vars

references/doi_10.1017_S002185961600099X.ris

@@ -0,0 +1,24 @@
+TY  - JOUR
+AB  - Sweetpotato breeding requires at least 5 years to obtain an advanced breeding clone for further testing with the goal of cultivar release. An accelerated breeding scheme (ABS) can be feasible if the genotype × year interaction is low. The objectives of the present study were to describe an ABS for sweetpotato and to investigate the efficiency of this breeding scheme for selecting high-yielding and well-adapted orange-fleshed sweetpotato (OFSP) cultivars with high β-carotene (BC) content. More than 198 500 seeds from two crossing blocks were germinated and rapidly multiplied for evaluation in observation trials at four breeding locations in Mozambique. Breeding clones with storage root yields above 10 t/ha were advanced to preliminary and advanced yield trials across four sites and for 3 years. As a result, 64 high-yielding OFSP breeding clones were selected and evaluated in four mega-environments following a randomized complete block design with three replicates at Angónia, Chókwè, Gurúè and Umbelúzi. Data from multi-environment trials were subjected to single site and combined analysis of variance as well as to stability analysis. The genotype × environment interaction was highly significant for storage root and vine yields, dry matter (DM) and BC content. Storage root yield and DM content for 15 OFSP breeding clones ranged from 14·9 to 27·1 t/ha and from 24·8 to 32·8%, respectively. BC content, iron and zinc ranged from 5·9 to 38·4, 1·6 to 2·1 and 1·1 to 1·5 mg/100 g dry weight, respectively. The OFSP breeding clones also met the culinary tastes required by local consumers in Mozambique. The proposed ABS seems to be an attractive scheme for genetic enhancement of sweetpotato.
+AU  - ANDRADE, M. I.
+AU  - RICARDO, J.
+AU  - NAICO, A.
+AU  - ALVARO, A.
+AU  - MAKUNDE, G. S.
+AU  - LOW, J.
+AU  - ORTIZ, R.
+AU  - GRÜNEBERG, W. J.
+DB  - Cambridge Core
+DO  - DOI: 10.1017/S002185961600099X
+DP  - Cambridge University Press
+ET  - 2016/12/05
+IS  - 6
+PB  - Cambridge University Press
+PY  - 2017
+SN  - 0021-8596
+SP  - 919-929
+T2  - The Journal of Agricultural Science
+TI  - Release of orange-fleshed sweetpotato (Ipomoea batatas [l.] Lam.) cultivars in Mozambique through an accelerated breeding scheme
+UR  - https://www.cambridge.org/core/product/CA3537823CEA037C602E80FCBD62D524
+VL  - 155
+ER  - 

scripts/intercrop/doi_10.7910_DVN_G4QLNP.R

@@ -24,7 +24,7 @@ cropping system (sole maize, sole pigeonpea, maize-pigeonpea, maize-gliricidia,
 		carob_date="2024-04-09"
 	)
 	
-	f1 <- ff[basename(ff) == " 006_siDom_droughtResistance.csv"]
+	f1 <- ff[basename(ff) == "006_siDom_droughtResistance.csv"]
 	r1 <- read.csv(f1)
 	f2 <- ff[basename(ff) == "007_siteCharacterization_droughtResistance.csv"]
 	r2 <- colMeans(read.csv(f2))

scripts/potato_trials/doi_10.21223_GZI7PD.R

@@ -13,7 +13,7 @@ carob_script <- function(path) {
 		data_institute = "CIP", 
 		carob_contributor="Cedric Ngakou", 
 		data_type="experiment", 
-		treatment_vars = "variety;longitude;latitude", 
+		treatment_vars = "variety", 
 		project=NA, 
 		carob_date="2023-12-09"
 	)

scripts/potato_trials/doi_10.21223_JKNWBC.R

@@ -17,26 +17,27 @@ carob_script <- function(path) {
 	   publication= NA,
 	   project=NA,
 	   data_type= "experiment",
-	   treatment_vars = "variety;longitude;latitude",
+	   treatment_vars = "variety",
 	   carob_contributor= "Cedric Ngakou",
 	   carob_date="2024-06-16"
 	)
 
 	r <- carobiner::read.excel(ff[basename(ff)=="PTYield2019-2020_Combinado_CIP395123.6_exp.xlsx"], sheet = "Fieldbook")
 
 	d <- data.frame(
-	   country="Peru",
-	   crop="potato",
-	   variety= r$INSTN,
-	   rep= as.integer(r$REP),
-	   yield=r$MTYNA*1000, # to kg/ha
-	   location=r$LOC,
-	   on_farm= TRUE,
-	   irrigated= FALSE,
-	   inoculated= FALSE,
-	   yield_part= "tubers",
-	   trial_id= "1",
-	   season="2019-2020"
+		country="Peru",
+		crop="potato",
+		variety= r$INSTN,
+		rep= as.integer(r$REP),
+		yield=r$MTYNA*1000, # to kg/ha
+		location=r$LOC,
+		on_farm= TRUE,
+		irrigated= FALSE,
+		inoculated= FALSE,
+		yield_part= "tubers",
+		trial_id= "1",
+		season="2019-2020",
+		is_survey = FALSE
 	)
 
 
@@ -58,4 +59,5 @@ carob_script <- function(path) {
 
 
 	carobiner::write_files(path, dset, d)
-}
+}
+

scripts/potato_trials/doi_10.21223_P3_FHUVF9.R

@@ -17,7 +17,7 @@ Samples were prepared and analysed for Fe and Zn by inductively coupled plasma-o
 		data_institute = "CIP",
 		carob_contributor="Cedric Ngakou",
 		data_type="experiment",
-		treatment_vars = "variety;longitude;latitude",
+		treatment_vars = "variety",
 		project=NA,
 		carob_date="2023-12-12"
 	)

scripts/potato_trials/doi_10.21223_P3_UTZBYL.R

@@ -15,7 +15,7 @@ carob_script <- function(path) {
 		data_institute = "CIP",
 		carob_contributor="Cedric Ngakou",
 		data_type="experiment",
-		treatment_vars = "variety;longitude;latitude",
+		treatment_vars = "variety",
 		project=NA,
 		carob_date="2023-10-30"
 	)

scripts/potato_trials/doi_10.21223_V7ZQD4.R

@@ -15,7 +15,7 @@ carob_script <- function(path) {
       data_institute = "CIP",
       carob_contributor="Cedric Ngakou",
       data_type="experiment",
-		treatment_vars = "variety;longitude;latitude",
+		treatment_vars = "variety",
       project=NA,
       carob_date="2024-02-26"
    )

scripts/potato_trials/doi_10.21223_WX2HIT.R

@@ -15,7 +15,7 @@ We use the randomized complete blocks (RCB) statistical design, with three repet
 		data_institute = "CIP",
 		carob_contributor="Cedric Ngakou",
 		data_type="experiment",
-		treatment_vars = "variety;longitude;latitude",
+		treatment_vars = "variety",
 		project=NA,
 		carob_date="2023-12-12"
 	)

scripts/rice_trials/doi_10.7910_DVN_OF7M9D.R

@@ -3,8 +3,7 @@
 
 carob_script <- function(path) {
 
-"In 2010, the Africa-wide Rice Breeding Task Force was launched by AfricaRice involving National Agricultural Research System (NARS) from about 30 countries. The objectives of the network are to evaluate the stability of traits incorporated in breeding processes and to identify varieties best fit to growth conditions in target regions and to markets. The Task Force also accumulates data on performance of new elite lines, thereby facilitating varietal release procedures. Furthermore, by exposing breeders from NARS and farmers to these elite lines during the testing phase, dissemination will be facilitated. The activities conducted by the Task Force consists of a series of consecutive trials. Promising breeding lines developed by AfricaRice or by national and international partners, such as IRRI, CIAT and the NARS are nominated for evaluation in one or several rice cultivation environments: rainfed lowland, irrigated lowland, rainfed upland, high elevation and mangrove. All nominated lines should be fixed and accompanied by supporting data on traits incorporated during the breeding process and with information on yield performance. These characteristics are checked at AfricaRice before incorporation into the network. The first phase (MET, Multi-Environment Testing) consists of an initial evaluation of about 100 lines selected from the nominated lines. Each national partner evaluates these lines at sites in their country. Such sites may be at an experimental station under optimal management to evaluate yield potential, or may be ‘hot spots’ to check the performance of the nominations in a stressed growth environment. Trials are replicated three times and include at least a common and a local check. The second phase (PET,Participatory Evaluation Trial) serves to evaluate and confirm the performance of the selected lines. These lines are cultivated using the same experimental design with 3 replications. An important feature of PET is that farmer and other stakeholders such as miller and traders are invited to participate in varietal selection and their opinion on the performance of all entries (i.e. participatory varietal selection, PVS) collected. Based on the data collected, observations by the breeders and the opinion of stakeholder groups, NARS partners select up to 10 lines. Further, NARS evaluated these lines in at least three sites per country and during one or more growing seasons, depending on varietal release requirements. All stakeholders are again invited to get acquainted with the new lines and voice their opinion to help select lines for further advancement. Among the 10 lines, farmers are invited to select three lines and cultivate these in their own fields, together with a common check and their own variety.
-"
+"In 2010, the Africa-wide Rice Breeding Task Force was launched by AfricaRice involving National Agricultural Research System (NARS) from about 30 countries. The objectives of the network are to evaluate the stability of traits incorporated in breeding processes and to identify varieties best fit to growth conditions in target regions and to markets. The Task Force also accumulates data on performance of new elite lines, thereby facilitating varietal release procedures. Furthermore, by exposing breeders from NARS and farmers to these elite lines during the testing phase, dissemination will be facilitated. The activities conducted by the Task Force consists of a series of consecutive trials. Promising breeding lines developed by AfricaRice or by national and international partners, such as IRRI, CIAT and the NARS are nominated for evaluation in one or several rice cultivation environments: rainfed lowland, irrigated lowland, rainfed upland, high elevation and mangrove. All nominated lines should be fixed and accompanied by supporting data on traits incorporated during the breeding process and with information on yield performance. These characteristics are checked at AfricaRice before incorporation into the network. The first phase (MET, Multi-Environment Testing) consists of an initial evaluation of about 100 lines selected from the nominated lines. Each national partner evaluates these lines at sites in their country. Such sites may be at an experimental station under optimal management to evaluate yield potential, or may be ‘hot spots’ to check the performance of the nominations in a stressed growth environment. Trials are replicated three times and include at least a common and a local check. The second phase (PET,Participatory Evaluation Trial) serves to evaluate and confirm the performance of the selected lines. These lines are cultivated using the same experimental design with 3 replications. An important feature of PET is that farmer and other stakeholders such as miller and traders are invited to participate in varietal selection and their opinion on the performance of all entries (i.e. participatory varietal selection, PVS) collected. Based on the data collected, observations by the breeders and the opinion of stakeholder groups, NARS partners select up to 10 lines. Further, NARS evaluated these lines in at least three sites per country and during one or more growing seasons, depending on varietal release requirements. All stakeholders are again invited to get acquainted with the new lines and voice their opinion to help select lines for further advancement. Among the 10 lines, farmers are invited to select three lines and cultivate these in their own fields, together with a common check and their own variety."
 
 	uri <- "doi:10.7910/DVN/OF7M9D"
 	group <- "rice_trials"
@@ -19,7 +18,7 @@ carob_script <- function(path) {
 		carob_contributor="Eduardo Garcia Bendito",
 		carob_date="2022-01-21",
 		data_type="experiment",
-		treatment_vars = "variety_code;longitude;latitude"
+		treatment_vars = "variety_code"
 	)
 
 	d <- list()
@@ -31,7 +30,7 @@ carob_script <- function(path) {
 		dd$variety_code <- dd$genotype
 		# Burkina Faso and Mali miss the grain weight data
 		dd$grain_weight <- ifelse("gw1000" %in% colnames(dd), dd$gw1000, NA) 
-		d[[i]] <- dd[,c("country", "site", "season", "variety_code", "yield", "grain_weight")]
+		d[[i]] <- dd[, c("country", "site", "season", "variety_code", "yield", "grain_weight")]
 	}
 
 	d <- do.call(rbind, d)

scripts/soybean_trials/doi_10.25502_wpce-te77_d.R

@@ -17,7 +17,7 @@ carob_script <- function(path) {
 		carob_contributor="Cedric Ngakou",
 		carob_date="2023-09-21",
 		data_type="experiment",
-		treatment_vars = "variety;longitude;latitude",
+		treatment_vars = "variety",
 		project=NA 
 	)
 

scripts/sweetpotato_trials/doi_10.21223_P3_M0HGJ4.R

@@ -15,23 +15,20 @@ carob_script  <- function(path) {
 		carob_contributor="Cedric Ngakou",
 		carob_date="2023-11-06",
 		data_type="experiment",
+		treatment_vars = "variety",
 		project=NA 
 	)
 
-	# read file
 	r <- carobiner::read.excel(ff[basename(ff)=="Drought_06_08.xls"])  
 
-	d <- r[,c("YEAR","TREATMENT","REP","CULTIVAR","RYTHA","FYTHA","BIOM")]
-	colnames(d) <- c("planting_date","irrigation","rep","treatment","yield","dmy_leaves","dmy_total")
-
-
-	## add columns
-	d$crop <- "sweetpotato" 
+	d <- r[,c("YEAR","TREATMENT", "REP", "CULTIVAR", "RYTHA", "FYTHA", "BIOM")]
+	colnames(d) <- c("planting_date", "irrigation", "rep", "variety", "yield", "dmy_leaves", "dmy_total")
 
+	d$crop <- "sweetpotato" 	
 	d$country <- "Mozambique"
 	d$adm1 <- "Maputo"
 	d$location <-  "Umbeluzi"
-	d$trial_id <- paste(d$treatment,d$location,sep = "_")
+	d$trial_id <- as.character(as.integer(as.factor(d$location)))
 	d$yield_part <- "roots"
 	d$on_farm <- TRUE
 	d$is_survey <- FALSE

scripts/sweetpotato_trials/doi_10.21223_P3_OIQSOC.R

@@ -1,75 +1,75 @@
 # R script for "carob"
 
-## ISSUES
-
 
 carob_script <- function(path) {
 
-"The objectives of this study were to describe an Acelerated breeding scheme, ABS, for sweetpotato and to investigate the efficiency of this breeding scheme for selecting high-yielding and well-adapted orange-fleshed sweetpotato (OFSP) cultivars with high β-carotene content. More than 198,500 seeds from two crossing blocks were germinated and rapidly multiplied for evaluations in observation trials at four breeding locations in Mozambique. Breeding clones with storage root yields above 10 t/ha were advanced to preliminary and advanced yield trials across four sites and for three years. As a result, 64 high-yielding OFSP breeding clones were selected and evaluated in four mega-environments following a randomized complete block design with three replicates at Angónia, Chókwè, Gurué, and Umbelúzi. Field agronomic data and storage root quality data were collected. Data from multi-environment trials were subjected to single site and combined analysis of variance as well as to stability analysis using AMMI and regression."
+"The objectives of this study were to describe an Acelerated breeding scheme, ABS, for sweetpotato and to investigate the efficiency of this breeding scheme for selecting high-yielding and well-adapted orange-fleshed sweetpotato (OFSP) cultivars with high β-carotene content. More than 198, 500 seeds from two crossing blocks were germinated and rapidly multiplied for evaluations in observation trials at four breeding locations in Mozambique. Breeding clones with storage root yields above 10 t/ha were advanced to preliminary and advanced yield trials across four sites and for three years. As a result, 64 high-yielding OFSP breeding clones were selected and evaluated in four mega-environments following a randomized complete block design with three replicates at Angónia, Chókwè, Gurué, and Umbelúzi. Field agronomic data and storage root quality data were collected. Data from multi-environment trials were subjected to single site and combined analysis of variance as well as to stability analysis using AMMI and regression."
 
-   ## Process 
    uri <- "doi:10.21223/P3/OIQSOC"
    group <- "sweetpotato_trials"
    ff <- carobiner::get_data(uri, path, group)
 
    dset <- data.frame(
-		carobiner::read_metadata(uri, path, group, major=1, minor=7),
-		publication="doi:10.1017/S002185961600099X",
-		project=NA,
-		data_institute="CIP",
-		carob_contributor="Cedric Ngakou",
-		carob_date="2023-11-29",
-		data_type="Experiment"
+		carobiner::read_metadata(uri, path, group, major=1, minor=7), 
+		publication="doi:10.1017/S002185961600099X", 
+		project=NA, 
+		data_institute="CIP", 
+		carob_contributor="Cedric Ngakou", 
+		carob_date="2023-11-29", 
+		treatment_vars = "variety", 		
+		data_type="experiment"
    )
 
-   ## process file(s)
    r <- carobiner::read.excel(ff[basename(ff)=="Multilocational trials with 64 clones at 4 sites .xls"])
-   r$treatment<- r$`Geno Name`
-   d<- r[,c("Year","Locality","treatment","RYTHa","Biomass","RVY")]
-   colnames(d) <- c("planting_date","location","treatment","yield","dmy_total","dmy_leaves") 
+   d <- r[, c("Year", "Locality", "Geno Name", "RYTHa", "Biomass", "RVY")]
+   colnames(d) <- c("planting_date", "location", "variety", "yield", "dmy_total", "dmy_leaves") 
 
    ## add columns
    d$country <- "Mozambique"
    d$crop <- "sweetpotato" 
-   d$row_spacing <- 90  # from #doi:10.1017/S002185961600099X
+   d$row_spacing <- 90  # from doi:10.1017/S002185961600099X
    d$plant_spacing <- 30    
 
-   d$trial_id <- paste(d$location,d$treatment,sep = "_")
+   d$trial_id <- paste(d$location, d$treatment, sep = "_")
    d$yield_part <- "roots"
    d$on_farm <- TRUE
    d$irrigated <- FALSE
    d$is_survey <- FALSE
-   d$irrigated <- TRUE # more information can be found here #doi:10.1017/S002185961600099X
+   d$irrigated <- TRUE # see doi:10.1017/S002185961600099X
 
    ## fix yield value
-   d$yield <- gsub("\\*",NA,d$yield)
+   d$yield <- gsub("\\*", NA, d$yield)
    d$dmy_leaves <- gsub("\\*", NA, d$dmy_leaves)
    d$dmy_total <- gsub("\\*", NA, d$dmy_total)
-   d <- d[!is.na(d$yield),] ## remove NA in yield	
+   d <- d[!is.na(d$yield), ] ## remove NA in yield	
 
    # data type
    d$yield <- as.integer(d$yield)  
-   d$dmy_total<- as.integer(d$dmy_total) 
-   d$dmy_leaves<- as.integer(d$dmy_leaves) 
-   d$planting_date<- as.character(d$planting_date)
-   d$yield<- d$yield*1000 # in kg/ha
-   d$dmy_total<- d$dmy_total*1000 # in kg/ha
-   d$dmy_leaves<- d$dmy_leaves*1000 # in kg/ha
+   d$dmy_total <- as.integer(d$dmy_total) 
+   d$dmy_leaves <- as.integer(d$dmy_leaves) 
+   d$planting_date <- as.character(d$planting_date)
+   d$yield <- d$yield*1000 # in kg/ha
+   d$dmy_total <- d$dmy_total*1000 # in kg/ha
+   d$dmy_leaves <- d$dmy_leaves*1000 # in kg/ha
 
    # set the values out of bounds into NA
-   d$dmy_leaves[d$dmy_leaves>20000]<- NA
-   d$dmy_total[d$dmy_total>100000]<- NA
+   d$dmy_leaves[d$dmy_leaves>20000] <- NA
+   d$dmy_total[d$dmy_total>100000] <- NA
    ## add long and lat coordinate 
-   geo<- data.frame(location=c("Angonia","Chokwe","Gurue","Umbeluzi"),
-                    longitude=c(34.1444739,32.8598472,36.9410455,32.56745),
-                    latitude=c(-14.7689832,-24.4886204,-15.4544621,-25.966213))
+   geo <- data.frame(location=c("Angonia", "Chokwe", "Gurue", "Umbeluzi"), 
+                    longitude=c(34.1444739, 32.8598472, 36.9410455, 32.56745), 
+                    latitude=c(-14.7689832, -24.4886204, -15.4544621, -25.966213))
 
-   d<- merge(d,geo,by="location",all.x = TRUE)
+   d <- merge(d, geo, by="location", all.x = TRUE)
 
-  d$season<- "October 2009- March 2010"  ## from #doi:10.1017/S002185961600099X
-  d$planting_date<- paste(d$planting_date,"10",sep = "-")  
-  d$harvest_date<- "2010-03" 
-
+  d$season <- "October 2009-March 2010"  # from doi:10.1017/S002185961600099X
+  d$planting_date <- paste(d$planting_date, "10", sep = "-")  
+  d$harvest_date <- "2010-03" 
+
+	d$N_fertilizer <- d$P_fertilizer <- d$K_fertilizer <- 0
+
+	d <- unique(d) 
+
    carobiner::write_files(path, dset, d)
 }