plant and soil sciences with little or no coding experience. Anyone with sufficient motivation, discipline, and interest in learning how to code and adopt reproducible research practices should (hopefully) find the content of this notes useful. The material is aimed at students that are transitioning from spreadsheets analysis to a programming environemnt and that first need to learn basic building blocks before tackling more complicated challenges. With most datasets in a tabular format, the material is easily accessible and inspectable using common spreadsheet software.
+Coding is an essential component of modern scientific research that enables more creative solutions, in-depth analyses, and ensures reproducibility of findings. This material is part of an introductory graduate level course offered to students in plant and soil sciences with little or no coding experience. Anyone with sufficient motivation, discipline, and interest in learning how to code and adopt reproducible research practices should (hopefully) find this content useful. The material is aimed at students that are transitioning from spreadsheets analysis to a programming environemnt and that first need to learn basic building blocks before tackling more complicated challenges. With most datasets in a tabular format, the material is easily accessible and inspectable using common spreadsheet software.
 
 I selected Python because of its accessibility (it's free), straightforward syntax, multi-purpose applications (data analysis, desktop applications, websites, games), widespread adoption in the scientific community, and a rich ecosystem of tools for reproducible research that makes the transition into the coding world a lot easier to beginners. The code presented here is complemented by live coding lectures and might not always reflect the most efficient or 'pythonic' methods. The goal is to present clear and explicit code to gradually familiarize students with the Python syntax, documentation resources, and improve the process of breaking down problems into a sequence of smaller logical steps to ultimately reach more advanced and elegant coding. This book strives for a balanced approach, blending task complexity with a judicious use of libraries. While libraries enhance reproducibility and benefit from extensive testing of the community, an overreliance on them can hinder beginners from truly grasping the underlying concepts and logic of programming.