Handheld NDVI Sensor-based Rice Productivity Assessment under Combinations of Fertilizer Soil Amendment and Irrigation Water Management in Lower Moshi Irrigation Scheme, Tanzania

Abstract

Handheld Optical Sensor was used to measure canopy reflectance at red region (656 nm) and near-infrared region (774 nm) to generate NDVI data for monitoring rice productivity under soil amendment with combinations of fertilizers at two levels of water regime in smallholder Irrigation Scheme, in Lower Moshi, Tanzania. The study was carried out in an experimental design consisted of two irrigation water levels (flooding and system of rice intensification) with multi-nutrients (NPK) and single nutrient (urea) application replicated three times in a randomized complete block design. Flood irrigation water was applied at 7 cm height throughout the growing season, while SRI treatment irrigation water was applied at 4 cm height under alternate wetting and drying conditions. The annual rate of fertilizers applied was 120 kg N/ha, 20 kg P/ha, and 25 kg K/ha. The variety SARO-5 was used in this experiment. Simple correlation coefficient (r) was used to measure the degree of association between field crop performance parameters (plant height, number of tillers, biomass, yield) and NDVI across growth stages and three positions of the sensor above the canopy in the tested fertiliser combinations and water regimes. Results show that at any given fertiliser combinations and water levels, there was no significant correlation between plant height and NDVI except for the plant height at a vegetative stage for 0.6 m above the crop canopy and booting stage at 0.3 m and 0.6 m above the canopy respectively (p < 0.05). A good correlation was also observed between NDVI at booting and full booting stage regardless of the position of the sensor above the canopy and the number of tillers at full booting growth stage (p < 0.05). A significant relationship was observed between rice grain yield and NDVI at the vegetative, booting, and full booting stage. The simple linear regression models explained only slightly less than 30% of the yield predictions by NDVI at the early stage of the crop growth, decreasing gradually to 5% at the full booting growth stage. Results from this study have demonstrated a positive linear relationship between rice grain yield and NDVI for the tested soil fertiliser amendments and irrigation water regimes. The study conclude that handheld NDVI-based sensor can be used in smallholder rice yield predictions for optimising soil fertiliser use and irrigation water management.

Full text

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Handheld NDVI Sensor-based Rice Productivity
Assessment under Combinations of Fertilizer Soil
Amendment and Irrigation Water Management in
Lower Moshi Irrigation Scheme, Tanzania
Oforo Didas Kimaro (  didas.oforo_kimaro@mailbox.tu-dresden.de )
Technische Universitat Dresden https://orcid.org/0000-0002-8226-133X
Sintayehu Legesse Gebre
KU Leuven: Katholieke Universiteit Leuven
Proches Hieronimo
Sokoine University of Agriculture
Nganga Kihupi
Sokoine University of Agriculture
Karl-Heinz Feger
TU Dresden: Technische Universitat Dresden
Didas N. Kimaro
Mwenge Catholic University
Research Article
Keywords: Handheld NDVI sensor, rice productivity, soil fertiliser use, irrigation water management, Moshi
Tanzania
Posted Date: July 26th, 2022
DOI: https://doi.org/10.21203/rs.3.rs-1601413/v1
License:   This work is licensed under a Creative Commons Attribution 4.0 International License. 
Read Full License

Page 2/26
Abstract
Handheld Optical Sensor was used to measure canopy reectance at red region (656 nm) and near-
infrared region (774 nm) to generate NDVI data for monitoring rice productivity under soil amendment
with combinations of fertilizers at two levels of water regime in smallholder Irrigation Scheme, in Lower
Moshi, Tanzania. The study was carried out in an experimental design consisted of two irrigation water
levels (ooding and system of rice intensication) with multi-nutrients (NPK) and single nutrient (urea)
application replicated three times in a randomized complete block design. Flood irrigation water was
applied at 7 cm height throughout the growing season, while SRI treatment irrigation water was applied at
4 cm height under alternate wetting and drying conditions. The annual rate of fertilizers applied was 120
kg N/ha, 20 kg P/ha, and 25 kg K/ha. The variety SARO-5 was used in this experiment. Simple correlation
coecient (r) was used to measure the degree of association between eld crop performance parameters
(plant height, number of tillers, biomass, yield) and NDVI across growth stages and three positions of the
sensor above the canopy in the tested fertiliser combinations and water regimes. Results show that at
any given fertiliser combinations and water levels, there was no signicant correlation between plant
height and NDVI except for the plant height at a vegetative stage for 0.6 m above the crop canopy and
booting stage at 0.3 m and 0.6 m above the canopy respectively (p < 0.05). A good correlation was also
observed between NDVI at booting and full booting stage regardless of the position of the sensor above
the canopy and the number of tillers at full booting growth stage (p < 0.05). A signicant relationship was
observed between rice grain yield and NDVI at the vegetative, booting, and full booting stage. The simple
linear regression models explained only slightly less than 30% of the yield predictions by NDVI at the early
stage of the crop growth, decreasing gradually to 5% at the full booting growth stage. Results from this
study have demonstrated a positive linear relationship between rice grain yield and NDVI for the tested
soil fertiliser amendments and irrigation water regimes. The study conclude that handheld NDVI-based
sensor can be used in smallholder rice yield predictions for optimising soil fertiliser use and irrigation
water management.
1. Introduction
Grain crop production plays a dynamic role in the economy of Tanzania with the main crops being maize,
rice, beans, sorghum, millet, wheat, cassava, potatoes, bananas, and plantains (URT, 2012). Rice (
Oryza
sativa
L.) is a staple cereal crop that constitutes a major part of the diet for more than 230,000
smallholder households in Tanzania. It has been estimated that more than 60% of Tanzanian
populations do eat rice frequently (Tusekelege et al., 2014). Tanzania is the second-largest rice producer
in the Eastern and Southern Africa region (Drame
et al
., 2014). In 2009/10, the area under rice was
1,136,290 ha having a production of 2,650,120 tons with an average of 2.33 tons/ha (Rural Livelihood
Development Company, 2009; Tusekelege et al., 2014). Rice production in Tanzania is mainly practiced in
semiarid plains (Kanyeka et al., 1994; Hatibu, 1999). However, the semiarid plains of Tanzania are faced
with poor soils, increased risks of soil degradation, soil fertility loss and increased frequency of drought
under changing climate and dwindling water resources (Bell et al., 2015). This situation challenges the

Page 3/26
current agricultural practices and jeopardize the livelihood of smallholder famers households. In these
areas, rains are so erratic that harvest is negligible and people survive on food-for-work.
The agricultural water demand in Tanzania exceeds the available water supplies (Katambara et al.,
2013). For example, in about 70% of subsistence farmers in Usangu Plain in Tanzania, have limited
access to the highly needed water resource for irrigation as well as for maintaining the ecosystem in the
Ruaha National Park and beyond (Kadigi et al., 2007). Likewise, the demand for food to feed the growing
population is increasing which calls for technologies and farming practices to ensure food securities at
the same time reducing agricultural water use (Katambala
et al
., 2013). Rice in Tanzania is produced at
subsistence level and most farmers practice continuous ooding, a practice that uses large amount of
water. Rice water use eciency (WUE) in Tanzania is estimated to be 0.3 kg grain yield/ha land with an
average water demand for a single growing season to be 8000 m3 water/ha land (Michael et al., 2014).
Therefore, it is essential that new systems of rice production should be explored that will increase yields
as well as increase water use eciency in rice. Additionally, such systems should be tailored to
smallholder farmers in that they require relatively low amounts of input such as fertilizers and water. One
such systems currently promoted is the System of Rice Intensication (SRI). The System of rice
intensication (SRI) is an array of practices developed to improve the productivity of rice grown in
paddies. The system was rst developed in Madagascar in 1980s through farmer experimentation by
changing water application techniques, crop spacing and seedling age and later up scaled in other
countries in Africa including East Africa (Katambara et al., 2013). SRI was developed as a rice-cultivation
strategy that may offer an opportunity to increase rice yield with less external inputs in particular the use
of less water in the face of changing climate (Uphoff, 2003).
In East and Southern African countries, and sub-Saharan Africa there is limited application of mineral
fertilizer (Bationo et al., 2012). Continued extraction of nutrients in the form of crop yields and crop
residues, soil erosion, and insucient recycling of nutrients (such as compost or manure) has rendered
the originally fertile soils very low-productive (Thierfelder et al. 2013). The combined average depletion
rate of N, P and K of all SSA countries is 54 kg/ha/yr (Sommer et al., 2013). Thus, nutrient limitation is
the major impediment for increasing yields especially of rice in Africa. Nutrient depletion rates vary
signicantly spatially, depending on the overall crop productivity level and farmer’s access to fertilizer
(Sommer et al., 2013). Therefore, site/region-specic knowledge of the soil fertility levels is thus a
prerequisite for the establishment of protable and sustainable nutrient management systems. According
to Sommer et al. (2013), fertilizer recommendations developed in the past often ignored differences
between soils and practices such as irrigation water regimes and are highly incompatible with
smallholders' resources. Taking into consideration the importance and challenges of rice production in
Tanzania, monitoring and improving rice productivity dominantly under smallholder management
practices including soil fertilizer use and irrigation water management is vital for prompt decisions at
farm level and marketing agencies. Such efforts are also in line with Global Sustainable Development
Goals (SDG’s). Directly, they contribute to increase resilient to climate extremes (SDG 13), (SDG 2)

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(reduce/end hunger, achieve food security & improved nutrition and promote sustainable agriculture) and
(SDG 1) (alleviate/end extreme poverty in all its forms everywhere) (UN, 2016; FAO, 2019).
Several tools have been developed to monitor crop growth and development for improving eld soil-water
management practices and decision-making options. For example, Normalized Difference Vegetation
Index (NDVI) was used to assess N status and predict grain yield in rice in the Sacramento Valley rice
growing area of California, USA. In that study NDVI was measured at the panicle initiation (PI) rice growth
stage to assess crop N status and predicts nal rice grain yield (Rehman et al., 2019). A study was also
conducted in Haryana, India to examine input eciencies focused on combinations of N-fertiliser and
irrigation input in wheat crops grown with four rotations (rice-wheat, cotton–wheat, pearl millet–wheat,
and cluster bean–wheat) (Coventry et al., 2011). The use of the GreenSeeker sensor in that study resulted
in N fertilizer savings of 21–25 kg N ha− 1 with similar grain yield, protein, and grain hardness to that
provided by using the recommended 150 kg N ha− 1. Furthermore, where the GreenSeeker was used the
apparent fertilizer recovery was 70–75% compared with the 60% recovery with the recommended rate. In
another study conducted in Taiwan Agricultural Research Institute in Taiwan, water management with
eld sensors was carried out for water and fertilizer use eciency (Li, G.-S
et al
., 2021). In that study
different irrigation methods and nitrogen fertilizer levels were evaluated. Results of that study indicated
that plant nitrogen and chlorophyll content at the maximum tillering stage were signicantly inuenced
by the interaction between water and fertilizer (Li, G.-S
et al
., 2021). Furthermore, NDVI obtained from the
multispectral images captured by the sensors, correlated well with plant nitrogen content and rice growth
stages.
NDVI sensor (GreenSeeker Hand-Held Optical sensor unit) (Figs.1, 2) is a tool in precision agriculture
technology for providing useful data to monitor the growing status of crops at different soil and fertilizer
management practices and across growth stages (Lan et al., 2009). The multispectral camera of this
sensor was developed mainly for biomass estimation (Inoue et al., 2000; Jones et al. 2007), and the hyper
spectroradiometer for close monitoring of crop conditions including crop stress (drought and soil nutrient
deciency) (Laudien et al., 2003; Darvishzadeh et al., 2008). Many of these sensors and instruments have
been used to measure real-time crop conditions in many countries of the world. Green-Seeker Hand-Held
Optical sensor unit, (Figs.1, 2) has been used as a tool to measure Normalized Difference Vegetation
Index (NDVI) above the canopy of wheat at 50 cm height across different growth stages during the
season for yield estimation in Faisalabad, Pakistan (Sultana et al., 2014). The potential of NDVI to
differentiate wheat cultivars for grain yield under different nitrogen levels was demonstrated under
agroclimatic conditions of Pakistan.
Prediction of dry direct-seeded rice (DDSR) yields using Chlorophyll Meter (SPAD), Leaf Colour Chart
(LCC), and Green SeekerHand-Held optical sensor was conducted in north-western India (Ali et al., 2014).
Results revealed that the yield of DDSR can be satisfactorily predicted by the sensors. The NDVI readings
measured by Green SeekerHand-Held optical sensor were superior to SPAD meter readings. In Kenya and
Zimbabwe, it was possible to forecast maize yield using NDVI data derived from images acquired by the
SPOT VEGETATION sensor (Lewis et al., 1998; Kuri et al., 2014). However, in those studies it was observed

Page 5/26
that the use of handheld sensors is advantageous over remotely sensed imagery which can also detect N
variability and forecast yield in crops but have some limitations such as the timeliness in which the
imagery is acquired. Hand-held active remote sensing devices like Green-Seeker Hand-Held optical
sensors may overcome these limitations. The use of such sensors in tropical Sub-Saharan Africa (SSA)
and particularly in Tanzania, is lagging behind. In this study, the GreenSeeker Hand-Held NDVI-based
sensor, was used for monitoring rice productivity under soil amendment with combinations of fertilizers
at two levels of irrigation water in the semi-arid plains of Tanzania. Specically, the objective of the study
was to assess rice productivity using handheld NDVI-based sensor under the condition of smallholder
irrigated rice farming for optimising soil fertiliser use and irrigation water management.
2. Methodology
2.1. Study area
The experimental study was carried out at the Lower Moshi irrigation scheme in Mabogini village, Moshi
District, NE Tanzania. There, farmers practice rice farming under inadequate rainfall and scarce water for
irrigation. The study area is located between Universal Transverse Mercator (UTM) coordinates 314996
and 320988 E and 9619988 and 9626979 N, UTM Zone 37 M (Fig.3). The Lower Moshi irrigation scheme
is located in the semi-arid lowland plains lying below 740 m asl and receiving an annual rainfall of < 800
mm. The study area is located on a uvial-volcanic plain of low relief stretching out of the foot of
Mountain Kilimanjaro. Rau River is the main source of irrigation water. Present land uses practiced
include irrigated rice farming; rain-fed maize farming with supplemental irrigation, and light grazing.
2.2. Experimental design and treatments
The experimental design consisted of two irrigation water levels (Flooding and System of Rice
Intensication (SRI)) with nutrient management (1) multi nutrients (NPK) and (2) single nutrient (urea)
application) replicated three times (each Replication plot measuring 8 m by 20 m) in a Randomized
Complete Block Design (RCBD). Flood Irrigation water was applied at 7 cm height (equivalent to the total
water volume of 11,900 m3/ha) throughout the growing season, while under SRI treatment irrigation
water was applied at 4 cm height (equivalent to the total water volume of 6,750 m3/ha) under alternate
wetting and drying conditions (AWD). Water was applied to the plots at seven days intervals. The rate of
fertilizers applied were 120 kg N/ha, 20 kg P/ha, and 25 kg K/ha. A multi-nutrient treatment received all
three nutrients (NPK), while in a single nutrient treatment only N was applied as urea. An improved rice
variety named SARO-5 was used in this experiment.
Treatments:
I = Flood irrigation water – no control of the height of ooded water at 7 cm height + farmers
recommended soil fertility management (120 kg N/ha as urea only) (CU)

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II = SRI – low irrigation water (controlled height of ooded water) + farmers recommended soil fertility
management (120 kg N/ha as urea only) (SU)
III = SRI – low irrigation water (controlled height of ooded water at 4 cm height) + NPK (SN)
IV = Flood irrigation water – no control of the height of ooded water at 7 cm height + NPK (CN)
2.3. GreenSeeker™ Hand-Held optical NDVI sensor data
collection
Near-Infrared (NIR)-reectance measuring “GreenSeeker” handheld sensor was used for online monitoring
of rice growth at the experimental site. Optical sensor readings were taken with a handheld GreenSeeker™
sensor (NTech Industries Inc., Ukiah, CA, USA). The sensor measured canopy reectance at the red region
(656 nm) and near-infrared (NIR) region (774 nm) to generate NDVI data. NDVI was determined as shown
in Eq. 1 (Ali et al., 2014). The sensor was passed over the crop at a height of 0.3 m, 0.6 m, and 0.8 m
across growth stages (early, vegetative, booting, and full booting development stages) during the season.
The readings were recorded every 14-d interval starting from 14 d after transplanting (DAT) and continued
up to the full booting stage before owering started. Five healthy plants were selected systematically
from each treatment plot on which measurements of NDVI were determined.
NDVI
= (
NIR
−
Red
) / (
NIR
+
Red
)
…………………....…..…………………..1
The sensor takes readings at a very high rate (approximately 1000 measurements per second) and
averages measurements between readings. In that way, the sensor automatically calculates average
NDVI readings for each measured sample sequence. The NDVI data from the sensor is then transmitted
serially to an HP iPAQ Personal Digital Assistant which was later exported to a LAPTOP computer for
analysis.
2.4 Measurements
Plant height and number of tillers
Five healthy plants were selected systematically from each treatment plot on which measurements of
plant height and number of tillers were done. The plant height was measured at every 14 days interval
starting from 14 days after transplanting (DAT) and continued up to full booting stage before owering
started. Plant height was determined by measuring the distance from the soil surface to the tip of the leaf
before heading and to the tip of the ag leaf after heading. Number of tillers per hill was counted at every
14 days interval starting from 14 DAT (early stage) and continued up to full booting stage before
owering started. The collected data were entered in a specially designed form for further analysis.
Biomass (AGB)

Page 7/26
Five hills from each treatment plot were uprooted and left dried for approximately three weeks from which
the dry matter weight was determined. Dry biomass (oven dried for 24 hours or more until no change in
weight) of different plant organs (stem, leaves and panicles) were also weighed.
Thousand Grain weight (TGW)
Thousand grains were counted from the grain yield of each treatment unit based on two observations (e,
g 00.0g) and weighed by a portable automatic electric balance after oven-drying at 700c for 24 hours in
an oven until a constant weight was obtained.
2.5. Data analysis
Data analysis was carried out in GENSTAT64 software, 15th edition to test the applicability of the sensor.
Analysis of Variance (ANOVA) was used to compare statistically the means between variables as per
randomized complete block design (RCBD) with the treatments. The means were separated by using
Least Signicant Difference (LSD) at alpha = 0.05. Simple Correlation coecient (r) was used to measure
the degree of association between eld crop parameters (plant height, number of tillers, thousand-grain
weight (TWG), biomass, number of panicles, yield) and NDVI at the four treatments (management
practices) and across growth stages. In addition, a simple linear regression model (coecient of
determination R2) was used to establish the prediction eciency of the sensor.
3. Results And Discussion
3.1 Plant height across crop growth stages and management practices
Results of plant height parameter across crop growth stages and management practices are presented in
Table 1. Plant height varied from 21.07 cm to 80.5 cm across growth stages and management practices
for the whole dataset though statistically were not different. Plant height across growth stage at SRI with
urea fertilizer was higher than in the other management practices although not signicant. These results
are in agreement with earlier observation by Aide and Beighly (2006) who noted that plant height was
signicantly correlated with nitrogen fertilizer application. Similar results were obtained by Islam (2008)
who noted that plant height was signicantly inuenced by the amount of fertilizers applied. The increase
in plant height could also be explained by the application of increased levels of nitrogen which might be
associated with stimulating effect of nitrogen on various physiological processes including cell division
and cell elongation of the plant. Materu (2014) observed that plant height for 100% SRI was signicantly
(p< 0.05) higher than 80% and 50% SRI (at reduced SRI water level). In Bihar, India, similar observation
was reported by Chowdhury
et al.
(2014) that plant height of rice crop increased with increasing irrigation
and levels of nutrients. Increasing nutrient levels has direct inuence on increasing the uptake of N which
in turn might have increased the plant height.
Table 1: Plant height as inuenced by water regime and nutrient management at different rice growth
stages

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Treatments H1(Early
stage)
cm
H2 (Vegetative
stage)cm H3 (Booting
stage) cm H4 (Full booting
stage) cm
Farmers Practice 24.57NS 55.6NS 64.9NS 77.3NS
SRI + Urea 26.90NS 56.3NS 67.3NS 80.5NS
Flooding + Urea 24.97NS 55.4NS 61.5NS 75.7NS
SRI + NPK 25.33NS 56.7NS 66.5NS 74.9NS
Flooding + NPK 21.07NS 54.0NS 64.1NS 78.1NS
PROBABILITY
(P<0.05) 0.112 0.871 0.593 0.675
NS: Not signicant
H1= Plant height at early stage; H2=Plant height at vegetative stage; H3=Plant height at booting stage;
H4=Plant height at full booting stage
3.2 Correlation Coecient between Plant height and NDVI score at Various Growth Stages
Table 2 presents the simple correlation between rice plant height and NDVI scores across growth stages
and at three positions of the sensor (0.3 m, 0.6m, and 0.8 m) above the crop canopy. Generally, no
signicant association was observed between plant height and NDVI except for the plant height at a
vegetative stage for 0.6 m above the crop canopy and booting stage at 0.3 m and 0.6 m above the
canopy respectively (Table 2). These results disagree with other results by Wijesingha
et.al
. (2015) who
observed a direct relationship between rice plant height and MODIS NDVI in Sa kaeo province, Thailand.
Furthermore, Rahetlah
et al
. (2014) observed a moderate relationship between NDVI derived from SPOT5
satellite image and height of Elephant grass (R² = 0.74; P < 0.001)in theVakinankaratra region,
Madagascar. Laboratory evaluation of the GreenSeeker™ hand-held optical sensor using corn as a test
crop in Texas, USA, showed that the sensor is highly sensitive (
P
< 0.0001) to the positions ranging from
30.5 cm to 91.5 cm above the crop canopy (Martin
et al
., 2012). In Tennessee, USA, a strong positive
correlation (r > 0.72) between NDVI and plant height of cotton was observed (Marisol, 2010). In this study,
it is apparent that studies to evaluate the relationship between rice plant height and sensor measured
NDVI is lacking. These results suggest that the evaluated GreenSeeker sensor requires further testing to
assess the general rice plant height performance over a wide range of conditions.
Table 2: Correlation coecient between plant height and NDVI score at various growth stages

Page 9/26
NDVI at various growth stages H1 H2 
H3

H4
ND-11_earlystg 0.06 NS 0.39 NS 0.38 NS 0.56 NS
ND-21_vegtv stg 0.29 NS 0.19 NS 0.25 NS 0.52 NS
ND-22_vegtv stg 0.67* 0.59 NS 0.47 NS 0.40 NS
ND-23_vegtv stg 0.49 NS 0.36 NS 0.39 NS 0.41 NS
ND-31_Bootng stg 0.36 NS 0.35 NS 0.25 NS 0.78*
ND-32_Bootng stg 0.22 NS 0.74* 0.10 NS 0.61 NS
ND-33_Bootng stg 0.51 NS 0.58 NS 0.32 NS 0.23 NS
ND-41_Full btng stg 0.31 NS 0.51 NS 0.31 NS 0.62 NS
ND-42_Full btng stg 0.30 NS 0.58 NS 0.11 NS 0.63 NS
ND-43_Full btng stg 0.19 NS 0.27 NS 0.08 NS 0.61 NS
* Signicant at 5% level; NS: Not signicant; n = 10
H1= Plant height at early stage; H2= Plant height at vegetative stage; H3= Plant height at booting stage;
H4= Plant height at full booting stage; ND-11= NDVI at early stage (sensor 0.3 m above the canopy); ND-
21= NDVI at vegetative stage (sensor 0.3 m above the canopy); ND-22= NDVI at vegetative stage (sensor
0.6 m above the canopy); ND-23= NDVI at vegetative stage (sensor 1.0 m above the canopy); ND-31=
NDVI at booting  stage (sensor 0.3 m above the canopy); ND-32= NDVI at booting stage(sensor 0.6 m
above the canopy); ND-33= NDVI at booting stage (sensor 1.0 m above the canopy); ND-41= NDVI at full
booting stage (sensor 0.3 m above the canopy); ND-42= NDVI at full booting stage(sensor 0.6 m above
the canopy); ND-43= NDVI at full booting stage (sensor 1.0 m above the canopy).
3.3 Number of tillers across rice growth stages and management practices
Number of tillers across rice growth stages and management practices are presented in Table 3. Results
show that number of tillers for SRI with NPK was signicantly higher (p < 0.05) than rest of the four
treatments followed by SRI with urea (Table 3). The higher number of tillers per hill for SRI with NPK is an
indicator of improved potential yield associated with the applied management practices (treatments).
This could be attributed to the fact that the tillering system is determined both by variety, management
practices and the nitrogen level in the soil (Ceesay, 2004; Laghari, 2010)
Table 3: Number of tillers as inuenced by water regime and nutrient management across different rice
growth stages

Page 10/26
Treatments T2 T3 T4
Farmers Practice 14.33 ab 18.93 abc 16.87 abc
SRI + Urea 21.00 a 19.33 ab 19.33 ab
Flooding + Urea 11.33 b 14.00 c 15.33 c
SRI + NPK 19.67 a 21.33 a 21.00 a
Flooding + NPK 13.67 b 16.67 bc 15.67 bc
 LSD5% =5.597 LSD5% =4.455 LSD5% = 2.395
Means not sharing any two letters differ signicantly at (𝑃≤ 0.05)
T2=Number of tillers at vegetative stage; T3=Number of tillers at booting stage; T4=Number of tillers at
full booting stage, SRI = System Rice Intensication, NPK = Nitrogen-phosphorus-potassium (22-6-12)
fertilizer
This is to say that if there is more nitrogen in the plant tissues it would likely promote active tillering.
Generally, rice yield is a function of the leaf area and the panicles attached to the tillers (Materu, 2014).
Therefore, number of tillers are usually an indicator of rice yield and hence the higher the number of tillers
the likelihood for increased rice yield. Several studies have reported the benet of SRI technique in
enhancing number of tillers and yield (Li, 2001; Sato and Uphoff (2007); Mati,
et al
., 2011; and Katambara
et al.
(2013)). The SRI promotes soil aeration, healthier root systems and benecial microbial activities
which enhance tillering while at the same time conserving water (Pandian, 2010; Ndiiri
et al
., 2012). In a
study conducted in Hunan, China, Badshah
et al.
(2014) reported a positive correlation between number
of tillers and panicles which was also correlated with grain yield under a combination of conventional
tillage and transplanting of rice seedlings when compared to direct seeding. Tillering is an important
characteristic for grain production and therefore an important crop performance parameter of rice growth
and development.
3.4 Correlation coecient betweennumber of tillersand NDVI score at various growth stages and height
above the canopy
Table 4 presents the simple correlation coecients between crop tillering and NDVI scores. It is apparent
that there is a good correlation between NDVI at booting and full booting stage regardless of the position
of the sensor above the canopy and the number of tillers at full booting growth stage (Table 4). Therefore,
GreenSeeker measured NDVI as a good indicator of crop performance with respect to the number of tillers
at the booting stage. A close relationship was also observed between NDVI at booting (0.3 m above the
canopy) and full booting (0.8 m above the canopy) stage and the number of tillers at the vegetative
growth stage (Table 4). Results show further that there was no correlation between NDVI and the number
of tillers at the booting growth stage regardless of the position of the sensor above the canopy.

Page 11/26
Table 4: Correlation coecient between number of tillers and NDVI score at various growth stages
NDVI at various growth stages T1 T2 T3 T4
ND-11_earlystg 0.02 NS 0.10 NS 0.14 NS 0.01 NS
ND-21_vegtv stg 0.06 NS 0.21 NS 0.08 NS 0.33 NS
ND-22_vegtv stg 0.13 NS 0.10 NS 0.13 NS 0.52*
ND-23_vegtv stg 0.11 NS 0.23 NS 0.02 NS 0.23 NS
ND-31_Bootng stg 0.35 NS 0.53* 0.06 NS 0.71*
ND-32_Bootng stg 0.27 NS 0.47 NS 0.04 NS 0.8*
ND-33_Bootng stg 0.51* 0.38 NS 0.08 NS 0.52*
ND-41_Full btng stg 0.22 NS 0.45 NS 0.29 NS 0.89*
ND-42_Full btng stg 0.40 NS 0.45 NS 0.34 NS 0.67*
ND-43_Full btng stg 0.37 NS 0.56* 0.42 NS 0.86*
* Signicant at 5% level; NS: Not signicant; n = 10
T1= Number of tillers at early stage; T2= Number of tillers at vegetative stage; T3= Number of tillers at
booting stage; T4= Number of tillers at full booting stage; ND-11= NDVI at early stage (sensor 0.3m
above the canopy); ND-21= NDVI at vegetative stage (sensor 0.3m above the canopy); ND-22= NDVI at
vegetative stage(sensor 0.6m above the canopy); ND-23= NDVI at vegetative stage (sensor 1.0m above
the canopy); ND-31= NDVI at booting  stage(sensor 0.3m above the canopy); ND-32= NDVI at booting
stage(sensor 0.6m above the canopy); ND-33= NDVI at booting stage(sensor 1.0m above the canopy);
ND-41= NDVI at full booting stage(sensor 0.3m above the canopy); ND-42= NDVI at full booting
stage(sensor 0.6m above the canopy); ND-43= NDVI at full booting stage(sensor 1.0m above the
canopy)
3.5 Biomass
Table 5 presents the above ground biomass (AGB) across different management practices. The AGB
varied from 55.15 g/hill to 103.88 g/hill across different management practices (Table 5). Results show
that above ground biomass for SRI with NPK and SRI with UREA was signicantly higher (p < 0.05) than
rest of the treatments (Table 5). These results are in agreement with earlier studies. For example, in
Madagascar, Uphoff (1999) observed positive response in terms of plant height and biomass production
with the application of recommended nitrogen fertilizer levels over farmers practices. Ceesay (2004)
observed similar results in the study carried out in Gambia West Africa. Barison (2002) reported enhanced
crop growth and biomass production of rice in Madagascar attributed to SRI and compost manure. Also

Page 12/26
in Madagascar, greater biomass (165.27 g/hill) was obtained from a combination of 50 % poultry
manure/FYM and RDN fertilizer with SRI (Prabhakara Setty
et al
., 2007).
Table 5: Variations in rice biomass across different management practices
Treatments BIOMASS (g/hill)
Farmers Practice 66.3abc
SRI + Urea 103.88a
Flooding + Urea 55.15c
SRI + NPK 96.75ab
Flooding + NPK 63.19c
 LSD5% = 25.72; (p<0,05)=0.036
Means not sharing any two letters differ signicantly at (𝑃 ≤ 0.05)
3.6 Correlation coecient betweenbiomassand NDVI score at various growth stages
Table 6 presents the simple correlation coecients between the above-ground biomass (AGB) and NDVI
across various growth stages. A good relationship was observed between biomass and NDVI at booting
(0.3 m and 0.6 m above the canopy) and full booting stage (0.8 m above the canopy). These results
concur with earlier studies that reported a good correlation between NDVI and above-ground biomass
(Verhulst
et al.
, 2009; Li
et al
., 2010). For example, in the subtropical highlands of Central Mexico Verhulst
et al
. (2009) observed a close correlation between NDVI and biomass of maize. Similar results were
reported by Li
et al
. (2010) who correlated GreenSeeker NDVI with biomass of winter wheat (
Triticum
aestivum
). The results are also in agreement with a study by Liu and Kogan (2002) who reported a close
relationship between NOAA/AVHRR measured NDVI and biomass of soybean in Brazil.
Biomass assessment is thus essential not only for studies which monitor crop growth but also in cereal
breeding programs as a complementary selection tool (Araus
et al
., 2009). Tracking changes in biomass
may also be a way to detect and quantify the effect of stresses on the crop, since stress may accelerate
the senescence of leaves, affecting leaf expansion (Royo
et al
., 2004) and plant growth (Villegas
et al
.,
2001). The measurement of spectral reectance characteristics of crop canopies is largely proposed as a
quick, cheap, reliable and non-destructive method for estimating plant above-ground biomass production
in small-grain cereals (Aparicio
et al
., 2002) and individual plant level (Álvaro
et al
., 2007). Near-infrared
(NIR) reectance of rice is directly related to green biomass (Niel and McVicar, 2001). High NDVI values
are indicative of high chlorophyll content. Chlorophyll is the most important part of the rice plant for
photosynthetic activity, which produces carbohydrates to form rice plant tissue and rice grain, and thus
has a signicant effect on the crop yield.It is also vivid from our study that the GreenSeeker sensor for
monitoring rice crop growth parameters like biomass has not been addressed widely.Therefore, it is

Page 13/26
important to point out that results obtained from the initial stage for further evaluation of the sensor in a
wide range of conditions.
Table 6: Correlation coecient between biomass and NDVI score at various growth stages.
NDVI at different growth development stages Biomass
ND-11_earlystg 0.07 NS
ND-21_vegtv stg 0.25 NS
ND-22_vegtv stg 0.15 NS
ND-23_vegtv stg 0.06 NS
ND-31_Bootng stg 0.92 *
ND-32_Bootng stg 0.81 *
ND-33_Bootng stg 0.17 NS
ND-41_Full btng stg 0.31 NS
ND-42_Full btng stg 0.31 NS
ND-43_Full btng stg 0.58 *
* Signicant at 5% level; NS: Not signicant; n = 10
ND-11= NDVI at early stage (sensor 0.3m above the canopy); ND-21= NDVI at vegetative stage (sensor
0.3m above the canopy); ND-22= NDVI at vegetative stage(sensor 0.6m above the canopy); ND-23=
NDVI at vegetative stage (sensor 1.0m above the canopy); ND-31= NDVI at booting stage(sensor 0.3m
above the canopy); ND-32= NDVI at booting stage(sensor 0.6m above the canopy); ND-33= NDVI at
booting stage(sensor 1.0m above the canopy); ND-41= NDVI at full booting stage(sensor 0.3m above the
canopy); ND-42= NDVI at full booting stage(sensor 0.6m above the canopy); ND-43= NDVI at full booting
stage(sensor 1.0m above the canopy)
3.7 Thousand Grain weight yield
Table 7 presents the Thousand Grain weight (TGW) across different management practices. Results
show that the TGW varied from 31.33 to 32.33 across different management practices (Table 7). Results
show further that SRI with NPK was relatively higher than SRI with Urea and also with other treatments
though not signicant (p < 0.05). Similarly, the treatment with ooding and NPK combination registered
higher yield than ooding with urea also though not signicant (p < 0.05).
Table 7: Variations in Thousand Grain weight (TGW) across different management practices

Page 14/26
Treatments % 1000 Grain weight
SRI + Urea 32.20a
Flooding + Urea 31.33a
SRI + NPK 32.33a
Flooding + NPK 31.93a
 LSD5% = 4.658; (p<0,05)=0.952
Means not sharing any two letters differ signicantly at (𝑃≤ 0.05)
Therefore, results presented on Table 7 shows that SRI and NPK insemination had relatively increased the
thousand-grain weight to a certain level. These results were in line with the ndings of Mohsan, (1999);
Hossain
et al
. 2008 and Hashmi, (2013) who reported that application of nitrogen fertilizers in rice
farming under good water management had positive effect on the thousand grain weight. Hence,
thousand grain weight is also a yield component that can be used for monitoring crop performance when
different management practices are applied like SRI and nutrient management. Another study by Malik
(2010) inKaror District Layyah, Pakistandemonstrated that application of NPK fertilizer at the rate of
175-150-125 Kg ha-1 gave better rice crop growth and higher grain yield 5168 Kg ha-1 when compared to
the other treatments (75-50-125, 100-75-50, 125-100-75, 150-125-100 and 200-150-125 NPK Kg ha-1)
which produced 33.26g, 38.90g, 41.35g, 44.31g and 44.04g of thousand grain weight respectively.
3.8 Correlation Coecient between Grain Yield and NDVI score at Different Growth Stages
Table 8 presents the simple correlations between rice grain yield and NDVI scores across various growth
stages.A signicant relationship was observed between rice grain yield and NDVI at vegetative, booting,
and full booting stage. Obviously, there is also aclose relationship between biomass and NDVI at the
booting and full booting stage, regardless of the position of the sensor above the canopy (Table 8).NDVI
has been correlated with many plant parameters including biomass, plant height, and number tillers,
which are also closely related to crop yield (cf. Wiegand
et al
., 1990; Verma
et al
., 1998). Ali
et al
. (2014)
reported a good correlation between GreenSeeker measured NDVI and grain yield of Dry Direct Seeded
Rice (DDSR) in India. Similar results were reported by Sultana
et al
. (2014) in Faisalabadin, Pakistan
where NDVI was positively correlated with grain yield at stem elongation (r = 0.888), booting (r = 0.950),
and maturity stage (r = 0.927). Although many studies have demonstrated a good correlation between
GreenSeeker measured NDVI and crop yield, such studies have not been done widely in tropical Sub-
Saharan Africa (Teboh
et al
., 2012). It is obvious from the results of this study that wide testing and
evaluation of the GreenSeeker Handheld Optical NDVI sensor is of paramount importance.
Table 8: Correlation coecients between grain yield and NDVI score at various growth stages.

Page 15/26
NDVI at different growth development stages Grain yield
ND-11_earlystg 0.05 NS
ND-21_vegtv stg 0.25 NS
ND-22_vegtv stg 0.94*
ND-23_vegtv stg 0.50*
ND-31_Bootng stg 0.38 NS
ND-32_Bootng stg 0.39 NS
ND-33_Bootng stg 0.82*
ND-41_Full btng stg 0.86*
ND-42_Full btng stg 0.41 NS
ND-43_Full btng stg 0.49*
* Signicant at 5% level; NS: Not signicant; n = 10
ND-11= NDVI at early stage (sensor 0.3m above the canopy); ND-21= NDVI at vegetative stage (sensor
0.3m above the canopy); ND-22= NDVI at vegetative stage(sensor 0.6m above the canopy); ND-23=
NDVI at vegetative stage (sensor 1.0m above the canopy); ND-31= NDVI at booting stage(sensor 0.3m
above the canopy); ND-32= NDVI at booting stage(sensor 0.6m above the canopy); ND-33= NDVI at
booting stage(sensor 1.0m above the canopy); ND-41= NDVI at full booting stage(sensor 0.3m above the
canopy); ND-42= NDVI at full booting stage(sensor 0.6m above the canopy); ND-43= NDVI at full booting
stage(sensor 1.0m above the canopy)
3.9NDVI trend across rice crop growth stages
The relationships between grain yield and NDVI score at various rice crop growth stages are presented in
Fig 4. The results show a positive linear relationship between rice grain yield and NDVI. NDVI at early
stage predict higher yield (R2=26%) > vegetative (R2=10%) > booting (R2=6%) > full booting stage (R2=5%)
(Fig. 4). The simple linear regression models demonstrated in this study explained only slightly less than
30% of the yield predictions by NDVI at the early stage of the crop growth, decreasing gradually to 5% at
full booting growth stage. Sawasawa (2003) reported similar results for rice irrigated elds in India. In
that study NDVI derived from space-born satellite data explained only 25% (R2) of the yield variability at
the eld level, while land and management factors accounted for 38% (R2). The study emphasized that
not all the factors that affected yield also affected NDVI. Gat
et al
. (2000) echoed that remotely sensed
data (NDVI) could be used to estimate yield in various crops. However, the authors suggested that more
effective indicators are needed that capture more of the factors that affect crop development and that
such parameters can be easily captured by remote sensing data and/or sensors (Gat
et al
., 2000; Rajak
et
al
., 2002; Ray
et al
., 2002).

Page 16/26
4. Conclusions
The plant parameters monitored in this study have demonstrated good relationship with biomass and
grain yield, hence, important plant yield components for monitoring rice crop performance when different
management practices are applied like SRI and nutrients. This suggest that rice grain yield, biomass and
number of tillers which are important eld measured plant parameters can be used for monitoring rice
crop growth at different smallholder land management practices across growth stages. The eld
measured rice performance parameters in this study within the available time and material resources
considered only one improved rice variety (SARO 5). Therefore, further research are required to investigate
other rice varieties/cultivars such as TXD 88,
Komboka
, IR 64, IR 56,
Tai
(Thailand) and NERICA common
in smallholder rice farming system in Tanzania. Response of the measured plant performance
parameters under diffrenet levels of fertilizer like nitrogen and phosphorus and the effect on their
interactions should also be investigated in the future studies.
The Green-Seeker Hand-Held optical sensor evaluated in this study has demonstrated the potential to
detect crop performance under varied smallholder management practices and forecast yield under the
conditions of the tropical Sub-Saharan Africa. The study has also demonstrated that GreenSeeker Hand-
Held Optical NDVI sensor can precisely forecast and monitor production trends of irrigated rice and
improved management practices (soil amendment using fertilisers) under smallholder irrigation farming
conditions. Therefore, if the technology is adapted in the agricultural irrigation schemes is likely to
enhance smallholder rice production in semi-arid plains of Tanzania. It is important to point out that
results from this study calls for wide testing and evaluation of the GreenSeeker Handheld Optical NDVI
sensor in a wide range of smallholder farming conditions.
Declarations
Acknowledgment
The authors appreciate nancial support by USAID through the Innovative Agriculture Research Initiative
(iAGRI) in Tanzania. It is a long-term collaboration between the Sokoine University of Agriculture, Ohio
State University, and the Ministry of Agriculture, Food Security, and Cooperatives in Tanzania. The authors
appreciate mentioning of the farmers of Lower Moshi Irrigation Scheme, Staff and management of
Kilimanjaro Agricultural Training Centre (KATC) of the Ministry of Agriculture Food Security and
Cooperatives (MAFC), District Executive Director, Moshi Rural District Council, Lower Moshi Irrigators
Association (LOMIA), Mr. Fred Mawolle from KATC Mabogini and the whole team of extension staff for
their devotion and hard working.
Author Contributions
Conceptualization: O.D.K., N.K., P.H., D.N.K., and K.H.F.; methodology. O.D.K., N.K.,P.H.,D.N.K.,S.L.G., and
K.H.F., validation, O.D.K., N.K.,P.H.,D.N.K.,S.L.G., and K.H.F.; formal analysis, O.D.K., N.K.,P.H.,D.N.K., and

Page 17/26
K.H.F.,; investigation, O.D.K., N.K., P.H., D.N.K. and K.H.F.; data curation, O.D.K., N.K.,P.H.,D.N.K., and K.H.F.;
writing—original draft preparation, O.D.K., N.K.,P.H.,D.N.K.,S.L.G., and K.H.F.,; writing—reviewing and editing,
O.D.K., N.K.,P.H.,D.N.K.,S.L.G., and K.H.F.; visualization, O.D.K and S.L.G.; All authors have read and agreed
to the published version of the manuscript.
References
1. Aide M, Beighley D (2006) Hyperspectral reectance monitoring of rice varieties grown under
different nitrogen regimes. Trans Missouri Acad Sci 40:6–11
2. Ali AM, Varinderpal-Singh., Thind HS, Sharma S (2014) Prediction of dry direct-seeded rice yields
using chlorophyll meter, leaf color chart and GreenSeeker optical sensor in northwestern India
Field
Crops Res
. http://dx.doi.org/10.1016/j.fcr.2014.03.001
3. Álvaro F, García D, Moral LF, Royo C (2007) Usefulness of remote sensing for the assessment of
growth traits in individual cereal plants grown in the eld. Int J Remote Sens 28:2497–2512
4. Aparicio N, Villegas D, Araus JL, Casadesu J, Royo C (2002) Relationship between growth traits and
spectral reectance indices in durum wheat.
Crop Science
42, 1547–1555
5. Baez-Gonzalez AD, Chen P, Tiscareno-Lopez M, Srinivasan R (2002) Using satellite and eld data with
crop growth modelling to monitor and estimate corn yield in Mexico. J Crop Sci 42:1943–1949
. Barison J (2002) Evaluation of nutrient uptake and nutrient- use eciency of SRI and conventional
rice cultivation methods in Madagascar. In:
Assessments of the System of Rice Intensication (SRI)
:
Proceedings of an International conference held in Sanya, China, April 1–4, 143–147
7. Bationo A, Hartemink A, Lungu O, Naimi M, Okoth P, Smaling E, Thiombiano L, Waswa B (2012)
Knowing the African soils to improve fertilizer recommendations. In Kihara, J. (eds.) Improving Soil
Fertility Recommendations in Africa using the Decision Support System for Agrotechnology Transfer
(DSSAT). Springer Dordrecht pp19-42
. Bell P, Kimaro D, Lal R (2015) Agricultural drought analysis for sustainable smallholder maize
production in semiarid areas: A case study of the Lower Moshi Irrigation Scheme, Tanzania. Tanzan
J Agric Sci 14:38–46
9. Cao Q, Miao Y, Wang H, Huang S, Cheng S, Khosla R, Jiamg R (2013) Non-destructive estimation of
rice plant nitrogen status with Crop Circle multispectral active canopy sensor. Field Crops Research
154:133–144
10. Ceesay MM (2004) Management of rice production systems to increase productivity in the Gambia,
West Africa. Dissertation for Award of Ph.D. Degree at faculty of the Graduate School Cornell
University, Ithaca, New York, 159pp
11. Chowdhury MDR, Kumar V, Sattar C, Brahmachari K (2014) Studies on the water use eciency and
nutrient uptake by rice under system of intensication. Int Q J life Sci 9(1):85–88
12. Coventry DR, Yadav A, Poswal RS, Sharma RK, Gupta RK, Chhokar RS, Gill SC, Kumar V, Kumar A,
Mehta A, Kleemann SGL, Cummins JA (2011) Irrigation and nitrogen scheduling as a requirement for

Page 18/26
optimising wheat yield and quality in Haryana, India. Field Crops Research 123:80–88
13. Darvishzadeh R, Skidmore A, Schlerf M, Atzberger C, Corsi F, Cho M (2008) LAI and chlorophyll
estimation for a heterogeneous grassland using hyperspectral measurements. ISPRS J
Photogrammetry Remote Sens 63:409–426
14. Dramé KN, Ndunguru J, Kashenge S, Gabriel H, Nkori JM (2014) Genotyping of Tanzanian rice
genotypes. In: Proceedings of the Eastern Africa Agricultural Productivity Project (EAAPP) Rice
Scientic Conference. 15–18 July 2014, Morogoro, Tanzania. Pg. 3
15. Elliott GA, Regan KL (1993) Use of reectance measurements to estimate early cereal biomass
production on sand plain soils. Aust J Exp Agric 33:179–183
1. FAO (2019) Sustainable Development Goals. Ecosystem Services & Biodiversity (ESB); UN-FAO
Report
17. Flowers M, Weisz R, Heiniger R (2001) Remote sensing of winter wheat tiller density for early nitrogen
application decisions. Agron J 93:783–789
1. Flowers M, Weisz R, Heiniger R, Tarleton B, Meijer A (2003) Field validation of a remote sensing
technique for early nitrogen application decisions in wheat. Agron J 95:167–176
19. Gamon JA, Peñuelas J, Field CB (1992) A narrow-waveband spectral index that tracks diurnal
changes in photosynthetic eciency. Remote Sens Environ 41:35–44
20. Gamon JA, Serrano L, Surfus J (1997) The photochemical reectance index: an optical indicator of
photosynthetic radiation use eciency across species, functional types, and nutrient levels.
Oecologia 112:492–501
21. Gamon J, Field BC, Goulden LM, Grin LK, Hartley EA, Joel G, Penuelas J, Valentin R (1995)
Relationships between NDVI, canopy structure and photosynthesis in three California vegetation;
Ecological applications. Ecol Soc Am 5:28–41
22. Gat N, Erives H, Fitzgerald GJ, Kaffa SR, Maas SJ (2000) Estimating sugar beet yield using AVIRIS-
derived indices [pdf]. [http://Makalu.jpl.nasa.gov/docs/workshops/00_docs/gat_web.pdf. Retrieved
14/06/2002
23. Gnyp ML, Miao Y, Yuan F, Ustin SL, Yu K, Yao Y, Huang S, Bareth G (2014) Hyperspectral canopy
sensing of paddy rice aboveground biomass at different growth stages. Field Crops Research
155:42–55. https://doi.org/10.1016/j.fcr.2013.09.023
24. Govaerts B, Verhulst N, Ken D, De Sayre P, Goudeseune B, Lichter K, Crossa J, Dendooven L, Deckers J
(2007) Evaluating spatial within plot crop variability for different management practices with an
optical sensor? Plant Soil 299:29–42. DOI 10.1007/s11104-007-9358-6
25. Grisso R, Alley M, Groover G (2009) Precision Farming Tools, GPS Navigation. Virginia Cooperative
Extension, Virginia Tech, p 59
2. Harrell DL, Tubana BS, Walker TS, Phillips SB (2011) Estimating rice grain yield potential using
normalized difference vegetation index. Agron J 103:1717–1723

Page 19/26
27. Hashmi MRQ (2013) Tillage, nitrogen and residue management strategies in wheat (
Triticum
aestivum
L.) grown under rice-based system. Dissertation for Award of PhD Degree at University of
Agriculture, Faisalabad, Pakistan, 273pp
2. Hatibu N (1999) Rainwater harvesting for agriculture in semi-arid areas of Tanzania: Getting the
policies right. Journal of Agricultural Economics and Development, Vol.3 June (1999)
29. Hossain MB, Islam MO, Hasanuzzaman M (2008) Inuence of different nitrogen levels on the
performance of four aromatic rice varieties. Int J Agri Biol 10:693–696
30. Inoue Y, Morinaga S, Tomita A (2000) A blimp–based remote sensing system for low altitude
monitoring of plant variables: A preliminary experiment for agriculture and ecological applications.
Int J Remote Sens 21:379–385
31. Islam MS, Hasanuzzaman M, Rokonuzzaman Md (2008) Ratoon rice response to different fertilizer
doses in Irrigated Condition. Agriculturae Conspectus Scientifcus 73(4):197–202
32. Jones CL, Maness NO, Stone ML, Jayasekara R, Stone ML, Chrz D (2007) Chlorophyll estimation
using multispectral reectance and height sensing. Trans ASAE 50:1867–1872
33. Jones CL, Weckler PR, Maness NO, Jayasekara R, Stone ML, Chrz D (2007) Remote sensing to
estimate chlorophyll concentration in spinach using multi-spectral plant reectance. Trans ASAE
50(6):2267–2273
34. Kadigi RMJ, Mdoe NSY, Ashimogo GC (2007) Understanding poverty through the eyes of the poor:
The case of Usangu plains in Tanzania. Phys Chem Earth Parts A/B/C 32:1330–1338. doi:
10.1016/j.pce.2007.07.029
35. Kanyeka ZL, Msomba WS, Kihupi NA, Penza SM (1994) Rice Ecosystems in Tanzania,
Characterization and Classication. Tanzan Agric Res Train Newsl 9(1–2):13–15
3. Katambara Z, Kahimba FC, Mbungu WB, Reuben P, Maugo M, Mhenga FD, Mahoo HF (2013)
Optimizing system of rice intensication parameters using Aquacrop model for increasing water
productivity and water use eciency on rice production in Tanzania. J Agric Sustain 4(2):235–244
37. Katambara Z, Kahimba FC, Mahoo HF, Mbungu WB, Mhenga F, Reuben P, Maugo M, Nyarubamba A
(2013) Adopting the system of rice intensication (SRI) in Tanzania: A review. Agricultural Sci
4(8):369–375. doi:10.4236/as.2013.48053
3. Kuri F, Murwira A, Murwira KS, Masocha M (2014) Predicting maize yield in Zimbabwe using dry
dekads derived from remotely sensed Vegetation Condition Index. Int J Appl Earth Obs Geoinf
33:39–46
39. Laghari GM, Oad FC, Tunio S, Gandahi AW, Siddiqui MH, Jagirani AW, Oad SM (2010) Growth, yield
and nutrient uptake of various wheat cultivars under different fertilizer regimes. Sarhad J Agric
26(4):490–498
40. Lan Y, Zhang H, Lacey R, Hoffmann WC, Wu W (2009) Development of an Integrated Sensor and
Instrumentation System for Measuring Crop Conditions”. The CIGR Ejournal XI:11–15
41. Lan Y, Zhang H, Lacey R, Hoffmann WC, Wu W (2009) Development of an Integrated Sensor and
Instrumentation System for Measuring Crop Conditions”. Agricultural Engineering International: the

Page 20/26
CIGR Ejournal. Manuscript IT 08 1115. Vol. XI
42. Laudien R, Bareth G, Doluschitz R (2003) Analysis of hyperspectral eld data for detection of sugar
beet diseases. European Federation for Information Technology in Agriculture, Food and the
Environment Conference, Debrecen, Hungary 5–9. July 2003, 375–381
43. Lewis JE, Rowland J, Nadeau A (1998) Estimating maize production in Kenya using NDVI: some
statistical considerations. Int J Remote Sens 19:2609–2617
44. Lewis JE, Rowland J, Nadeau A (1998) Estimating maize production in Kenya using NDVI: some
statistical considerations. Int J Remote Sens 13:2609–2617
45. Li F, Miao Y, Feng G, Yuan F, Yue S, Gao X, Liu Y, Liu B, Ustin SL, Chen X (2014) Improving estimation
of summer maize nitrogen status with red edge-based spectral vegetation indices. Field Crops
Research 157:111–123
4. Li F, Zhang H, Jia L, Bareth G, Miao Y, Chen X (2010) Estimating winter wheat biomass and nitrogen
status using an active crop sensor. Intell Autom Soft Comput 16(6):1221–1230
47. Li G-S, Wu D-H, Su Y-C, Kuo B-J, Yang M-D, Lai M-H, Lu H-Y, Yang C-Y (2021) Prediction of Plant
Nutrition State of Rice under Water-Saving Cultivation and Panicle Fertilization Application Decision
Making. Agronomy 11:1626. https://doi.org/10.3390/ agronomy11081626
4. Li YH (2001) Research and practice of water-saving irrigation for rice in China. In: Barker R, Li Y,
Tuong TP, eds. Water-saving irrigation for rice. Proceedings of an International Workshop, Wuhan,
China. Colombo (Sri Lanka). International Water Management Institute, 135–144
49. Liu WT, Kogan F (2002) Monitoring Brazillian soybean production using NOAA/AVHRR based
vegetation condition indices. Int J Remote Sens 23:1161–1179
50. Malik AU, Malghani AL, Sattarb A, Hussaina F, Abbasc G, Hussain J (2010) Response of growth and
yield of wheat to NPK fertilizer. Sci Int (Lahore) 24(2):185–189
51. Marisol BR (2010) Monitoring Nitrogen Levels in the Cotton Canopy using Real-Time Active-
Illumination Spectral Sensing. MSc thesis, University of Tennessee, USA.
http://trace.tennessee.edu/utk_gradthes/604
52. Martin DE, López JD Jr, Lan Y (2012) Laboratory evaluation of the GreenSeeker™ hand-held optical
sensor to variations in orientation and height above canopy. Int J Agric Biol Eng 5:43–47
53. Materu ST (2014) Yield and water use of alternative rice production systems in Tanzania: Field
experiments and modelling. Unpublished Thesis for Award of MSc Degree at Graduate School of the
University of Florida, USA. 101pp
54. Mckenzie G, Spackman S, Lamb D, Louis J (2000) Retrieving biophysical data from airborne
multispectral imagery of rice crops. Int Archives Photogrammetry Remote Sens 33:1447–1451 Part
B7
55. Michael A, Kuznetsov D, Mirau S (2014) Estimation of irrigation water demand in rice production
Tanzania. Math Theory Model 4(7):24–39

Page 21/26
5. Mkhabela MS, Mashinini NN (2005) Early maize yield forecasting in the four agro-ecological regions
of Swaziland using NDVI data derived from NOAA's-AVHRR. Agric For Meteorol 129:1–9
57. Mohsan S (1999) Population dynamics and nitrogen management effects on maize (
Zea mays
L.)
productivity. Dissertation for Award of Ph.D. at Dept. Agron. Univ. Agri., Faisalabad
5. Ndiiri JA, Mati BM, Home PG, Odongo B, Uphoff N (2012) Comparison of water savings of paddy rice
under system of rice intensication (SRI) growing rice in Mwea, Kenya. Int J Curr Res Rev 4(6):63–73
59. Niel TGV, McVicar TR (2001) Remote Sensing of Rice-Based Irrigated Agriculture:
[http://www.clw.csiro.au/publications/consultancy/2001/CRC-Rice-TRP11050101.pdf] (15
December 2009).
0. Norman JC, Otoo E (eds) (2003)
Rice Development Strategies for Food security in Africa
. Proc. 20th
Session of the International Rice Commission, Bangkok, Thailand, July 2002. 60 pp
1. Pandian BJ (2010) The system of rice intensication: A unique system to produce more with less
input. 28th International Rice Research Conference, 8–12 November 2010, Hanoi
2. Prabhakara Setty TK, Bandi AG, Singh S, Sanjay MT (2007) Inuence of Integrated Nutrient
Management on growth and yield of hybrid rice under System of Rice Intensication (SRI) and
Aerobic method of cultivation. In:
SRI India 2007
.
Second National Symposium on System of Rice
Intensication (SRI) in India
– Progress and Prospects. Papers and Extended Summaries, 3-5th
October, Agartala, Tripura, India, 82–84
3. Quarmby NA, Milners M, Hindle TL, Silleos N (1993) The use of multitemporal – NDVI measurements
from AVHRR Date for yield estimation and protection. Int J Rem Sens 14:199–210
4. Rahetlah VB, Salgado P, Andrianarisoa B, Tillard E, Razandrazaka H, Mézo LL, Ramalanjaona VL
(2014) Relationship between normalized difference vegetation index (NDVI) and forage biomass
yield in the Vakinankaratra region, Madagascar.
Livestock Research for Rural Development.
Volume26.
Retrieved May 16, 2015, from http://www.lrrd.org/lrrd26/5/rahe26095.html
5. Rajak DR, Oza MP, Bhagia N, Dadhwal VK (2002) Relating wheat spectral prole parameters to yield
and phenology. Paper presented at the ISPRS Commission VII Symposium-Resource and
Environmental Monitoring, Hydrabad, India
. Rasmussen MS (1997) Operational yield forecast using AVHRR NDVI data: reduction of
environmental and inter-annual variability. Int J Remote Sens 18:1059–1077
7. Ray SS, Singh JP, Dutta S, Panigrahy S (2002) Analysis of within-eld variability of crop and soil
using eld data and spectral information as a pre-cursor to precision crop management. Paper
presented at the ISPRS Commisiion VII Symposium-Resource and Environmental Monitoring
Hydrabad, India
. Rehman HT, Reis BFA, Akbar N, Linquist AB (2019) Use of Normalized Difference Vegetation Index to
assess N Status and predict grain yield in rice. Agron J 111:1–10. doi:10.2134/agronj2019.03.021
9. RLDC Rural Livelihood Development Company (2009) Improving rice protability through increased
productivity and better marketing focusing on Tanzania’s Central Corridor. Rice Assessment & Draft
Sector Strategy., Tanzania, p 37

Page 22/26
70. Royo F, Andersson MG, Bangyeekhun E, Muzquiz JL, Söderhäll K, Cerenius L (2004) Physiological
and genetic characterisation of some new
Aphanomyces
strains isolated from freshwater craysh.
Vet Microbiol 104:103–112
71. Sato S, Uphoff N (2007) A review of on-farm evaluation of system of rice intensication (SRI)
methods in eastern Indonesia. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition
and Natural Resources, vol 2. Commonwealth Agricultural Bureau International, Wallingford, UK, p 54
72. Shaver TM, Westfall DG, Khosla R (2007) Remote sensing of corn N status with active sensors
Western Nutrient Management Conference
. Vol.7, 78–84
73. Sommer R, Bossio D, Desta L, Dimes J, Kihara J, Koala S, Mango N, Rodriguez D, Thierfelder C,
Winowiecki L (2013) Protable and sustainable nutrient management systems for East and Southern
African smallholder farming systems – Challenges and Opportunities: A synthesis of the Eastern and
Southern Africa situation in terms of past experiences, present and future opportunities in promoting
nutrients use in Africa. CIAT, The University of Queensland, QAAFI & CIMMYT. pp91
74. Sultana SR, Ali A, Ahmad A, Mubeen M, Zia-Ul-Haq M, Ahmad S, Ercisli S, Jaafar HZE (2014)
Normalized Difference Vegetation Index as a tool for wheat yield estimation: A Case Study from
Faisalabad, Pakistan.
The Scientic World Journal
2014, 2014:725326. doi: 10.1155/2014/725326.
Epub 2014 Jun 22. PMID: 25045744; PMCID: PMC4090434
75. Teal RK, Tubana BS, Girma K, Freeman KW, Arnall DB, Walsh O, Raun WR (2006) In-season prediction
of corn grain yield potential using normalized difference vegetation index. Agron J 98:1488–1494
7. Teboh JM, Tubaña BS, Udeigwe TK, Emendack YY, Lofton J (2012) Applicability of ground-based
remote sensors for crop N management in Sub Saharan Africa. J Agric Sci 4:175–188
77. Thierfelder C, Mombeyarara T, Mango N, Rusinamhodzi L (2013) Integration of conservation
agriculture in smallholder farming systems of southern Africa: identication of key entry points. Int J
Agric Sustain. http://dx.doi.org/10.1080/14735903.2013.764222
7. Tilly N, Hoffmeister D, Cao Q, Lenz-Wiedemann V, Miao Y, Bareth G (2013) Precise plant height
monitoring and biomass estimation with terrestrial laser scanning in paddy rice;
ISPRS Annals of the
Photogrammetry
. II–5/W2:295–300. Remote Sensing and Spatial Information Sciences10.5194
79. Tran DV, Nguyen NV (eds) (2006) The concept and implementation of precision farming and rice
integrated crop management systems for sustainable production in the twenty-rst century. In:
International Rice Commission, Crop and Grassland services
. (
Edited by FAO
), FAO, Rome, Italy
pp.91–102
0. Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation.
Remote Sens Environ 8:127–150
1. Tusekelege HK, Kusolwa P, Kihupi AL, Onasanya A (2014) Preliminary pathotype prole of
Xanthomonas oryzae
pv.
oryzae
isolates from the rice ecosystems in Tanzania. In: Proceedings of the
Eastern Africa Agricultural Productivity Project (EAAPP) Rice Scientic Conference. 15–18 July 2014,
Morogoro, Tanzania. Pg. 8

Page 23/26
2. Unganai LS, Kogan FN (1998) Drought monitoring and corn yield estimation in Southern Africa from
AVHRR data. Remote Sens Environ 63:219–232
3. United Nations (2016) Global Sustainable Development Report 2016. Department of Economic and
Social Affairs, New York, July
4. Uphoff N (2003) Higher yields with fewer external inputs? The System of Rice Intensication and
potential contributions to agricultural sustainability. Int J Agric Sustain 1:38–50
5. Verhulst N, Govaerts B (2008) The GreenSeeker handheld NDVI sensor; toward the integrated
evaluation of crop management, concepts and case studies user guide. International Maize and
Wheat Improvement Center. Mexico DF, Mexico
. Verhulst N, Govaerts B, Sayre KD, Deckers J, François IM, Dendooven L (2009) Using NDVI and soil
quality analysis to assess inuence of agronomic management on within-plot spatial variability and
factors limiting production. J plant soil 317:41–59. DOI 10.1007/s11104-008-9787
7. Verma KS, Saxena RK, Hajare TN, Kumar SCR (1998) Gram yield estimation through SVI under
variable soil and management conditions. Int J Remote Sens 19:2469–2476
. Vicente-Serrano S, Cuadrat-Prats JM, Romo A (2006) Early prediction of crop production using
drought indices at different time-scales and remote sensing data: application in the Ebro Valley
(North-East Spain). Int J Remote Sens 27(3):511–518
9. Wang J, Rich PM, Price KD, Kettle WD (2005) Relations between NDVI, grassland production, and
crop yield in the central great plains. Geocarto Int 20:5–11
90. Wiegand CL, Rechardson AJ, Escobar DE, Gerbermann AH (1990) Vegetation indices in crop
assessment. Remote Sens Environ 35:105–119
91. Wijesingha JSJ, Deshapriya NL, Samarakoon L (2015) Rice crop monitoring and yield assessment
with MODIS 250m gridded vegetation product: A case study in Sa kaeo province, Thailand. Int
Archives Photogrammetry Remote Sens Spat Inform Sci 40:121–127
Figures

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Figure 1
Wearing/Assembling of GreenSeeker Hand HeldTM Optical sensor unit (Lan
et al
., 2009)
Figure 2

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GreenSeeker Hand HeldTM Optical sensor unit collecting the reectance readings (NDVI readings) in
smallholder rice irrigation scheme in Lower Moshi, Tanzania.
Figure 3
Location of the study area

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Figure 4
Relationship between grain yield and NDVI at different growth stages.