2019 Survey, Soil Mineral Content Relationships

soilMineralVars <- dataframe %>%
  select( contains( "soil10.loi.xrf" ),contains( "soil20.loi.xrf" ) ) %>%
  select( - contains("orig") ) %>%
  colnames

studiedVariables <- c( "crop",
                      "climateRegion",
                      "variety",
                      "cropColor",
                      "brix",
                      "sample_source"
                      )

studiedVariables <- c( studiedVariables, soilMineralVars )

dataframe <- dataframe %>%
  mutate_at( vars( !!soilMineralVars ), as.double )


generalMediansTable <- tibble()

soilTables <- function( variable, factors = factorsList, data = dataframe, imgBaseDir="./graphics/soil/", reportToGenTable = TRUE ) {
  output <- list()

  filtArray <- tribble(
    ~ variable, ~ value,
    ## "crop", selCrop
    )

  print("explained var")
  print( variable )

  cropDF <- data %>%
    select( !!studiedVariables,
           !!factors,
           sourceUID
           )

  output$df <- cropDF

  freqsTable90 <- frequencyDelta( explainedVar = variable,
                                 data = cropDF,
                                 filteringArray = filtArray,
                                 explicativeFactors = factors)

    output$table90 <- freqsTable90

  freqsTable75 <- frequencyDelta( explainedVar = variable,
                                 data = cropDF,
                                 filteringArray = filtArray,
                                 explicativeFactors = factors,
                                 rangeMin = 0.75)

  output$table75 <- freqsTable75

  freqsTable15 <- frequencyDelta( explainedVar = variable,
                                 data = cropDF,
                                 filteringArray = filtArray,
                                 explicativeFactors = factors,
                                 rangeMin = 0,
                                 rangeMax = 0.15
                                 )

  output$table15 <- freqsTable15

  output$mediansPractices <- pmap_df( enframe( farmPracticesCols, name = NULL ), ~ positionStatisticsByLogicColumn(
                                                                                  data = cropDF,
                                                                                  column = .x,
                                                                                  explained = variable,
                                                                                  center = "farm_practices.none"
                                                                                )
                                     ) %>% filter( N != 0 )

  output$mediansAmendments <- pmap_df( enframe( amendmentsCols, name = NULL ), ~ positionStatisticsByLogicColumn(
                                                                                data = cropDF,
                                                                                column = .x,
                                                                                explained = variable,
                                                                                center = "amendments.none"
                                                                              )
                                      ) %>% filter( N != 0 )

  if ( reportToGenTable ) {

    medPract <- output$mediansPractices %>% mutate( explained = variable,
                                                   ## crop =selCrop,
                                                   family = "practices"
                                                   )
    medAmend <- output$mediansAmendments %>% mutate( explained = variable,
                                                    ## crop =selCrop,
                                                    family = "amendments"
                                                    )
    generalTable <- bind_rows( generalMediansTable, medPract, medAmend )
    output$generalTable <- generalTable
  }

  return( output )
}

## plan( multisession, workers=20 )
## tablesObjectList <- future_map( soilMineralVars, ~ soilTables( variable = .x ) )

## write_rds( tablesObjectList, path = "../bigFiles/tablesObjectListSoilMinerals.Rds" )

tablesObjectList <- read_rds( "../bigFiles/tablesObjectListSoilMinerals.Rds" )

generalMediansTable <- tibble()
for ( entry in tablesObjectList ) {
  generalMediansTable <- bind_rows( generalMediansTable, entry$generalTable )
  ## speechTable <- bind_rows( speechTable, entry$speechTable )
}

generalMediansTable <- generalMediansTable %>% unique

synthesisTable <- generalMediansTable %>%
  filter( isRelevant == TRUE & !is.na( isRelevant ) ) %>%
  select( factor, family, explained, N, p.value, intervalMin, intervalMax ) %>%
  pivot_wider( names_from = c( explained ), values_from = c( N, p.value, intervalMin, intervalMax ) ) %>%
  mutate( factor = str_replace( string = factor, pattern = "(amendments.)|(farm_practices.)", replacement = "" ) )

amendmentsSynthesis <- synthesisTable %>% filter( family == "amendments" )
practicesSynthesis <- synthesisTable %>% filter( family == "practices" )

write_rds( x = amendmentsSynthesis, path=".soilAmendmentsSynthesisTable.Rds" )
write_csv( x = amendmentsSynthesis, path=".soilAmendmentsSynthesisTable.csv" )
write_rds( x = practicesSynthesis, path=".soilPracticesSynthesisTable.Rds" )
write_csv( x = practicesSynthesis, path=".soilPracticesSynthesisTable.csv" )

referenceValues <- generalMediansTable %>%
  filter( str_detect( string = factor, pattern="none" ) ) %>%
  select( factor, "HL Estimate", explained, family ) %>%
  mutate( reference = .data[["HL Estimate"]] ) %>%
  select( explained, family, reference )

generalMediansTable <- left_join( x= generalMediansTable, y = referenceValues, by=c( "explained", "family") )

relevantsTable <- generalMediansTable %>%
  filter( observedUIDs >= 3 ) %>%
  mutate( shiftPercentage = .data[['HL Median Shift Estimate']] / reference,
         shiftPercentage = round( shiftPercentage * 100, digits = 2 )
         ## shiftPercentage = round( shiftPercentage * 100, digits = 2 ) %>% str_c( ., "%", sep ="" )
         ) %>%
  select( factor, explained, shiftPercentage, family ) %>%
  pivot_wider( names_from = explained, values_from = c( shiftPercentage ) ) %>%
  mutate( factor = str_replace( string = factor, pattern = "(amendments.)|(farm_practices.)", replacement = "" ) ) %>%
  select( factor, family, contains("10"), contains("20") )

sampleSizesTable <- generalMediansTable %>%
  filter( observedUIDs >= 3 ) %>%
  select( factor, explained, family, N ) %>%
  pivot_wider( names_from = c( explained ), values_from = c( N ) ) %>%
  mutate( factor = str_replace( string = factor, pattern = "(amendments.)|(farm_practices.)", replacement = "" ) ) %>%
  select( factor, family, contains("10"), contains("20") )

pValuesTable <- generalMediansTable %>%
  filter( observedUIDs >= 3 ) %>%
  mutate( p.value = ifelse( p.value > 0.1 & isRelevant == TRUE, 0.09, p.value  ) ) %>%
  select( factor, explained, family, p.value ) %>%
  pivot_wider( names_from = c( explained ), values_from = c( p.value ) ) %>%
  mutate( factor = str_replace( string = factor, pattern = "(amendments.)|(farm_practices.)", replacement = "" ) ) %>%
  select( factor, family, contains("10"), contains("20") )


relevants <- relevantsTable

pValueAlphaScale <- function( pValue ) {
  pValue <- 1 - pValue
  if ( pValue >= 0.95 ) { return(1) }
  else if ( pValue > 0.90 ) { return(0.70) }
  else if ( pValue > 0.8 ) { return(0.60) }
  else if ( pValue > 0.5 ) { return(0.4) }
  else if ( pValue < 0.1 ) { return(0.1) }
  else { return(0.3) }
}

assignBackgroundNonVec <- function( value, pValue ) {
  if ( is.na(value) | is.na(pValue) | typeof( value ) != "double" ) { return("none") }
  pValue <- pValueAlphaScale( pValue )
  if ( value >= 0 ) { return( paste0( "rgba(1,121,11,", pValue ,")" ) ) }
  else if ( value < 0 ) { return( paste0( "rgba(255,0,0,", pValue ,")" ) ) }
}

assignBackground <- function( vecsValue, vecsPValue ) {
  map2_chr( vecsValue, vecsPValue, assignBackgroundNonVec )
}

assignColorNonVec <- function( value ) {
  if ( is.na(value) | typeof( value ) != "double" ) { return("black") }
  else if ( value == 0 ) { return("white") }
  else if ( value > 0 ) { return("white") }
  else if ( value < 0 ) { return("white") }
}

assignColor <- function( vecsValue ) {
  vecsValue %>% map_chr( assignColorNonVec )
}

formatCols <- relevants %>% select( -"factor", -"family" ) %>%
  colnames

relevantSpecAmendments <- relevants %>% filter( family == "amendments" ) %>% select( - "family" )

sampleSizesAmendments <- sampleSizesTable %>% filter(  family == "amendments") %>% select( - "family" )

pValuesAmendments <- pValuesTable %>% filter(  family == "amendments") %>% select( - "family" )

for ( col in formatCols ) {
  relevantSpecAmendments[[col]] <- cell_spec(
    relevantSpecAmendments[[col]],
    background = assignBackground( relevantSpecAmendments[[col]], pValuesAmendments[[col]] ),
    tooltip = sampleSizesAmendments[[col]] %>% str_c( "Sample size: ", ., sep = "" ) ,
    color = assignColor( relevantSpecAmendments[[col]] ),
    bold = TRUE
  )
}

fullColorAmendments <- relevantSpecAmendments %>%
  filter( factor != "none" ) %>%
  mutate( factor = str_replace_all( factor, pattern = "_", replacement = " " ) %>% str_to_title( . ) ) %>%
  rename_all( ~ str_replace_all( ., pattern = "_", replacement = " " ) %>% str_to_title( . ) ) %>%
  kable( escape = FALSE, caption = "Percent of Variation over Median Value" ) %>%
  kable_styling( c( "hover", "striped" ),
                full_width = FALSE,
                fixed_thead = TRUE
                ) %>%
  column_spec( 1, bold = TRUE, border_right = TRUE )

write_rds( fullColorAmendments, path="./soilAmendmentsShiftTable.Rds" )

relevantSpecPractices <- relevants %>% filter( family == "practices" ) %>% select( - "family" )

sampleSizesPractices <- sampleSizesTable %>% filter(  family == "practices") %>% select( - "family" )

pValuesPractices <- pValuesTable %>% filter(  family == "practices") %>% select( - "family" )

for ( col in formatCols ) {
  relevantSpecPractices[[col]] <- cell_spec(
    relevantSpecPractices[[col]],
    background = assignBackground( relevantSpecPractices[[col]], pValuesPractices[[col]] ),
    tooltip = sampleSizesPractices[[col]] %>% str_c( "Sample size: ", ., sep = "" ) ,
    color = assignColor( relevantSpecPractices[[col]] ),
    bold = TRUE
  )
}

fullColorPractices <- relevantSpecPractices %>%
  filter( factor != "none" ) %>%
  mutate( factor = str_replace_all( factor, pattern = "_", replacement = " " ) %>% str_to_title( . ) ) %>%
  rename_all( ~ str_replace_all( ., pattern = "_", replacement = " " ) %>% str_to_title( . ) ) %>%
  kable( escape = FALSE, caption = "Percent of Variation over Median Value" ) %>%
  kable_styling( c( "hover", "striped" ),
                full_width = FALSE,
                fixed_thead = TRUE
                ) %>%
  column_spec( 1, bold = TRUE, border_right = TRUE )

write_rds( fullColorPractices, path="./soilPracticesShiftTable.Rds" )

fullColorPractices

Percent of Variation over Median Value

Factor	Soil10.Loi.xrf.na	Soil10.Loi.xrf.mg	Soil10.Loi.xrf.al	Soil10.Loi.xrf.si	Soil10.Loi.xrf.p	Soil10.Loi.xrf.s	Soil10.Loi.xrf.k	Soil10.Loi.xrf.ca	Soil10.Loi.xrf.ti	Soil10.Loi.xrf.v	Soil10.Loi.xrf.cr	Soil10.Loi.xrf.mn	Soil10.Loi.xrf.fe	Soil10.Loi.xrf.co	Soil10.Loi.xrf.ni	Soil10.Loi.xrf.cu	Soil10.Loi.xrf.zn	Soil20.Loi.xrf.na	Soil20.Loi.xrf.mg	Soil20.Loi.xrf.al	Soil20.Loi.xrf.si	Soil20.Loi.xrf.p	Soil20.Loi.xrf.s	Soil20.Loi.xrf.k	Soil20.Loi.xrf.ca	Soil20.Loi.xrf.ti	Soil20.Loi.xrf.v	Soil20.Loi.xrf.cr	Soil20.Loi.xrf.mn	Soil20.Loi.xrf.fe	Soil20.Loi.xrf.co	Soil20.Loi.xrf.ni	Soil20.Loi.xrf.cu	Soil20.Loi.xrf.zn
Certified Organic	0.03	0.04	-11.63	12.87	0.02	0	-16.22	-22.22	0.01	14.26	4.49	5.94	-11.32	4.52	50.13	9.07	-30.06	0.01	-0.01	-18	5.99	0.02	0	-16.66	0	-0.01	13.08	-0.3	-1.96	-16.95	-2.22	83.83	8.72	-24.32
Organic	-0.02	0.01	-13.95	1.17	0	3.33	-10.81	-22.22	0.02	6.12	-11.85	-8.62	-18.87	-2.58	43.41	7.36	-7.76	0.01	66.61	-12	0	-0.01	3.39	-11.11	-9.09	0.01	11.25	-10.81	-7.56	-20.34	-3.88	61.39	11.71	-11.96
Notill	0.04	66.63	-11.63	-2.34	0.01	6.67	-16.21	-14.82	0.01	9.16	-12.67	-6.32	-15.09	4.52	20.67	11.04	-16.42	0.02	66.63	-12	-7.63	-0.04	10.17	-16.67	-9.09	0.01	20.41	-12.43	-7.15	-13.56	2.77	30.36	18.68	-21.2
Nospray	40	66.6	13.95	3.51	0.02	6.67	-16.22	-37.03	33.34	29.05	-16.45	6.58	-11.32	16.77	18.09	5.36	8.21	39.97	133.25	10	-1.64	0.02	20.34	-16.67	-18.19	28.58	51.5	-8.99	14.44	-10.17	13.3	40.26	35.74	-19.51
Biodynamic	-0.01	0	-6.98	7.6	-0.02	3.34	-10.81	-29.63	-0.01	4.99	9.91	-3.3	-11.32	9.03	30.49	10.51	6.43	-0.02	66.59	-12	3.27	-0.03	6.78	-5.56	-9.08	-0.01	-1.32	10.31	-0.86	-13.56	1.66	24.42	6.6	29.39
Cover Crops	39.99	0.02	4.65	4.09	0.02	3.33	-21.63	-29.63	33.32	29.85	-16.55	6.08	-3.77	18.06	16.54	11.56	-23.41	39.98	66.65	0	-2.82	49.99	16.95	-27.78	-18.19	28.58	52.05	-13.34	11.41	-3.39	19.39	38.94	29.64	-34.2
Nonchlorine Water	0.04	66.65	-13.95	4.68	0.02	6.67	-16.22	-29.63	0.02	22.5	-11.85	-2.34	-18.87	2.58	46.51	23	-18.86	0.02	66.62	-14	3.27	-0.04	6.78	-16.66	-18.18	0.01	24.93	-14.76	-5.05	-20.34	0.55	79.87	17.31	-13.52
Irrigation	0.01	66.64	-18.6	3.51	-0.02	10	-16.21	-14.82	0.01	16.05	-9.3	-4.26	-26.41	-7.1	97.16	18	-20.97	0	66.62	-24	1.63	-0.04	6.78	-11.11	-18.18	0.01	26.6	-12.33	-11.23	-27.12	-11.08	171.62	16.81	-19.77

fullColorAmendments

Percent of Variation over Median Value

Factor	Soil10.Loi.xrf.na	Soil10.Loi.xrf.mg	Soil10.Loi.xrf.al	Soil10.Loi.xrf.si	Soil10.Loi.xrf.p	Soil10.Loi.xrf.s	Soil10.Loi.xrf.k	Soil10.Loi.xrf.ca	Soil10.Loi.xrf.ti	Soil10.Loi.xrf.v	Soil10.Loi.xrf.cr	Soil10.Loi.xrf.mn	Soil10.Loi.xrf.fe	Soil10.Loi.xrf.co	Soil10.Loi.xrf.ni	Soil10.Loi.xrf.cu	Soil10.Loi.xrf.zn	Soil20.Loi.xrf.na	Soil20.Loi.xrf.mg	Soil20.Loi.xrf.al	Soil20.Loi.xrf.si	Soil20.Loi.xrf.p	Soil20.Loi.xrf.s	Soil20.Loi.xrf.k	Soil20.Loi.xrf.ca	Soil20.Loi.xrf.ti	Soil20.Loi.xrf.v	Soil20.Loi.xrf.cr	Soil20.Loi.xrf.mn	Soil20.Loi.xrf.fe	Soil20.Loi.xrf.co	Soil20.Loi.xrf.ni	Soil20.Loi.xrf.cu	Soil20.Loi.xrf.zn
Alfalfa Meal	-0.01	49.98	-14.29	-10.61	-0.01	16.67	20	0	0	17.42	-3.16	4.33	8.33	9.29	-68.03	40.29	-32.06	0	0.02	-8.89	-7.8	-50	9.84	27.59	-9.09	-0.01	11.17	-8.21	-0.31	12	8.65	-94.97	27.04	-24.06
All Purpose	-0.01	0.01	4.76	-2.23	-49.95	13.33	20	8	-0.01	19.64	2.63	12.78	12.5	14.24	-98.77	25.18	3.5	0	0	0	-2.79	-50.02	16.39	20.69	18.18	-0.01	19.17	1.14	13.49	16	13.83	-91.89	25.72	7.8
Azomite	-39.99	0.03	-2.38	-8.38	0	10	13.33	48	-28.57	-3.32	1.43	6.61	8.33	3.72	-68.03	13.92	18.04	-0.02	-0.02	2.22	0.56	-49.99	3.28	13.79	27.28	-0.01	-4.61	5.51	10.98	12	3.22	-108.56	-0.68	55.01
Blood Meal	-0.02	150.01	-9.53	5.03	-49.97	46.67	13.33	-24	0	86.75	7.16	30.87	-8.33	20.43	-3.28	101.26	-76.3	-39.97	99.99	-15.55	4.46	-50	32.79	13.79	-18.19	-0.02	52.2	3.64	20.3	-8	8.65	10.36	71.87	-60.6
Boron	0.02	50.05	-50	-6.7	-0.02	26.66	0	-24	57.14	78.7	-58.77	-14.01	-45.84	-21.05	104.51	56.86	-58.1	40.01	50.01	-33.33	9.47	-0.01	22.95	-6.9	-54.55	74.99	99.76	-56.1	-6.48	-44	-6.92	172.52	52.16	-84.53
Calcium	-0.01	50.01	-42.86	24.58	-49.95	10	-46.67	-8.01	0	10.3	10.95	-5.67	-50	-27.86	136.7	38.97	-79.26	0.02	99.96	-33.33	31.2	-50.02	19.67	-48.28	-9.1	-0.01	42.83	19.01	14.75	-36	-8.65	214.41	53	-96.42
Compost	0.02	-50.01	9.52	12.85	49.99	-30	0	-8	-0.03	-38.68	-3.9	-16.47	-16.67	-26.01	45.9	-58.45	60.5	0	-99.95	8.89	11.14	50	-29.51	0	-0.01	0	-37.46	-9.35	-18.94	-13.53	-24.21	17.57	-59.13	100.39
Compost Tea	-0.01	50.01	-42.86	24.58	-49.95	10	-46.67	-8.01	0	10.3	10.95	-5.67	-50	-27.86	136.7	38.97	-79.26	0.02	99.96	-33.33	31.2	-50.02	19.67	-48.28	-9.1	-0.01	42.83	19.01	14.75	-36	-8.65	214.41	53	-96.42
Compost With Chicken Manure And Peatmoss	-40	-0.03	-11.91	-12.29	-49.99	0	20	24	-28.57	-25.11	3.37	-9.38	8.33	-6.19	-37.29	16.17	-14.55	-39.97	0.01	6.67	6.13	-50.02	3.28	27.59	27.27	-24.99	-2.67	5.3	16.88	16	2.31	-110.36	11.06	15.6
Copper Sulfate	-0.01	50	-42.86	-2.23	-49.96	13.33	13.34	-8	28.57	40.35	-51.08	-17.45	-33.33	-21.67	78.69	22.8	-45.51	0.01	49.96	-40	8.36	-49.98	13.11	13.8	-18.18	25.01	53.26	-50.39	-12.04	-36	-17.29	120.27	28.98	-39.47
Donald Stoner Compost	39.98	300.03	-9.52	-1.68	-0.01	113.33	60	-56	57.14	284.71	-27.17	58.74	-4.17	60.06	53.69	230.86	-193.76	39.99	299.92	8.89	20.61	-0.01	95.08	68.97	-54.55	50	235.83	-31.06	55.03	0	58.79	119.37	190.26	-188.17
Down To Earth	0	-0.01	11.91	1.68	-49.96	6.67	20	23.99	0	7.09	10.29	21.32	20.83	13	-130.74	14.05	16.04	-0.03	0.01	6.67	1.67	-49.97	9.83	20.69	27.27	-25	4.99	5.51	18.86	20	10.95	-124.32	15.63	22.63
Feather Meal	0.01	50.02	-26.19	1.68	-0.01	20	-6.67	-24	28.6	66.05	-37.91	-2.91	-37.5	-11.76	93.03	44.53	-50	39.98	49.97	-4.45	5.01	-50	13.11	-6.9	-45.46	49.99	51.94	-35.32	-7.52	-24	1.73	84.23	28	-48.11
Fish Emulsion	-39.99	150	-47.62	-29.05	-49.98	80	33.34	-23.99	0	91.72	-20.33	26.09	-29.17	3.1	32.79	146.06	-122.55	-0.02	50.03	-42.22	-2.23	-50.05	29.51	41.38	-45.46	0.01	57.39	-21.3	-14.53	-32	-2.88	187.84	62.38	-108.58
Fish Seaweed	-0.01	50.01	-42.86	24.58	-49.95	10	-46.67	-8.01	0	10.3	10.95	-5.67	-50	-27.86	136.7	38.97	-79.26	0.02	99.96	-33.33	31.2	-50.02	19.67	-48.28	-9.1	-0.01	42.83	19.01	14.75	-36	-8.65	214.41	53	-96.42
Fishbone Meal	-0.02	0.01	21.43	2.79	-0.01	6.67	20	32	-0.01	13.17	9.14	25.66	29.17	18.58	-126.1	7.6	32.06	-0.01	0.02	8.89	5.01	-50.01	9.84	20.69	36.36	-0.01	11.83	8.62	28.17	24	14.41	-134.68	10.58	41.09
Green Sand	-0.02	150.02	-52.38	-7.82	-49.94	70	-13.33	-24	0	115.78	-1.05	16.86	-33.33	9.29	68.44	143.41	-125.71	-39.97	99.96	-57.78	-6.68	-50.05	32.79	-6.9	-18.18	-0.01	40.37	-8.21	-15.04	-32	-9.22	87.39	66.71	-78.41
Gypsum	0.03	49.99	0	6.15	-49.97	13.34	26.67	-24	-0.02	29.59	7.06	8.54	16.67	19.19	33.61	21.47	-12.25	0.01	49.98	11.11	14.48	-49.98	13.12	27.59	-27.27	-0.02	28.58	12.57	15.91	20	21.9	22.97	21.39	-15.34
Kelp Meal	-0.01	100.01	-35.71	8.38	-49.95	46.67	-6.66	-16	0.01	71.71	1.9	18.94	-20.83	9.29	107.38	86.55	-74.62	-0.01	99.97	-35.55	11.14	-50.01	29.51	0	-18.19	-0.01	45.79	2.29	6.11	-20	2.88	129.73	56.97	-51.5
Manganese Sulfate	39.98	0.02	-2.38	29.61	0	0	6.67	0	-0.02	12.44	10.01	20.85	0	6.81	180.74	-39.63	49.56	-0.01	0.02	-20	17.83	-50.01	-6.56	0	9.09	-0.01	-20.65	10.18	5.59	-12	-12.68	112.16	-32.21	98.31
Nutrilive Vitality	-0.01	50.01	-42.86	24.58	-49.95	10	-46.67	-8.01	0	10.3	10.95	-5.67	-50	-27.86	136.7	38.97	-79.26	0.02	99.96	-33.33	31.2	-50.02	19.67	-48.28	-9.1	-0.01	42.83	19.01	14.75	-36	-8.65	214.41	53	-96.42
Nutrisprout	40	100.03	-19.05	35.75	-0.03	30	0.01	-56	28.58	109.9	-7.69	26.67	-16.66	22.91	302.46	72.91	-10.39	40	99.97	-17.78	38.44	-0.01	26.23	6.89	-54.55	25.02	82.77	-10.6	23.98	-20	15.56	357.21	54.81	-2.86
Potash	-40	-0.02	-28.57	-22.9	0.02	-13.33	0	24	-28.56	-35.17	19.69	-27.15	-20.83	-24.77	4.92	-1.54	-15.43	-40.01	-49.98	-22.22	-15.04	0.03	-9.83	0	36.37	-25	-27.81	18.16	-22.79	-20	-23.63	77.03	-1.32	-13.91
Potassium Sulfate	0.02	0.01	-2.38	19.55	-49.96	3.33	20	-32.01	28.57	26.04	-10.43	-2.98	-12.5	-2.48	89.75	3.98	-12.8	0.01	0	2.22	18.38	-50.01	0	13.79	-36.37	24.99	19.95	-8.73	-0.62	-8	2.88	86.94	-1.92	1.43
Pro Grow	79.99	99.98	-14.29	-21.23	299.87	30	0	72	85.75	121.85	3.48	27.54	45.83	71.83	-130.74	61.59	-56.51	40	49.98	-28.89	-32.31	150.06	19.67	-6.9	72.72	74.98	74.63	4.57	6.47	40	53.6	-121.17	51.08	-62.42
Proboost	-39.98	-0.02	-16.67	-12.85	49.97	-10	0	16	-0.01	-24.31	21.17	-16.24	-12.5	-19.2	24.18	-2.78	-12.58	-39.98	-49.99	-15.55	-0.56	50.01	-9.83	0	36.36	-24.99	-21.85	25.39	-11.81	-16	-16.79	81.98	-3.86	-1.43
Progro	0.02	-0.02	0	3.91	0	-10	6.66	0	-0.02	-3.96	28.52	-4.12	0	-0.62	78.28	-6.63	2.49	39.98	-49.97	17.78	22.84	50.02	-3.28	6.9	18.18	-0.01	12.82	41.77	24.42	12	10.95	178.38	-12.85	47.96
Revita	-0.02	150.02	-52.38	-7.82	-49.94	70	-13.33	-24	0	115.78	-1.05	16.86	-33.33	9.29	68.44	143.41	-125.71	-39.97	99.96	-57.78	-6.68	-50.05	32.79	-6.9	-18.18	-0.01	40.37	-8.21	-15.04	-32	-9.22	87.39	66.71	-78.41
Seasalt	0.01	-99.98	11.9	34.08	-49.96	-26.67	26.66	-24	-0.01	-24.77	25.28	-3.66	-4.17	-14.86	142.21	-49.04	51.53	0.02	-100	2.22	34.54	-50.01	-29.51	27.58	-9.09	-0.01	-30.26	18.53	-5.23	-8	-17.87	192.98	-53.49	81.53
Sodium Molybdate	0	0	-2.38	30.17	-0.03	0	6.67	-8	0	10.86	14.32	16.13	0	3.1	157.79	-31.15	37.64	-0.02	0.02	-20	20.06	-50.01	0	0	9.09	-0.01	-10.34	14.55	13.04	-8	-6.34	125.22	-21.15	78.02
Sodium Selenate	0	0	-2.38	30.17	-0.03	0	6.67	-8	0	10.86	14.32	16.13	0	3.1	157.79	-31.15	37.64	-0.02	0.02	-20	20.06	-50.01	0	0	9.09	-0.01	-10.34	14.55	13.04	-8	-6.34	125.22	-21.15	78.02
Solubor	0.02	0.01	-2.38	29.61	-49.97	0	6.67	0	-0.01	9.9	13.48	17.94	0	5.57	168.44	-36.85	51.53	-0.01	49.98	-13.33	23.4	-50	6.56	6.89	0	-0.01	5.12	15.69	21.38	-4	1.73	148.65	-9.13	57.09
Sulfur	39.98	49.99	-7.14	19.55	0.01	10	6.66	23.99	0.01	44.64	7.9	23.4	16.66	25.39	100	-8.08	19.58	0	0.01	-22.22	-4.46