Research on World Agricultural Economy | Vol.4,Iss.2 June 2023

Research on World Agricultural Economy
Editor-in-Chief
Guido Van Huylenbroeck, Ghent University, Belgium
Cheng Sun, China Branch of World Productivity Federation of Science and Technology; Academic Committee of the
United Nations NGO International Information Development Organization, China
Associate Editors
Jesus Simal-Gandara, University of Vigo, Spain
Filippo Sgroi, University of Palermo, Italy
Yu Sheng, Peking University, China
Editorial Board Members
Erwin Bulte, Wageningen University, Netherlands
Man-Keun Kim, Utah State University, United States
Fabian Capitanio, University of Naples Federico II, Italy
Tomoaki Nakatani, The University of Tokyo, Japan
G M Monirul Alam, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Bangladesh; University of South-
ern Queensland(USQ), Australia
Alberto J. Nunez-Selles, Universidad Nacional Evangelica (UNEV), Dominican Republic
Jiban Shrestha, National Plant Breeding and Genetics Research Centre, Nepal
Zhiguo Wang, China Association for Science and Technology, China
Xiaoyong Huang, International Energy Security Research Center, Chinese Academy of Social Sciences, China
Giuseppe Pulighe, Council for Agricultural Research and Economics (CREA), Italy
Alamgir Ahmad Dar, Sher-e-Kashmir University of Agricultural Sciences & Technology, India
Keshav D Singh, Agriculture and Agri-Food Canada (AAFC), Canada
K. Nirmal Ravi Kumar, Acharya NG Ranga Agricultural University, India
Zhengbin Zhang, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, China
Paul Alejandro Herrera, Escuela Superior Politecnica del Litoral (ESPOL), Ecuador
Rishi Ram Kattel, Agriculture and Forestry University, Nepal
Jianping Zhang, Chinese Academy of International Trade and Economic Cooperation, China
Lin Shen, China Agricultural University, China
Juan Sebastián Castillo Valero, University of Castilla-La Mancha, Spain
Shahbaz Khan, National Agricultural Research Centre, Pakistan
Gioacchino Pappalardo, University of Catania, Italy
Alisher Tleubayev, Suleyman Demirel University, Kazakhstan
Ali Darub Kassar, University of Anbar, Iraq
Shaobo Long, Chongqing University, China
Wenjin Long, China Agricultural University, China
Mohammad Jahangir Alam, Bangladesh Agricultural University, Bangladesh & Zhongnan University of Economics
and Law, China

Volume 4 Issue 2 • June 2023 • ISSN 2737-4777 (Print) 2737-4785 (Online)
Research on World
Agricultural Economy
Editor-in-Chief
Guido Van Huylenbroeck
Cheng Sun

Volume 4 ｜ Issue 2 ｜ June 2023 ｜ Page1-77
Contents
Research Articles
1 Resilience of Grain Storage Markets to Upheaval in Futures Markets
Emma Hayhurst B.Wade Brorsen
6 Screening of Elite Mungbean Genotypes (Vigna radiata (L.) Wilczek) through Multivariate Analysis for
Food and Nutritional Security
Sanhita Ghosh Anindita Roy Sabyasachi Kundagrami
32 Development Trends of the Market of Agricultural Lending to Households in Ukraine: Analysis of Con-
sumer and Mortgage Loans
Svitlana Andros Vasyl Gerasymchuk
47 Exploring the Adoption and Impact of Conservation Agriculture among Smallholder Farmers in
Semi-Arid Areas: Evidence from Chamwino District, Tanzania
Noel Yared Selya Provident Dimoso Yohana James Mgale
62 Is Policy Greasing the Wheels of Global Palm Oil Trade?
Shweta Adhikari Dikshit Poudel Munisamy Gopinath
Review Article
18 Agricultural Research in Colombia: Counterpoint with the Brazilian System
Heiber Andres Trujillo Carlos José Caetano Bacha
Short Communication
13 Navigating the Path to Sustainable Oil Palm Cultivation: Addressing Nexus Challenges and Solutions
Giuseppe Pulighe

1
Research on World Agricultural Economy | Volume 04 | Issue 02 | June 2023
https://journals.nasspublishing.com/index.php/rwae
Copyright © 2023 by the author(s). Published by NanYang Academy of Sciences Pte. Ltd. This is an open access article under the Creative
Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License. (https://creativecommons.org/licenses/by-nc/4.0/).
1. Introduction
During much of 2005-2010, the U.S. wheat, corn and
soybeans futures markets experienced non-convergence.
Non-convergence occurs when futures contracts are set-
tled much higher or lower than the corresponding mar-
ket’s cash price. Futures contracts nearing expiration are
expected to be close to or equal to the cash price at deliv-
ery locations, as arbitrage is expected to cause the law of
one price to hold [1]
. As Garcia, Irwin, and Smith [2]
argue,
this divergence was created by a divergence in the price of
deliverable warehouse receipts and the price of grain.
In a non-converging market, the hedger is still protect-
ed from price risk as long as the futures and cash prices
move in the same direction. Cash market gains and loss-
es can still be offset by futures market gains and losses.
In this case, cash and futures prices do not converge to
each other, but they converge on a predictable basis. On
the other hand, if the basis at expiration exhibits random
fluctuations, then a hedger is not insulated from price risk.
The volume of futures trading remained high during the
non-convergence periods, which suggests that hedgers
may have been able to adapt.
Whether firms hedge or not, they typically base their
DOI: http://dx.doi.org/10.36956/rwae.v4i2.826
Received: 14 March 2023; Received in revised form: 14April 2023; Accepted: 20April 2023; Published: 25April 2023
Citation: Hayhurst, E., Brorsen, B.W., 2023. Resilience of Grain Storage Markets to Upheaval in Futures Markets.
Research on World Agricultural Economy. 4(2), 826. http://dx.doi.org/10.36956/rwae.v4i2.826
*Corresponding Author:
B.Wade Brorsen,
Department of Agricultural Economics, Oklahoma State University, Stillwater, OK, 74078, USA;
Email: wade.brorsen@okstate.edu
RESEARCH ARTICLE
Resilience of Grain Storage Markets to Upheaval in Futures Markets
Emma Hayhurst1,2
B.Wade Brorsen2*
1. Consolidated Grain and Barge Co., Catoosa, OK, 74015, USA
2. Department of Agricultural Economics, Oklahoma State University, Stillwater, OK, 74078, USA
Abstract: The past two decades have had times when grain cash and futures markets did not converge during delivery.
What was the economic impact of this non-convergence on storage markets? To answer this question the supply of
storage is estimated for corn, soybeans, and wheat. The lack of convergence is measured using a historical basis. The
econometric model shows no relationship between the supply of storage and the lack of convergence. Thus, empirical
results suggest that markets were able to adapt to the lack of convergence. Overall, the research indicates the resilience
of storage markets to structural change.
Keywords: Basis; Convergence; Hedging; Storage

2
price expectations upon the futures market. The particular
concern is that the non-convergence could have caused
the returns to storage to be overestimated. To address
this concern, the primary objective of this research is to
determine the effect of lack of convergence on the supply
of storage. Note that mispricing in one market has the
potential to spread to other markets [3,4]
, so the issue is of
concern to world grain markets.
2. Theory of Storage
The theory of storage [5-8]
, defines the equilibrium rela-
tionship between cash and futures prices. This relationship
can be stated in terms of the basis, the difference between
the contemporaneous spot price in period t, St, and the
futures price (as of date t) for delivery at date T, Ft,T. The
theory is that the (negative of the) basis is composed of
the cost-of-carry: Interest foregone to borrow to buy the
commodity, St rt, (where rt is the interest charge on a dollar
from t to T), plus the physical storage costs w(T – t), mi-
nus a convenience yield, ct, which is an implied return on
inventories:
, −  =  + ( − ) −  (1)
The futures price minus the spot price equals the basis.
The basis is equal to St rt, the opportunity cost, plus the
marginal storage cost (w(T – t) where w is the daily phys-
ical cost of storage), minus the convenience yield. Under
the theory of storage, inventories are held only if expected
returns are positive. A lack of convergence (with futures
higher than cash) would distort this formula and project
returns to be higher than actual. Therefore, a shift in the
demand for storage could occur and more grain would
be stored. The expected profit maximization for a storage
provider, assuming that the producer is hedging, can be
expressed as:
max

  =  +ℎ −  −  +ℎ −   − ()
 ≤    
(2)
where E(π) is the expected profit, Q is the quantity stored,
Ft+h is the distant futures price, Ft is the nearby futures
price, St is the cash price, St+1 is the distant cash price and
C(Q) is a cost function that includes storage fees, insur-
ance, pest management and other costs associated with
the storing of the grain. The amount of grain that can
be stored is constrained by the capacity, where capacity
equals the amount of storage available, for example grain
elevators. Brennan [7]
lets the amount of a commodity held
in storage be determined by the equality of marginal cost
of storage and the temporal price spread. In a competitive
market a firm seeking to maximize net revenue will hold
the amount of stocks such that the net marginal cost of
storage per unit equals the expected change in price per
unit of time.
Van Huellen [9]
explains the non-convergence augments
using the commodity storage model and a price-pressure
component:
 +ℎ = ,+ℎ +  + (  +ℎ)(3)
where E(St+h) is the expected future cash price, Ft,t+h is the
futures price at time t and contract maturity of t+h, ρt is
a risk premium, and E(Basist+h) is the expected basis at
time t+h. Non-convergence makes it difficult for firms to
forecast basis. If they are unable to predict the non-con-
vergence then their expected returns to storage will be
inaccurate and there will be a loss of social welfare [10,11]
.
Hatchett and Brorsen [12]
as well as Thompson et al. [13]
suggest using only the most recent information to forecast
basis during times of structural change, but even that is
only partly successful.
The Chicago Board of Trade (CBOT) and Kansas City
Board of Trade (KCBOT) made changes to grain futures
contract specifications to combat the 2005-2010 non-con-
vergence problems. Changes included limiting the number
of warehouse receipts and shipping certificates that a trad-
er could hold, expanding delivery locations, and variable
storage rates [20]
. Irwin [20]
argues that the most fundamen-
tal change was the implementation of a variable storage
rate (VSR) rule for CBOT wheat beginning in September
2010. The Chicago Mercantile Exchange (CME) did not
introduce VSR to corn and soybeans markets but chose to
increase their fixed storage fees in 2008 and later in 2020 [15]
.
The objective of implementing VSR was to improve con-
vergence, and that is ultimately what it did. While index
funds are often blamed for distorting markets, there is lit-
tle empirical evidence that they do so [16-18]
.
3. Data and Methods
Data used for this research came from multiple sourc-
es. Futures prices for corn and soybeans were compiled
by the Livestock Marketing Information Center (LMIC)
and stem from reported prices of CBOT/CME Group
futures contract settlement prices. The Kansas City hard
red winter wheat contract was used for wheat and these
prices come from Barchart. Cash prices for all three com-
modities were compiled by LMIC based on USDA reports
with both #2 Yellow Corn and #1 wheat using Kansas
City prices and #1 Yellow Soybeans using Central Illinois
prices. The ending stocks for each commodity come from
the World Agricultural Supply and Demand Estimates
(WASDE) report. The annual ending stock quantities used
for wheat are on May 1st, and corn and soybeans are on
July 1st. The annual interest rate used is the market yield

3
on U.S. Treasury securities at 10-year constant maturity,
which comes from the Federal Reserve Economic Data
(FRED). Non-convergence was measured using the basis
of the 4 weeks prior to each contract’s expiration date,
which is the 15th of that month.
The equation estimated for the supply of storage is:
  = 0 + 1   + 2   
+ 3      + 
(4)
where ESt is the quantity of ending stocks of the commod-
ity at time t, OppCostt is the cash price of the commodity
multiplied by the annual interest rate at time t, which
measures the opportunity cost of storing, Returnst is the
expected returns on storage of the commodity using the
futures price, at time t, NonConvergence is a measure
of the basis, and ϵt is the random error term such that
 ~  0, 
2
. Note that the relationship to returns is some-
times considered nonlinear [19]
. The linear approximation
is used here due to the relatively small degrees of free-
dom.
4. Results and Discussions
Table 1 presents the estimates of Equation (4), the sup-
ply of storage equation using opportunity cost, the returns
from storage and measurement of non-convergence. When
trying to connect non-convergence to the amount of grain
stored, Table 1 indicates that the measure of convergence
is not statistically significant for any of the three com-
modities. Note that this finding is consistent with Revore-
do-Giha and Zuppiroli [20]
who found no change in hedg-
ing effectiveness in U.S. wheat markets over 2007-2012.
Similarly, Karali et al. [21]
found that non-convergence did
not affect the economic relationship between soft red win-
ter wheat delivery and non-delivery locations. Shi and In-
sengildina-Massa [22]
, however, found that hedging failure
was more common in corn markets during 2007-2013.
The expected sign for the convergence variable is nega-
tive, so it would counter the naive expectation of higher
returns on storage than actual returns. So, corn does not
have the expected sign for the convergence variable. The
other explanatory variables have the expected signs and
are statistically significant.
5. Conclusions
The empirical results suggest that grain storage mar-
kets adapted to the lack of convergence between cash and
futures prices. This research found a negative relationship
between opportunity cost and ending stocks, as well as a
positive relationship between returns to storage and end-
ing stocks. Thus, firms appear to have formed price ex-
pectations based on the predicted change in futures prices
rather than by assuming that basis would converge.
Table 1. Estimates of the effect of non-convergence on the supply of storage.
Commodity Variable Coefficient t-val p-value
KCHRW Intercept 518 *** 7.85 0.001
Opportunity cost ($/bu) –1463 *** –4.26 0.001
Return on storagea
($/bu) 295 ** 2.79 0.012
Basisb
($/bu) –35 –0.93 0.368
Corn Intercept 1669 *** 4.82 0.000
Opportunity cost ($/bu) –7754 *** –3.39 0.004
Return on storagea
($/bu) 2978 *** 3.62 0.002
Basisb
($/bu) 37 0.46 0.649
Soybeans Intercept 738 *** 6.23 0.0001
Oppportunity cost ($/bu) –1924 *** –4.18 0.001
Return on storagea
($/bu) 1425 ** 3.50 0.003
Basisb
($/bu) –286 –1.49 0.156
*p0.1, ** p0.05, ***p0.01.
Notes: The time period was 2000-2021, which gave 21 observations. The dependent variable is ending stocks (May for wheat and
July for corn and soybeans).
a
Return on storage is the calendar spread (for example, KC HRW March 2018 Futures Contract Price minus KC HRW May 2017 Fu-
tures Contract Price).
b
Basis is the average of the four weeks prior to the contract’s expiration date.

4
Author Contributions
The manuscript is based on the MS thesis by Emma
Hayhurst, which was submitted in May 2022 with the
same title. She conducted all statistical analysis, wrote the
original draft, and substantial revisions. Dr. Wade Brorsen
provided advice and editorial suggestions.
Funding
Funding was provided by the Oklahoma Agricultural
Experiment Station and National Institute of Food and
Agriculture Hatch Project OKL03170 as well as the A.J.
and Susan Jacques chair.
Data Availability
The data are available upon request from the corre-
sponding author.
Conflict of Interest
The authors declare no conflict of interest.
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6
Received: 18 February 2023; Received in revised form: 18April 2023; Accepted: 27April 2023; Published: 10 May 2023
Citation: Ghosh, S., Roy, A., Kundagrami, S., 2023. Screening of Elite Mungbean Genotypes (Vigna radiata (L.)
Wilczek) through Multivariate Analysis for Food and Nutritional Security. Research on World Agricultural Economy.
4(2), 814. http://dx.doi.org/10.36956/rwae.v4i2.814
Sanhita Ghosh,
Department of Genetics and Plant Breeding, University of Calcutta, Kolkata, West Bengal, 70019, India;
Email: sanhitaghosh91@gmail.com
RESEARCH ARTICLE
Screening of Elite Mungbean Genotypes (Vigna radiata (L.) Wilczek)
through Multivariate Analysis for Food and Nutritional Security
Sanhita Ghosh*
Anindita Roy Sabyasachi Kundagrami
Department of Genetics and Plant Breeding, University of Calcutta, Kolkata, West Bengal, 70019, India
Abstract: The ever-increasing urbanization to accommodate the growing population reduces substantially the
agricultural land but poses a threat to meeting the requirement of proper nutrition for human health. Mungbean [Vigna
radiata (L.) Wilczek] is a unique gift bestowed by nature to mankind, which has the potency to make up the gap of
protein shortage with an inexpensive cost, but due to its low level of production as well as productivity, which in a
roundabout way influences the nutritional status of people resulting in malnutrition. Therefore, enhancement of the total
area under mungbean cultivation is not permissible, and an increase in the total productivity per unit area is necessary.
In this manner, screening and evaluation of improved genotypes for high yield are necessary to ensure food security.
But at the same time seed yield being a complex character governed by several other contributing traits, selection for
the characters proves to be quite challenging. As a prerequisite for any breeding program aimed at yield enhancement
presence of significant genetic diversity in a given population is highly important. In the present investigation principal
component analysis was performed and the results revealed two principal components contributing to the total variance
in the population. While the PC1 was predominated by yield and its attributing traits, the PC2 was mainly comprised
of growth-related traits. The hierarchical (UPGMA) cluster analysis using standardized data classified the fifty-two
mungbean genotypes into 4 clusters, which showed 2 major, 1 minor and one outlier. Among them, cluster II is the
most fascinating, as its individual had high seed yield plant–1
and related traits. The present work concluded that the
identification of promising high-yielding mungbean genotypes through multivariate analysis has a good promise for
future breeding programs with a view of food and nutritional security.
Keywords: Mungbean; Screening; Multivariate analysis

7
1. Introduction
The present unequivocal confirmation that the global
population has already grown exponentially and predicted
it will rise from the present to 9 billion in 2050 [1]
. With
rapid urbanization and unchecked population growth
ensuring food and nutritional security for the population
has proved to be quite difficult even with the support of
advanced technology in the field of agricultural science [2,3]
.
In plant genetics resources, pulse crop species are the
base subsistence of the world food security for a growing
population. Mungbean [Vigna radiata (L.) Wilczek] is a
unique gift presented by nature to mankind, which has the
potency to make up the gap of protein shortage in view
of its three-fold amount of protein as much as cereals [4]
.
Besides, this crop has not only the capability to enrich
soil fertility with physical and biological properties of soil
health through symbiotic nitrogen fixation but also plays
an important role in the economy to increase the farmer’s
income through the intercropping system [5]
. India alone
with grown area of 3.72 million hectares and production
of 1.70 million tons with productivity of 406.98 kg/ha rep-
resents around two third of global production [6]
. Thus, the
overall annual production of the crop has increased yet the
crop productivity has plateaued due to the non-availability
of high-yielding genotypes and lack of genetic variability
as well as post-harvest losses because of bruchid attack [7]
.
Under the circumstances, enhancement of productivity is
necessary for ensuring the food security of the population.
So, there is a strong need for increasing the mungbean
productivity but the current agricultural practices and the
availability of sufficient land put a bar on it. Hence, an
alternative approach is necessary to look for introducing
improved high-yielding genotypes.
The sound knowledge of genetic diversity in genetic
resources is a crucial part for plant breeders to better
comprehend the evolutionary and the hereditary connec-
tions among accessions, to choose germplasm in a more
organized and impressive way and to create convenient
diversity in their plant breeding program [8]
. From the very
beginning of agriculture genetic variability within crop
species to meet subsistence food requirements has been
done and now it is being utilized to surplus food for ris-
ing populations. The unavailability of stable high-yielding
varieties potential is a major bottleneck for growing
mungbean. Empirical selection for genotypes with high
yield is difficult because of the yield complex nature con-
trolled by polygenes. Yield is a complex trait, associated
with many contributing traits which is highly influenced
by the environment. Analysis of yield and related traits
are also presented an intricate chain of relationships and
picturized a reflection of their gene effects [6]
. Multivariate
analysis such as principal component analysis and cluster
analysis are statistically eligible to experiment and ana-
lyze a matrix of complicated values which can be utilized
to think about the connection among traits and decide key
properties and attributes that are involved in economic
yield [9]
. PCA makes it conceivable to transform a given
set of traits, which are either associated or not into a new
system while cluster analysis is a clear and easy method
to group the investigated data through their similarities by
a view of a two-dimensional vision [10,11]
. Estimation of the
genetic diversity can help in the identification of geneti-
cally distant parents present in the population. Hybridiza-
tion between such genetically distant parents can ensure a
maximum number of recombinants expected in the segre-
gating generation of such crosses.
Keeping these factors in view, the present investigation
was conducted to determine the nature and magnitude of
genetic diversity among the fifty-two mungbean genotypes
for yield and yield attributing traits through multivariate
analysis, particularly principal component analysis. Such
analysis can clarify the association among agro-morpholog-
ical traits and cluster analysis provides valuable informa-
tion to screen and identify the promising high-yielding elite
mungbean genotypes for future food security.
2. Materials and Methods
2.1 Experimental Material
The fifty-two mungbean genotypes were collected from
different areas of India such as NBPGR (New Delhi);
Pulse Oil Seed Research Station (Berhampore); some
local accessions of different districts of West Bengal and
all genotypes listed in Table 1.
2.2 Experimental Site, Seasons and Cultivation
The present study was carried out at the Department of
Genetics and Plant Breeding at Institute of Agricultural
Science, University of Calcutta and the experimental
materials consisted of fifty-two mungbean genotypes that
were evaluated at Experimental Farm of University of
Calcutta, Baruipur, South 24 Parganas West Bengal, India
(220 N, 88.260 E and 9.75 m above the sea level) during
the period of mid-March to end May in three different
Years. The experiment was laid out in a Random Block
Design (RBD) using three replications with the experi-
mental plot. There were rows per plot of each genotype
spaced 30 cm apart. The length distance of each row was
3 m, with plant to plant distance of 10 cm within a row.
Most of the cultural practices were performed according
to Park, 1978 [12]
.

8
2.3 Observed Traits
Data were collected on five randomly selected healthy
harvested plants from each replication and each genotype.
Pods of each plant were kept separately in an envelope
and dried. Threshing was done by hand was taken to avoid
a mixture of seeds. The pre and post-harvesting observa-
tions were recorded from five randomly selected plants
from each replication on different parameters such as plant
height (PH), branches plant–1
(BPP), pods plant–1
(PPP),
pod length (PL), seeds pod–1
(SPP), 100 seed weight (HSW),
harvest index (HI) and seed yield plant–1
(SYPP) which were
determined on plot basis according to Moussa [13]
and the
mean values computed from the observations of both the
seasons were used for statistical analysis.
2.4 Statistical Analysis
To assess the overall variation attributed by yield attrib-
uting traits in mungbean, the descriptive statistics includ-
ing mean, standard error (SE) and range in standard unit
were calculated using SPAR 2.0 software package and the
Principal component analysis (PCA) and k-means cluster-
ing (combined data over three seasons used for each trait)
were done using IBM SPSS 20.0 while tree diagram (den-
drogram) based on Unweighted Pair Group Method with
Arithmetic Mean (UPGMA) method with the Euclidean
distance matrix [14]
was constructed by Darwin version 6.
The first two principal components were plotted against
each other to find out the patterns of trait variability
among the mungbean genotypes using SPSS version 20.
3. Results and Discussion
The basic statistics for eight agro-morphological traits
were analyzed and summarized in Table 2 exhibited a
noticeable variation present in the experimental material.
Pods plant–1
, plant height, seed yield plant–1
and harvest
index showed high to medium variation whereas the rest
of the traits showed low variation.
Screening is the first best step to selecting good geno-
types for crop improvement. The hierarchical (UPGMA)
cluster analysis constructed and classified the fifty-two
mungbean genotypes into 4 clusters showing 2 major, 1
minor and one outlier in Figure 1. The genotypes were
distributed in each cluster presented in Table 3 exhibited
the result in a way that one genotype into cluster I con-
tained the outlier (1.92%), 17 accessions were grouped
into cluster II (32.69%), 2 genotypes made a small group
into cluster III (3.85%) while 32 accessions grouped into
transgressive cluster IV (61.54%). The K-Mean values
were displayed in Table 4 and Figure 2 based on four
clusters. Among them, cluster II constituted the most fas-
cinating group because here each elite genotype had high
seed yield as well as branches plant–1
, pods plant–1
, har-
vest index whereas cluster IV showed intermediate yield
potency. Cluster II showed lower values in all the traits
except pod length and 100 seed weight while the outlier
(cluster I) was showed distinct from the other cluster be-
cause it demonstrated that the lowest seed yield plant–1
as
well as low branches plant–1
, pods plant–1
, harvest index.
The inter-cluster distance among four cluster range be-
tween 10.57 to 28.60 based on Euclidean dissimilarity
matrix presented in Table 5. The highest inter-cluster dis-
tance was found between clusters I and IV (28.60) fol-
lowed by clusters I and III (26.71), clusters I and II (14.41).
The closer cluster distance appeared between clusters III
and IV (10.57) followed by clusters II and III (14.39) and
clusters II and IV (14.63). Kahraman et al. [11]
and Darkwa
et al. [15]
present similar result in common beans. Eigen-
values of eight principal components have been shown in
the scree plot Figure 3. Principle component analysis (PCA)
demonstrated that PC1 to PC2 had the Eigenvalues 1 con-
tributed traits variability 71.18% through PC1 and 28.81%
Table 1. List of mungbean genotypes.
Serial No. Genotype Name Serial No. Genotype Name Serial No. Genotype Name Serial No. Genotype Name
1 APDM-84 14 A-82 27 IPM-99-125 40 Sukumar
2 MH-98-1 15 PM-2 28 IPM-205-07 41 PDM-54
3 B1 16 TM-98-20 29 IPM-5-17 42 Sonamung 2
4 PS-16 17 HUM-8 30 KM-139 43 CUM1
5 PTM-11 18 Sonamung-1 31 PM-11-51 44 CUM2
6 SML-302 19 Panna 32 Pusa-1431 45 CUM3
7 ML-5 20 Baruipur local 33 SML-115 46 CUM4
8 APDM-116 21 Howrah local 34 PDML-13-11 47 CUM5
9 UPM-993 22 Purulia local 35 Pusa-1432 48 CUM6
10 MC-39 23 Bankura local 36 Samrat 49 CUS1
11 Pusa Baisakhi 24 Pant mung-5 37 HUM-16 50 CUS2
12 Pusa- 9632 25 VC-639 38 MH-909 51 CUS3
13 K-851 26 Pusa Vishal 39 WBM-045 52 CUS4

9
through PC2 in Table 6. Seed yield plant–1
and pods plant–1
with maximum values closer to unity within PC1 whereas
plant height and seeds plant–1
close with PC2 illustrated
in Figure 4. The positive and negative values in PCA
represented correlation trend between the traits. These
results were in trends with the findings of Pandiyan et al.
Therefore, PC1 assists to select the traits such as branches
plant–1
and seed yield plant–1
for yield improvement.
PH-Plant Height, BPP-Branches per Plant, PPP-Pods
Per Plant, PL-Pod Length, SPP-Seeds Per Pod, HSW-
Hundred Seed Weight, HI-Harvest Index, SYPP-Seed
Weight Per Plant.
PH-Plant Height, BPP-Branches per Plant, PPP-Pods
Per Plant, PL-Pod Length, SPP-Seeds Per Pod, HSW-
Hundred Seed Weight, HI-Harvest Index, SYPP-Seed
Weight Per Plant.
Screening is a prerequisite strategy for breeding to
improve productivity so that an important crop through
breeding traits variation is a necessity. Significant vari-
ation exists in the present study for yield contributing
Table 2. Basic statistics for eight quantitative traits in fifty-two mungbean genotypes.
Traits Pooled Mean ±Standard error Range Minimum Maximum
PH (cm) 61.97±0.49 50.02 76.90
NBPP 3.88±0.04 2.50 4.97
NPPP 44.55±0.66 20.90 63.53
PL (cm) 7.55±0.06 6.67 9.37
NSPP 11.61±0.05 9.64 13.15
HSW (gm) 3.35±0.06 1.80 5.48
HI 24.89±0.36 17.23 32.64
SYPP (gm) 14.99±0.34 9.65 25.04
Note: PH-Plant Height, NBPP-No. of Branches per Plant, NPPP-No. of Pods Per Plant, PL-Pod Length, NSPP-No. of Seeds Per Pod,
HSW-Hundred Seed Weight, HI-Harvest Index, SYPP-Seed Weight Per Plant, cm-centimeter, gm-gram.
Figure 1. Dendrogram showing a cluster of 52 different mungbean genotypes.
Table 3. Cluster analysis and classification with regard to agro morphological traits of mungbean.
Cluster No of Genotypes Percentage of Contribution Name of Genotypes
I 1 1.92 CUS4
II 17 32.69
Pusa Baishakhi, PS-16, MC-39, NDML-13-11, Panna, Sonamung-2,
IPM-5-17, Howrah local, PM-11-51, HUM-16, Baruipur local, Pant
mung-5, IPM-205-07, APDM-84, MH-909, B1, HUM-8.
III 2 3.85 CUM4, Pusa-1432.
IV 32 61.54
Sukumar, PM-2, PDM-54, UPM-993, CUS3, IPM-99-125, ML-5,
CUM6, CUM1, Pusa-1431, CUS2, Sonali, K-851, CUM3, WBM-045,
A-82, APDM-116, CUM2, CUS1, VC-639, SML-115, KM-139, Pusa-
9632, Purulia local, Pusa Vishal, Bankura local, PTM-11, MH-98-1,
Samrat, TM-98-20, SML-302, Pusa 1432.

10
traits. Ghosh et al. [16]
reported that adequate knowledge of
trait variation is an imperative marker that provides a sign
of the distinctive impacts which influence the aggregate
variation of plant traits while variation alludes to detect-
able contrasts among individuals for a specific trait. The
knowledge of Multivariate analysis not only indicates the
significant variance between average vectors but also pro-
vides efficient utilization for securing the genetic resourc-
es to forecast the potentiality of the breeding material by
rapid authentication [11,17]
. The nature of the distribution
of the genotypes across four clusters observed in the cur-
rent investigation suggested that the analysis successfully
Table 4. K-Mean performance of agro-morphological traits of four different clusters in mungbean genotypes.
Cluster PH (cm) BPP PPP PL (cm) SPP HSW (gm) HI SYPP (gm)
I 56.23 ± 3.03 2.80 ± 0.46 20.90 ± 0.17 8.70 ± 0.80 12.00 ± 0.14 4.40 ± 0.17 27.42 ± 1.66 9.65 ± 0.22
II 62.37 ± 0.69 4.01 ± 0.05 51.73 ± 0.83 7.82 ± 0.10 11.50 ± 0.08 3.59 ± 0.11 28.28 ± 0.51 20.15 ± 0.39
III 74.23 ± 1.54 3.19 ± 0.37 48.46 ± 1.47 7.26 ± 0.19 11.74 ± 0.18 2.98 ± 0.11 23.43 ± 0.63 10.65 ± 0.18
IV 61.18 ± 0.62 3.89 ± 0.05 41.22 ± 0.58 7.38 ± 0.06 11.65 ± 0.07 3.22 ± 0.07 22.96 ± 0.42 12.68 ± 0.20
Note: cm-centimeter, gm-gram
Figure 2. Means of eight quantitative traits of mungbean genotypes grouped into four clusters.
Table 5. Inter cluster distance and mean performance of agro-morphological traits of four different clusters of mung-
bean genotypes.
Cluster II III IV
I 14.41 26.71 28.60
II 14.39 14.63
III 10.57
Figure 3. Scree plot constructed for eight principal components.

11
classified the accessions based on their phenotypic per-
formances. Similar observations were earlier reported by
Basnet et al. [18]
. Cluster II with seventeen elite genotypes
presented the highest mean performance on seed yield
plant-1 as well as pods plant-1 and high values for the rest
of the traits and presumes that had special significance
in diversification, conservation of natural resources, crop
development and sustainability of production systems.
Mohammad and Sharif [9]
suggested that the selection of
genotypes for hybridization must take into account the
inter-cluster distances between different clusters as well
as the intra-cluster distances among genotypes belonging
to the same cluster to obtain optimum segregation during
recombination. In addition to cluster analysis the princi-
pal component analysis revealed that the first principal
component designated at PC1 plays a conceivable role to
identify the ideotype yield enhancement traits while PC2
differentiated factors that related to vegetative growth
exclusively in regenerative advancement. Pandiyan
et al. [19]
reported that K-Mean values showed traits ho-
mology, degree of genetic diversity and almost similar
trends in principle component analysis. Hence, pods
plant–1
, branches plant–1
, harvest index was considered as
the most important yield attributing component which is
directly reflected in the final yield and also selected seven-
teen elite high-yielding mungbean genotypes from cluster
II which transform new opportunity to surplus food and
nutrition for the rising population.
4. Conclusions
The current investigation successfully elucidated the
magnitude of diversity existing within a given population
of fifty-two mungbean germplasms. The study also helped
in identifying seventeen germplasms distributed within
the same cluster based on their high yield and promising
morphological traits. Such information can be worthwhile
to identify suitable parents for exploitation in future hy-
bridization programs, and also aim for yield improvement
along with other economically important traits.
Author Contributions
The first author as well as corresponding author San-
hita Ghosh took the lead in analysis, interpretation as well
as writing the manuscript while co-authors Sabyasachi
Kundagrami provided suggestions on experiments and
Anindita Roy helped during the analysis.
Acknowledgements
Authors highly acknowledge University Grant Com-
mission (UGC) and University of Calcutta for the finan-
cial support.
Data Availability
Data are available upon request to the corresponding
author.
The authors disclosed that they do not have any conflict
of interest.
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PPP 0.992 0.126
PL (cm) –0.933 0.359
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% of Variance 71.189 28.811
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12
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13
1. Introduction
The global demand for biomass for food, energy, and
chemical uses has led to a rapid expansion of oil palm
tree (Elaeis guineensis Jacq.) plantations in Southeast
Asia, Central Africa, Latin America and the Caribbean [1]
.
It was estimated that by 2050 the worldwide oil palm
plantations are expected to increase from 14.6 million
hectares in 2010 to 31.1 million hectares [2]
. Moreover, oil
palm cultivation has become a major source of income for
many countries in the tropics and subtropics, contributing
significantly to the provision of private and community
Received: 4 April 2023; Received in revised form: 10 May 2023; Accepted: 15 May 2023; Published: 22 May 2023
Citation: Pulighe, G., 2023. Navigating the Path to Sustainable Oil Palm Cultivation: Addressing Nexus Challenges
and Solutions. Research on World Agricultural Economy. 4(2), 835. http://dx.doi.org/10.36956/rwae.v4i2.835
Giuseppe Pulighe,
CREA, Research Centre for Agricultural Policies and Bioeconomy, Via Barberini 36, 00187 Rome, Italy;
Email: giuseppe.pulighe@crea.gov.it
SHORT COMMUNICATION
Navigating the Path to Sustainable Oil Palm Cultivation: Addressing
Nexus Challenges and Solutions
Giuseppe Pulighe*
CREA, Research Centre for Agricultural Policies and Bioeconomy, Via Barberini 36, 00187 Rome, Italy
Abstract: Global palm oil demand for energy, food, and chemical uses has led to a rapid expansion of tree plantations
in Southeast Asia, Central Africa, Latin America and the Caribbean. This oil tree is the world’s most productive, highly
profitable and traded vegetable oil crop, and the demand is expected to increase further in the near future. Nevertheless,
oil palm expansion involves risks and nexus challenges. This work supports the idea that disruptive farming
intensification, instead of land expansion, could scale up productivity, reducing the anthropogenic pressure on tropical
forests and biodiversity losses. Findings from recent studies suggest that there is considerable scope for further yield
improvements per hectare of palm oil with sustainable agronomic practices and farming intensification. Smallholder
producers, agribusiness investors, civil society actors, NGOs, governments, researchers, and industry should make
coordinated efforts with regulatory and support schemes and landscape design to increase yield and productivity with
sustainable management practices and to achieve zero deforestation by protecting ecosystems.
Keywords: Land-use changes; Ecosystem services; Sustainable intensification; Deforestation; Tree plantations

14
goods in rural villages [3]
.
The process of planting oil palm trees typically begins
with the preparation of the land. This involves clearing the
existing vegetation and trees, which often results in defor-
estation and soil degradation. The land is then drained and
plowed, and young oil palm seedlings are planted in rows.
The seedlings are carefully tended to until they mature,
which takes about three years. Once the oil palm trees
mature, they start to produce fresh fruit bunches (FFBs)
which are harvested and then transported to a mill where
they are processed to extract the crude palm oil (Figure 1).
Figure 1. Oil palm plantation in Negeri Sembilan, Malaysia.
Source: Image courtesy of Nazarizal Mohammad.
https://unsplash.com.
Oil palm is the most productive (average oil yield 5.5
tonnes per hectare) [4]
, versatile, highly profitable and
traded vegetable oil crop in the world [5]
, and demand is
expected to grow further in the near future. Today, palm
oil is used in an impressive number of packaged products
(e.g. soap, cosmetics, detergents, chocolate, margarine,
and cookies), cooking oil, as well as for biofuel [6]
. Oil
palm is not only a source of edible oil but also a source of
bioenergy (Figure 2).
In the last decade, driven by government support [7]
tropical oils used as biodiesel-diesel blends were promot-
ed as a renewable resource in many scenarios for achiev-
ing climate change commitments. However, the rapid
expansion of oil palm plantations has also led to environ-
mental, social, and economic challenges. Global palm oil
demand acts as a driver of land-use changes with associ-
ated nexus challenges in the environmental, social and
economic spheres, leading to concerns about deforesta-
tion, soil degradation and losses of ecosystem services [8,9]
,
and other telecoupled effects such as land-grabbing, food
price volatility, income inequalities, and land conflicts
associated with palm oil concessions, especially for in-
dependent smallholder plots. In this perspective essay,
we claim that achieving sustainable oil palm cultivation
requires a collaborative effort that involves all stakehold-
ers, including governments, producers, retailers, and
consumers. We argue that the adoption of sustainable and
smart cultivation practices is essential to ensure that palm
oil production supports economic development and pov-
erty reduction in tropical regions, while minimizing the
negative impacts on the environment and society. In sum-
mary, the path to sustainable oil palm cultivation involves
balancing the economic benefits with environmental and
social considerations.
Figure 2. Palm oil value chain.
Source: This cover has been designed using resources from
https://Freepik.com.
2. Nexus Challenges
Addressing the challenges facing the palm oil industry
requires a comprehensive approach that considers eco-
nomic, social, and environmental issues. Several studies
have identified many future challenges, including emerg-
ing threats from GHG emissions and climate change, land
degradation, and pests and diseases [10]
. Nevertheless,
previous studies have suffered from siloed approaches
in addressing the range of challenges associated with
oil palm cultivation. In this study, the authors attempt to
solve nexus domains that coexist within the oil palm value

15
chain, highlighting the multifaceted challenges and pos-
sible solutions.
One of the main nexus challenges is deforestation.
Large-scale oil palm cultivation has been a leading cause
of deforestation in many tropical regions. This has re-
sulted in the loss of valuable carbon sinks and biodiversity
hotspots, leading to climate change and ecosystem de-
struction. Overall, tropical oil trees expansion has impli-
cations in the well-known trilemma [11]
posed to scientists,
international organizations, economists and governmental
institutions for balancing the domain of biofuel produc-
tion, food security and environmental implications. For
instance, land use changes and landscape fragmentation
affected high-biodiversity wilderness areas, as recently
suggested by de Almeida et al. [12]
in a long-term trajectory
of oil palm expansion in the eastern Brazilian Amazon.
In the same vein, Rulli et al. [13]
argue that bioenergy and
food industry demands have driven forest losses, forest
fragmentation and freshwater pollution in different areas
across Indonesia.
However, the GHG emissions following land use/cover
changes have been questioned by researchers in many
cross-sectional studies, suggesting substantial challenges
and trade offs concerning the accounting of greenhouse
fluxes [14,15]
. Bioenergy production is another intercon-
nected challenge associated with oil palm cultivation. As
46% of total palm oil imported by the European Union
was used as biofuels, in 2018 the EU Parliament provi-
sionally agreed to phase out the use of palm oil for trans-
port fuel to reduce the risk of direct and indirect land-use
changes [16]
. Nevertheless, avoiding palm by switching to
alternative replacement oils is not the solution in the short
term because other cultivated crops (e.g. jatropha, jojoba,
soybean, rapeseed) are less productive and will require ad-
ditional land resources [17]
. A recent assessment found that
land-intensive bioenergy will play a significant role in the
energy mix in the coming decades during the energy tran-
sition towards net-zero emissions targets [18]
. Future bio-
fuel targets and mandates will require a further land area
with substantial land-use changes and probably may lead
to multiple interdependencies. In this sense, completely
banning exports of bioenergy may not be the best solution
for the planet.
Social impacts are also a major concern associated
with oil palm cultivation. Strictly linked with the oil palm
expansion are land grabbing, displacement of indigenous
communities, poor working conditions for plantation
workers, and human rights abuses. Agribusiness multina-
tional corporations and big companies may drive continu-
ous expansion impacting the rural villages, populations
and territories [19]
, putting at risk freshwater availability,
food sovereignty and drinkable water for livestock.
Moreover, recent studies suggest a clear link between
deforestation and outbreaks of vector-borne and zoonotic
diseases [20,21]
. New plantations take up large tracts of land,
exacerbating interdependent connections on land, water,
food and human rights.
3. Solutions toward Sustainability
To address these challenges and controversies, a new
paradigm for modernizing oil palm cultivation and the
value chain is necessary. First, a viable solution is to sup-
port sustainable intensification with disruptive technolo-
gies, i.e., agriculture 4.0 (agriculture revolution which
uses digital technologies) [22]
and precision agriculture,
scaling-up productivity reducing pressure on tropical de-
forestation and biodiversity losses.
In this sense, findings from recent field-scale studies
and reviews suggest that there is considerable scope for
further yield improvements with improved high-yielding
varieties (i.e. breeding, genetic improvement) and inte-
grated farming systems for optimal inputs management
of nutrients, irrigation, pests and diseases. For example,
scientists are developing oil palm varieties that are more
resistant to drought and heat, which will become increas-
ingly important as the climate changes [4,23]
. Although
technology transformation and industry 4.0 are relatively
new concepts in the palm industry [24]
, the application of
disruptive innovations can effectively improve the sus-
tainability of the value chain. Under a high-yield growth
scenario of doubling global average palm oil yields up to
9 metric tons per hectare [2]
, future expansion of oil palm
plantations can be counterbalanced, and the harvested area
will slow at 2010 levels assuming no change in global de-
mand.
Regarding the issues on biodiversity and ecosystems,
new agroforestry systems planting buffer zones of native
vegetation around oil palm plantations and integrating
trees with crops can create more diverse and resilient
landscapes. For example, shade-tolerant crops like cof-
fee, cocoa, and black pepper can be grown beneath oil
palm trees, which provide habitat for wildlife and helps to
reduce the impact of monoculture cropping. This could di-
versify and stabilize the price and supply of the food bas-
ket and income of smallholders, reinforcing the resilience
and livelihoods of local communities.
4. Conclusions and Future Perspectives
To develop appropriate and sustainable oil palm cul-
tivation practices, nexus challenges and viable solutions
in the production pathway need to be better understood.

16
As stated above, the palm oil processing industry, large
agricultural companies, researchers, governments, and
small-scale producers should raise their ambition toward
technology innovations and new processing technologies
to modernize their practices of production and to reduce
competition and conflicts among different land uses.
If growth in palm oil demand continues to rise in the
next years, a key priority for policymakers should there-
fore be to plan for effective supply-chain interventions.
Important vehicles for nexus solutions include regulatory
support and economic support schemes [25]
. Regulatory
support instruments can include company pledges, codes
of conduct, sector-wide sanctions [26]
and rigorous ac-
counting rules protecting tropical and subtropical rainfor-
ests and biodiversity-rich ecosystems against unnecessary
and detrimental land conversions. In this sense, commit-
ments such as “No Deforestation, No Peat, No Exploita-
tion (NDPE)” can help raise awareness and prevent new
damage [6]
. Companies that adopt NDPE policies commit
to sourcing palm oil from suppliers who do not engage in
deforestation, conversion of peatlands, or exploitation of
workers. Economic support instruments include realistic
measures to regulate production and implementing policy
instruments such as mandatory quotas, tax incentives or
credits, capital subsidies, grants and rebates, and voluntary
market initiatives. Furthermore, sustainability indicators
such as those established by the Roundtable on Sustainable
Palm Oil principles and criteria (see The RSPO, 2020) [27]
,
and certified international standards checked against per-
formance measures (e.g. practical to implement, sensitive,
measurable and traceable) can further increase sector sus-
tainability, encouraging companies to adopt sustainable
practices and provide assurance to consumers that palm
oil is produced sustainably.
In conclusion, the path to sustainable oil palm cultiva-
tion involves a comprehensive approach that balances
economic, social, and environmental considerations. Ad-
dressing the challenges requires the cooperation of gov-
ernments, industry, and civil society. Adopting sustainable
practices can benefit not only the environment and local
communities but also the long-term profitability and repu-
tation of the palm oil industry.
Data Availability
The data presented in this study are available on re-
quest from the corresponding author.
The author declares that there have no known compet-
ing financial interests or personal relationships that could
have appeared to influence the work reported in this paper.
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18
Received: 22 April 2023; Received in revised form: 23 May 2023; Accepted: 29 May 2023; Published: 1 June 2023
Citation: Trujillo, H.A., Bacha, C.J.C., 2023. Agricultural Research in Colombia: Counterpoint with the Brazilian
System. Research on World Agricultural Economy. 4(2), 848. http://dx.doi.org/10.36956/rwae.v4i2.848
1. Introduction
In most Latin American countries, the agricultural sec-
tor is an important source of income and employment.
Also, export earnings contribute to overall economic
growth, poverty reduction, and the sustainable use of
natural resources [1]
. Another of the functions attributed
to agriculture in the economic development process is the
production of food and raw materials to meet the demands
of both domestic and foreign markets [2]
. This function
can be achieved, among other mechanisms, through agri-
Heiber Andres Trujillo,
Department of Crop Science, Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo (USP), Av. Pádua Dias 11,
Piracicaba, SP, 13418-900, Brazil;
Email: hatrujillos@usp.br
REVIEW ARTICLE
Agricultural Research in Colombia: Counterpoint with the Brazilian
System
Heiber Andres Trujillo1*
Carlos José Caetano Bacha2
1. Department of Crop Science, Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo (USP), Av.
Pádua Dias 11, Piracicaba, SP, 13418-900, Brazil
2. Department of Economics, Administration and Sociology, Luiz de Queiroz College of Agriculture (ESALQ),
University of São Paulo (USP), Av. Pádua Dias 11, Piracicaba, SP, 13418-900, Brazil
Abstract: This paper analyzes the evolution and structure of Colombia’s agricultural research network, paying special
attention to the role of government expenditures in modeling this system. The authors also compare the Colombian
agricultural network with the path followed by the Brazilian agricultural sector, which has been considered a pattern in South
America. For this purpose, a bibliographic review and historical and institutional data are presented. Although agricultural
research in Colombia began in the early 20th century, it has evolved more recently with the creation of different public and
private institutions linked to the National Science and Technology System. However, agriculture and its research sector have
faced major challenges related to government endowments that are needed to fund infrastructure and demand for researchers,
as well as lower competitiveness compared to their Brazilian counterparts determined by social profit.
Keywords: Competitiveness; Technological development; Institutions; Social profit

19
cultural research, which allows the country to expand its
range of processed products as well as increase productiv-
ity per area. Agricultural research shows up for itself as a
proposal for a sectoral approach, fundamentally aimed at
benefiting the sector with a view to making it competitive
and expanding its capacity to generate profits [3]
. However,
examining the impacts of research on food systems and,
therefore, on farmers and consumers is a complex task.
Agricultural research is considered one of the condi-
tioning factors of agricultural transformation [4]
, and it
has been one of the key factors to explain the increase of
agricultural productivity in South America during the last
decades, especially in countries such as Brazil, Chile, and
Uruguay [5,6]
. Producer associations, research foundations,
private sector companies, and universities have played an
increasing role in the technological development in the
region [1]
. In turn, increased productivity in agriculture is
one of the main sources of growth in the sector [7]
and it
is associated with greater investment in research in these
countries.
Investment in agricultural research is characterized by
returns much higher than those obtained in other activi-
ties. In the case of Brazil, according to Bonelli and Pes-
soa [5]
, rates of return were in the order of 20% to 30% in
the first half of the 1990s. More current data, such as the
Social Balance of Brazilian Agricultural Research Com-
pany (EMBRAPA), estimate an average rate of return on
investments in agricultural research of 45.1% [8]
. Such in-
vestments afforded by the Government expenditures have
been beneficial to Brazilian society. At present and by
various indicators associated with it, it is evident that Bra-
zilian agriculture has become one of the leading and most
competitive in the world. Both the structure and funding
of public research in agriculture have been essential to
achieving this competitiveness.
Agricultural modernization in Brazil, after the 1960s,
was stimulated by government policies at different levels
(particularly through rural credit policies, minimum pric-
es, research, and agricultural extension). Innovations in
technology (resulting from investments in research) led to
an increase in agricultural productivity [5]
. In this process,
the creation of the National Agricultural Research System
(SNPA), the role of EMBRAPA, the role of state-funded
research and technical assistance institutions, and the role
of universities and private, for-profit and non-profit or-
ganizations stand out [9]
.
In Colombia, agriculture was a determining sector in
the country’s development during most of the last century,
highlighted by the growth of coffee cultivation in dif-
ferent regions [10]
. However, since the 2000s, agriculture
has decreased its share in the Colombian economy [11,12]
.
According to data from the World Bank, the National
Administrative Department of Statistics (DANE), and the
National Planning Department (DNP), the contribution
of agriculture to the Colombian GDP went from 25% in
1965 to 22.30% in 1990 and reached only 6.30% in 2017.
The loss in the contribution of agriculture and livestock to
the Colombian trade balance is the result, on the one hand,
of the higher relative growth of other sectors and, on the
other hand, of the low productivity of the sector itself. The
low productivity of the agricultural sector also generates
less development, especially in areas where agriculture
has been considered the main economic vocation. Dur-
ing the last three decades, Colombia’s economic growth
has been driven by the advancement of sectors such as fi-
nance, mining, public services, electricity, and information
and communication technologies. According to Ludena [13]
,
between 2001 and 2007, the growth rate of Total Factor
Productivity (TFPa
) in Colombia’s agricultural sector
declined significantly. This, in part, reflects the lack of a
fully structured agricultural research segment that is even
capable of being competitive with these other sectors at
the national level, as could be the case in Brazil.
Agricultural research in Colombia has progressively
advanced and been strengthened with the creation of dif-
ferent institutions and diverse approaches. Agricultural
research began systematically in 1914 with the start of
academic activities at the School of Tropical Agriculture
and Veterinarian of Medellín. At that time, the prevailing
view was that agriculture was restricted to the produc-
tion of food for the domestic market, and there was an
enormous need for technical personnel trained in the areas
of agriculture, livestock, forestry, fisheries, and natural
resources, as well as in post-harvest activities. Although,
since the creation of the National Coffee Research Center
(CENICAFÉ) in 1938, Colombia had centers specialized
in different crops, it was only in 2017 that the National
Agricultural Innovation System (SNIA) was established.
The main objective of this system is to contribute to the
improvement of productivity and competitiveness through
the articulation of national and regional policies to en-
courage the development of science, technology, and in-
novation in the agricultural sector. Currently, agricultural
research in Colombia includes a significant number of
governmental entities, higher education institutions, non-
profit, private, and international entities working on it [14]
.
However, in order to measure, monitor, and compare the
resources (human and financial), results, and performance
a Total factor productivity (TFP) can be defined as a ratio of total out-
put to total inputs. Thus, TFP is a unique measure designed to describe
the efficiency of the use of inputs to achieve a total volume of final out-
puts.

20
of agricultural research and development systems in Co-
lombia over time, it is essential to have indicators that
make it possible to evaluate the contribution of agricul-
tural research to the country’s development.
In this context, the general objective of this article is
to analyze the evolution of agricultural research in Co-
lombia, paying attention to the institutional framework
of the public sector, financial and human resources, and
the results of the research system, trying to make a com-
parison with the existing agricultural research system in
Brazil. This article is based on a bibliographic survey, the
collection of secondary data, and the analysis of technical
reports on agricultural research in Colombia and Brazil,
comparing them to identify facts that would allow the Co-
lombian system to position itself better in relation to the
Brazilian system. In Gil’s [15]
classification, this is explora-
tory research using the comparative research methodb
.
In addition to this introduction, the article comprises
five more sections. The second section presents the litera-
ture review, placing the previous objective in the context
of current knowledge about the subject under analyzing.
Section 3 presents the historical milestones of agricultural
research in Colombia and Brazil. In sequence, Section 4
presents a comparison between the entities conducting
agricultural research in both Colombia and their counter-
parts in Brazil. Section 5 analyzes the human and financial
resources granted to agricultural research in these two
countries (Colombia and Brazil), followed by Section 6,
which brings the final considerations of the article.
2. Literature Review
The literature closest to this paper’s objective refers
to works that address the origin and evolution of the ag-
ricultural research system and its current stage in Brazil
and Colombia. In the case of Brazil, for example, Stumpf-
Junior and Balsadi [3]
present the historical evolution of
Brazilian agricultural research from 1500 until the crea-
tion of EMBRAPA in 1973, the different approaches to
agricultural research, and an agenda for its development.
Considering a more recent period, Castro [16]
complements
the history and evolution of institutions conducting pub-
lic agricultural research in Brazil. This author advocates
continuing the allocation of public resources to agricul-
tural research because of the results it has achieved. Ad-
dressing the situation existing at a given time, there is, for
example, the work of Dossa and Segatto [17]
, who describe
b According to Gil [15]
, exploratory research is developed with the ob-
jective of providing an approximate vision of a given fact. On the other
hand, the comparative method involves the investigation of individuals,
classes, phenomena, or facts in order to highlight the differences and
similarities between them.
the institutions and interrelationships between public and
private sector activities in agricultural research in Brazil
as they existed in the mid-1990s. They also emphasize the
need for the Brazilian government to continue investing
in research and in the implementation of new forms of
public-private partnerships in order to maximize the social
benefits of scientific activity. More recently, Moreira and
Teixeira [9]
studied the creation of the National Agricul-
tural Research System (SNPA) and development institu-
tions, highlighting the return on investment in agricultural
research in Brazil and its impacts on society.
Among the few studies about the agricultural research
institutions in Colombia, Roldan [18]
provides a historical
but not complete panorama. The author starts by high-
lighting the Botanical Expedition of José Celestino Mu-
tis, emphasizing the various systems of education with a
focus on agriculture and livestock. Torres [19]
, presents a
reflection relating to higher education with an agricultural
focus in Colombia and the process of creating the Faculty
of Agricultural Sciences in the State of Nariño. Recently,
Junguito et al. [11]
recounted the main problems related to
the productivity and competitiveness of Colombian agri-
culture and have proposed mechanisms for strengthening
research institutions, paying particular attention to the
Colombian Agricultural Research Corporation (AGROSA-
VIA), which has come up as the axis of the national sys-
tem of agricultural science, technology, and innovation.
However, there is a lack of complete studies concern-
ing the evolution of the Colombian agricultural research
system, especially about what happened in the first two
decades of the 21st century. In this regard, Stads et al. [14]
present an analysis of agricultural research institutions in
Latin America and the Caribbean (including Colombia
and Brazil), detailing the structure and financing of their
research systems. However, this work does not highlight
how the better performance of some countries (for exam-
ple, Brazil) can be used as a comparative parameter for
other countries, such as Colombia. Given the above ex-
plained, the contribution of this article is the registration
and analysis of the main historical facts and institutions
that allowed the constitution of the agricultural research
system in Colombia up to the present time. This compara-
tive analysis with the Brazilian system will make it pos-
sible to formulate policy suggestions in Colombia that can
meet the demands of its agricultural sector and guarantee
its future development.
3. A Historical Survey of Agricultural Re-
search in Colombia and Brazil
The Spanish priest José Celestino Mutis, also a natural-
ist and mathematician, dedicated himself, after his arrival

21
in Colombia in 1760, to the recognition and study of the
Andean flora through several scientific excursions that led
to important botanical discoveries. Mutis carried out stud-
ies on zoology and minerals, observed astronomical phe-
nomena, and described the geography of the country [20]
.
During his lifetime in Colombia, Mutis exchanged corre-
spondence with European scholars, especially Mr. Carlos
Linnaeus, seeking better cooperation and exchange of
knowledge between both scholars about the collection and
nomenclature of unknown plants, as well as a scientific
development.
In 1805, Francisco José de Caldas assembled a consid-
erable herbarium of species from the southern and south-
western regions of Colombia, recording his observations
on the geography and distribution of plants in addition
to his contributions to astronomy and physics. This her-
barium was an essential component in the knowledge of
Colombian plant species not only because of its volume
but also because of the descriptions of common uses, es-
pecially in agriculture, industry, and the conservation of
natural resources in the regions where it was collected.
A century later, in 1914, landmarks were set up for the
creation of the School of Tropical Agriculture and Vet-
erinary Medicine in Medellín, and in 1916 its academic
activities began. Due to the country lacked of technicians,
qualified teachers from the United States of America,
Puerto Rico, Cuba, France, and Germany were hired.
Four years later, by ordinance, a complete course in ag-
riculture and veterinary medicine was introduced. With
the emergence of faculties of agronomy and zootechnics
in different Colombian states, there was a great diffusion
of new production techniques for different species, which
promoted the quality of Colombian agricultural products
at the time.
Table 1 displays the main historical milestones in the
process of creating agricultural science research and
education institutions in Colombia. From the 1940s to
the 1960s, several faculties were created to provide un-
dergraduate courses in agricultural sciences. The 1970s
and 1980s were characterized by the creation of several
research centers focused on specific agricultural activities.
Since its creation in 1970, the Faculty of Agricultural Sci-
ences at the Universidad Nacional de Colombia in Palmira
has pointed out among the most relevant public institu-
tions that provide higher education in the country, with
an emphasis on agricultural sciences. This institution has
contributed to the generation and development of research
in agronomy, biotechnology, agricultural innovation, en-
vironment, biodiversity, and zootechnics, not only for the
Valle del Cauca region, which is an important Colombian
agricultural region, but also for the development of other
Andean and Pacific Colombian regions.
Later, with the creation of the Colombian Agricultural
Research Corporation (AGROSAVIA) in 1993, national
public research began to be centered in this institution,
which became responsible for generating scientific knowl-
edge and technological solutions through research, inno-
vation, technology transfer, and the training of research-
ers for the benefit of the Colombian agricultural sector.
Together with the Faculty of Agricultural Sciences of the
Universidad Nacional de Colombia in Palmira and the
International Center for Tropical Agriculture (CIAT), both
placed in the same region, they form the hub of agricul-
tural research in Colombia. In addition, the institutional
framework stimulates the strengthening of the former
National Science and Technology System and its defini-
tion. Law 607, issued in 2000, has modified the creation,
functioning, and operation of the Municipal Agricultural
Technical Assistance Units (UMATA) and regulated direct
rural technical assistance. Those have turned viable, the
participation of the territories in technological activities.
In this path, the implementation of the Strategic Plans for
Science, Technology, and Innovation (PECTIA) formu-
lated for most of the country’s states has been noble.
Brazil, from 1808, when the Rio de Janeiro’s Botani-
cal Garden was inaugurated, to 1973, when EMBRAPA
was founded, has faced several swings between federal
and state institutions in conducting activities linked to the
generation of science (knowledge) and technology (pro-
cesses and products) oriented to the development of Bra-
zilian agriculture. Public agricultural research was greatly
strengthened with the creation of the Agronomic Institute
of Campinas (IAC), an agency of the State of São Paulo
since the beginning of the 20th century, but which was
originally established in 1887 by the Central Government
(at that time it was an Imperial Government) as the Impe-
rial Agronomic Station of Campinas.
In Brazil, the State of São Paulo headed the Brazil-
ian agricultural research from the beginning of the 20th
century until the end of the 1970s. Agronomic Institute
of Campinas (IAC), the Biological Institute (IB), and the
Zootechnical Institute (IZ), which concentrated on Bra-
zilian agricultural research during the first three decades
of the 20th century were later, joined by four other state
institutions (Institute of Agricultural Economics (IEA),
Institute of Food Technology (ITAL), and Institute of
Fisheries and Forestry (IF)) [21]
. The emergence of formal
postgraduate degrees stricto sensu courses, in mid-1960s,
allowed public universities (federal and state) to conduct
an important share of agricultural research in Brazil [3]
.
Brazilian public model of agricultural research fells
strongly on Government funding, which includes the con-

22
struction of buildings, the installation of laboratories, and,
above all, the training of competent researchers’ teams as
well as professors at worldwide highest ranked universi-
ties [16]
. Embrapa, linked to the Ministry of Agriculture,
Livestock, and Supply (MAPA), has been, since its crea-
tion, responsible for generating, by itself or jointly with
other agencies, new agricultural knowledge and technolo-
gies for the country. Along with the creation of Embrapa
Table 1. Landmarks the evolution of agricultural research institutions in Colombia.
Year Landmark
1914 Creation of the School of Tropical Agriculture and Veterinary Medicine in Medellin, later named the National Agricultural Institute
1930 Start of the sugarcane program at the Experimental Station of the Colombian Agricultural Institute (ICA) in Palmira
1934
Creation of the Agricultural Institute of Valle del Cauca (later as Escuela Superior de Agricultura Tropical del Valle del Cauca (ESAT)),
the Experimental Agricultural Farm of Palmira, and the Agricultural Extension Service of the State
1938
The National Agricultural Institute merged with the National University of Colombia, was renamed the National Faculty of Agronomy,
later named the Faculty of Agricultural Sciences, and is currently the Faculty of Agricultural Sciences in Medellin
Creation of the National Coffee Research Center (CENICAFÉ)
1940
Sugar mills-initiated research and experimentation activities and later, starting in 1955, established cooperation agreements with the
ICA sugarcane program
1943 The Faculty of Agronomy is created, linked to the Universidad Popular de Manizales, currently the Universidad de Caldas
1944 ESAT became Faculty of Agronomy of Valle del Cauca
1945
The Faculty of Agronomy of Valle del Cauca was incorporated into the Universidad Industrial del Valle del Cauca and became the
Faculty of Agronomy of the Universidad Industrial del Valle del Cauca
1946
The Faculty of Agronomy of the Universidad Industrial del Valle del Cauca joined the Universidad Nacional de Colombia: Facultad
Nacional de Agronomía - Palmira
Creation of the Faculty of Agronomy of the Universidad de Nariño
1955 Creation of the University of Tolima as a Faculty of Agronomy
1963 Creation of the Faculty of Agronomy of the National University of Colombia in Bogotá
1963 Inauguration of the tropical research center that later became the Marine and Coastal Research Institute (INVEMAR)
1967 Establishment of the International Center for Tropical Agriculture (CIAT) in Palmira
1970
The National Faculty of Agronomy in Palmira becomes the Faculty of Agricultural Sciences, Palmira Campus, of the National
University of Colombia
1974 Creation of the National Corporation for Forestry Research and Development (CNRF)
1977 Creation of the National Sugarcane Research Center (CENICAÑA)
1985 Creation of the Banana Research Center (CENIBANANO)
1986
CIMPA: Research Agreement for the Improvement of Panela, signed between the Governments of Colombia and the Netherlands (Dutch
Cooperation)
1990 Establishment of the National Oil Palm Research Center (CENIPALMA)
1993 Creation of the Colombian Agricultural Research Corporation (CORPOICA), transformed in May 2018 into AGROSAVIA
1993 Creation of the Colombian Center for Aquaculture Research (CENIACUA)
2003
Creation of CENIRED, composed of research and development centers: CENIACUA, CENIBANANO, CENICAFÉ, CENICAÑA,
CENICEL, CENIFLORES, CENIPALMA and CONIF
2004 Creation of the Colombian Center for Innovation in Floriculture (CENIFLORES)
2012 Creation of the Cereal and Vegetable Research Center—CENICEL
2015 Creation of Science, Technology, and Agricultural Innovation Parks, Law 1753 of 2015
Source: Prepared by the authors based on the historical references of each institution.

23
came the stimulus for the creation of other institutions at
the state level in different regions. All together assemble
the National Agricultural Research System (SNPA), cur-
rently in force. The current structure of Brazilian agricul-
tural research is made up of public and private institutions
and a higher education system with an outstanding degree
of experience and performance, which has created a con-
solidated system in Latin America and has provided im-
portant contributions to Brazilian agriculture growth.
4. Main Entities that Carry out Agricultural
Research in Colombia and Brazil
Works such as those by Dossa and Segatto [17]
draw
attention to the four groups of organizations that carry
agricultural research in Brazil: Embrapa (linked to the
Federal Government), state public agencies (in the form
of autarchies and/or state-owned companies), universi-
ties (especially the state-funded universities), and private
companies. This item aims to evaluate the paths by which
these organizations play in Colombia and contrast them
with those that exist in Brazil.
According to Stads et al. [14]
, until 2013, 40% of the
agricultural research carried out in Colombia was done by
state-funded agencies, 20% led by the universities, and
40% by private sector entities and/or mixed-law organi-
zations (private and public). For comparative purposes,
in Brazil, in the same period, this distribution was 73%,
25%, and 2%, respectively. These data already illustrate,
at least, that the aforementioned entities play different
roles in the conduct of agricultural research in the two
countries analyzed (Colombia and Brazil).
4.1 Public Institutions Conducting Agricultural
Research: Agrosavia in Colombia versus Embra-
pa in Brazil
Among the entities with government participation dedi-
cated to agricultural research in Colombia, Agrosavia is
the largest. It is a public, decentralized, non-profit institu-
tion (in a similar mold to Embrapa). Its main function is
the generation of scientific knowledge and the develop-
ment of agricultural technologies through research, ad-
aptation, transfer of technology, and technical assistance.
Agrosavia has 21 research units, of which 13 are centers
and 8 are headquarters located in different agricultural
regions of the country (Table 2). These units carry out
research related to permanent crops (cacao and citrus, for
example), transition and agroindustry crops, fruit trees,
livestock, other crops, vegetables, and aromatic plants.
Table 2. Comparison between AGROSAVIA in Colombia and EMBRAPA in Brazil by number and type of researchers,
research centers, laboratories, portfolio, and social benefit for 2019.
AGROSAVIA EMBRAPA
Year of foundation 1993 1973
Indicators
Total number of researchers 378 2,252
Researchers with Master Degrees 211 236
Researchers with PhD Degrees 143 1,704
Other researchersA
24 312
Research centersB
21 50
Total number of laboratories 49 600
Portfolio 7 34
Social balance sheet
Technologies analyzed 26 160
Developed crops n.d. 220
Corporate shares 4 n.d.
Social profit (USD $, currency in 2020)
Social profit ($USD) $120,449,575.77 $6,695,523,028.94
Source: Prepared by the authors based on Agrosavia 2019 social report [22]
and Embrapa 2019 social report [23]
.
Notes: A
In the case of Agrosavia, professionals linked to research are included; in the case of Embrapa postdoctoral researchers
are included. B
In the case of Agrosavia there are 13 research centers and 8 headquarters; in the case of Embrapa, there are 43
decentralized units and 7 central units.

24
Agrosavia also generates knowledge on the conservation
and sustainable use of biodiversity [20]
.
According to Table 2, researchers with Ph.D. and Mas-
ter’s degrees linked to Agrosavia in 2019 represented 38%
and 56%, respectively, of the total. In 2013, according to
Stads et al. [24]
, the participation of PhDs and Masters in
the same institution was 15% and 17%, respectively. De-
spite the relative improvement in the linkage of high-level
personnel during the last decade, previous years were
characterized by lower paid salaries in the public sector
combined with inefficient job promotion inside the public
research system, which led many scientists to seek other
better-paid positions, even abroad. While the increase
in the number of researchers with doctoral and master’s
degrees has been significant in Colombia over the last
decade, attracting highly qualified researchers in some
priority areas remains a challenge for agricultural research
in Colombia.
In 2019, the social return of the investments in agri-
cultural science and technology in Colombia, considering
the case of Agrosavia, was 2.15 for each monetary unit
invested (1:2.15). That is, for every Colombian peso (COP)
invested, COP$ 2.15 was generated in benefits for the
sector. The total social benefit of Agrosavia in 2019 was
USD$ 120,449,575.77, which comes from the 26 tech-
nologies analyzed, 4 corporate actions, and also includes
plant and animal genetic material, crop management rec-
ommendations, different types of protocols for production,
agricultural designs, agroindustry, and extension [22]
. These
technologies enabled the improvement of production sys-
tems in different regions of the country.
As mentioned, public agricultural research in Brazil is
carried out at the federal and state levels. The Embrapa is
the main federal entity in Brazil, with at least 50 research
centers spread over all regions and a team comprised of
2252 researchers, 76% of whom have PhDs degrees (Table
2). In Brazil, the Dominican Republic, Ecuador, Panama,
and Venezuela, the government sector hired more than
70% of agricultural researchers in each country [14]
. Em-
brapa agricultural research spectrum is also quite broad,
covering at least 34c
knowledge fields. In addition to Em-
brapa’s involvement, most Brazilian states have their own
c They include: irrigated agriculture, food, Amazon biosystem, aquacul-
ture, automation, precision and digital agriculture, advanced biotechnol-
ogy applied to agribusiness, cocoa, coffee, meat, drought in the semi-arid
region, energy, chemistry and biomass, fibers and biomass for industrial
use, forestry, temperate fruit growing, tropical fruit growing, grains,
vegetables, organizational innovation, social innovation in agriculture,
biological inputs, livestock and forestry integration, intelligence systems,
land management and monitoring, milk, rational pesticide management,
climate change, nanotechnology, agricultural nutrition, pastures, genetic
resources, animal health, plant health, environmental services, ecologi-
cally based production systems and Brazilian soils.
agricultural research entities focused on their state reali-
ties. In Brazil, Embrapa (together with state public institu-
tions and public universities) generated knowledge and
technologies for national agriculture, which enabled the
reduction in production costs and helped the country in-
crease the food supply in a sustainable manner, in addition
to reducing the value of the basic food basket by more
than 41.49% [23]
. The social return for each Brazilian mon-
etary unit (Reais) invested in Embrapa in 2019 was R$
12.29 (1:12.29), which came back to the Brazilian society
in the form of technologies, knowledge, and employment.
Embrapa generated in the country, in 2019, a social return
of USD$ 6,695,523,028.94, calculated from the economic
impacts of a sample of 160 technologies and about 220
cultivars developed by the research company and its part-
ners [23]
, showing its high efficiency and consolidation in
the exercise of agricultural research.
4.2 Regional Research and International Cooperation
Colombia has some regional organizations that conduct
agricultural research, and several of them hold coopera-
tion with other organizations inside Latin America and the
Caribbean (LAC). Among them is the Inter-American In-
stitute for Cooperation on Agriculture (IICA), which plays
a useful role in coordinating, promoting, and facilitating
sustainable agricultural development in the region. IICA
works with all the LAC countries as well as with several
centers of the Consultative Group on International Ag-
ricultural Research (CGIAR) and other regional organi-
zations. The CGIAR Consortium conducts most of the
international research in the LAC region. It participates
in agricultural research and development in the region
through three centers, including the International Center
for Tropical Agriculture (CIAT) in Colombia.
At the same time, the Agricultural Research Coopera-
tive Programs (PROCIs) comprise a series of sub-regional
mechanisms made up of a group of national agricultural
research institutes. The PROCIs focus on the development
and strengthening of institutions, the coordination of re-
search projects in several countries, and the promotion and
transfer of technology. Currently, there are four programs
running: PROCISUR (Argentina, Bolivia, Brazil, Chile,
Paraguay, and Uruguay); PROCITROPICOS (Brazil, Bo-
livia, Colombia, Ecuador, Peru, Suriname, and Venezue-
la); PROCIANDINO (Bolivia, Colombia, Ecuador, Peru,
and Venezuela); and PROCICARIBE (Caribbean) [25]
.
On the other hand, the Tropical Agricultural Teaching and
Research Center (CATIE) is an autonomous non-profit
institution focused on agricultural and rural development
and natural resource management. Member states include
Colombia, where research on rural communities and so-

Research on World Agricultural Economy | Vol.4,Iss.2 June 2023

Research on World Agricultural Economy | Vol.4,Iss.2 June 2023

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