Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh

Md Abdullah Al Mamun; Sheikh Arafat Islam Nihad; Mou Rani Sarker; Md Abdur Rouf Sarkar; Md Ismail Hossain; Md Shahjahan Kabir

doi:10.1371/journal.pone.0300648

. 2024 Mar 15;19(3):e0300648. doi: 10.1371/journal.pone.0300648

Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh

Md Abdullah Al Mamun ^1,^*, Sheikh Arafat Islam Nihad ², Mou Rani Sarker ³, Md Abdur Rouf Sarkar ^4,^5,^*, Md Ismail Hossain ¹, Md Shahjahan Kabir ⁶

Editor: Vishal Ahuja⁷

PMCID: PMC10942088 PMID: 38489334

Abstract

Technological advancements have long played crucial roles in rice productivity and food security in Bangladesh. Seasonal variation over time and regional differences in rice production, however, pose a threat to agricultural sustainability but remain unexplored. We performed a spatial-temporal mapping of rice cultivation area, production, and yield from 2006–2007 to 2019–2020 using secondary data for disaggregating 64 districts in Bangladesh. Growth and multivariate approaches were employed to analyze time-series data. Results showed that Mymensingh had the highest rice cultivated area and production, while Bandarban had the lowest. The 14 years highest average rice yield was found in Gopalganj and Dhaka (3.63 tons/ha), while Patuakhali (1.73 tons/ha) had the lowest. For the Aus, Aman, and Boro, the rice cultivation area in 19 districts, 11 districts, and 13 districts declined significantly. The overall rice production increased significantly in most districts. For the Aus, Aman, and Boro seasons, the rice yield in 54, 50, and 37 districts demonstrated a significant upward trend, respectively. The adoption rate of modern varieties has risen dramatically. However, there are notable variances between regions and seasons. A significant increasing trend in Aus (0.007% to 0.521%), Aman (0.004% to 0.039%), and Boro (0.013% to 0.584%) were observed in 28, 34, and 36 districts, respectively, with an increase of 1% adaptation of HYV. Predictions revealed that rice cultivation area and production of Aus, Aman, and Boro seasons will be increased in most of the regions of Bangladesh by 2030. Based on spatiotemporal cluster analysis, the five identified cluster groupings illustrated that clusters lack spatial cohesion and vary greatly seasonally. This suggests increasing rice production by expanding cultivable land, adopting high-yielding varieties, and integrating faster technological advancement in research and extension. The findings will assist scientists in developing region-specific production technologies and policymakers in designing decentral region-specific policies to ensure the future sustainability of rice production.

1. Introduction

Rice (Oryza sativa L.) holds a preeminent position as a staple commodity for humanity, representing cultural heritage and providing millions of people with a means of subsistence. With over 3.5 billion people are solely rely on rice for more than 20% of their daily calories, and it supplies approximately 62% carbohydrate, 46% protein, 8% fat, 7% calcium, and 44% phosphorus of the recommended dietary allowance [1–3]. Rice’s ability to survive in the hill to submergence areas, drought to cold and other stresses makes it a miracle plant of the world. Nearly 787 million metric tons (MT) of milled rice were produced globally in 2021 [4]. China is the leading rice-producing country, followed by India, Bangladesh, and Vietnam [5]. The average paddy (un-milled rice) yield in Bangladesh is 4.81 tons/ha [6], compared to 6.93 tons/ha in China, 3.69 tons/ha in India, 5.41 tons/ha in Indonesia, and 5.58 tons/ha in Vietnam [7].

Rice is at the food security center in Bangladesh [5, 8]. In the country, rice is grown on more than 11.7 million hectares (ha) in three rice growing seasons (namely Aus, Aman, and Boro), accounting for 77% of the total cropped area [6]. Rice is a staple grain for 169 million people, accounting for about 80% of the total cereal food supply [9]. The daily rice consumption per capita is 367 grams [10]. In 2008, the country attained rice self-sufficiency, significantly contributing to national food demand [8]. Nevertheless, according to the latest projection from the Bangladesh Bureau of Statistics, the population is expected to reach 189.9 million by 2030 [11]. Consequently, there will be a pressing need for a substantial rise in un-milled rice production, estimated at approximately 42.5 million metric tons, in order to meet the growing human demand [12]. Additionally, a recent forecast suggests that the total un-milled rice demand in 2030, accounting for both human and non-human utilization, will be 53.8 million metric tons [8]. Therefore, it is imperative that we significantly enhance rice production to ensure the current level of food security is sustained.

Bangladesh has a rich history of the evolution of rice production. The rice sector has witnessed rapid dynamism in its production processes and wide adaptability in diverse ecosystems under varied soil and climate conditions. Put simply, rice cultivation is divided into four ecosystems, i.e., irrigated, rainfed lowland, upland, and deep water [13]. Agriculture is predominantly irrigated in the northern region, whereas the eastern region has rainfed lowland and deepwater ecosystems, and the southwestern region has irrigated and upland ecosystems [13, 14]. Scholars observed significant regional differences in rice production for soil, irrigation, pests and disease infestation, farming practices, infrastructural availability, varietal traits, varietal security, socio-economic characteristics of the farmer, and so on [5, 9, 15–18].

Over time, compared to growers of other food crops, rice farmers in Bangladesh confront various biotic and abiotic challenges that harm the plant’s potential for growth and production. For example, more than 30% of the 700 million low-income families in Asia, including Bangladesh, are in rain-fed lowlands that are impacted by environmental triggers [19]. Changes in meteorological variables such as rainfall, temperature, and humidity, as well as natural disasters such as cyclones, floods, drought, etc., cause differences in rice production across the country [20, 21]. Therefore, there is an urgent need to depict the regional rice cultivation scenario to ensure the future sustainability of the rice sector.

We found very few studies that have examined the spatial and temporal changes in rice area, production, and yield dynamics. These studies were primarily focused on major rice-growing countries such as China, India, Indonesia, Vietnam, Thailand, Myanmar, Japan, Philippines, Pakistan, and Brazil, with limited research specifically dedicated to Bangladesh [22–26]. To the best of our knowledge, only one study has investigated the growth and trend analysis of rice in Bangladesh, but its primary focus was on the regional context, examining 14 agricultural regions [5]. However, it is crucial to acknowledge that the rice-growing ecosystem in Bangladesh is characterized by a high level of diversity driven by various factors, including geographical position, socio-economic conditions, and environmental variations. As a result, the findings of the previous study have limitations in terms of their statistical robustness and generalizability for formulating region-specific evidence and policies. To overcome these limitations, our study contributes to the empirical literature by adopting a more disaggregated approach, analyzing data from 64 regions (currently known as districts). This approach allows for a comprehensive understanding of the dynamics of rice cultivation in Bangladesh. We believe that understanding the variations in rice production among different regions will serve as a valuable reference for determining micro-level growth scenarios in the rice sector and ensuring long-term food security.

In light of this, we analyzed spatiotemporal data on cultivation area, production and yield of rice to examine the trends and growth patterns from 2006–2007 to 2019–2020 in Bangladesh. Our analysis emphasized rice productivity at the disaggregate level (district) and revealed a distinct productivity gap between highly developed and less developed regions. The main objectives of the present study were as follows: (a) to assess the observed performances and future predictions of rice production systems at seasonal and disaggregated levels; (b) to estimate the district and season-wise temporal changes of rice growth and trend of cultivation area, production, and yield; and (c) to characterize the similarities and dissimilarities spatial group of clustering with the distribution of rice production determinants of Bangladesh. The outcomes of this study will assist researchers and policymakers in developing new technology and designing regional policies that will support the food security-environment-sustainability web of Bangladesh.

2. Materials and methods

2.1. Study area and data

Bangladesh is located in the eastern part of the South Asian sub-continent. The country’s geographic coordinates fall between 20°34’ to 26°38’ north latitude and 88°01’ to 92°41’ east longitude, with an average altitude of 30 feet above sea level. In this study, sixty-four districts of Bangladesh were considered to explore the district-wise rice data (Fig 1). Secondary data were used to carry out the objective of spatial clustering and temporal trend analysis of cultivation area (ha), production (metric ton), and yield (ton/ha) of rice in Bangladesh (S1 Data). Data collection was performed using multiple issues of the Yearbook of Agricultural Statistics. Rice cultivation area, production, and yield data of 14 years covering three rice growing seasons, i.e., Aus, Aman, and Boro for the periods 2006–2007 to 2019–2020 have been used in this study. We were unable to integrate data because of the statistical yearbook’s limitation on 64 districts level information; before 2006, there were only 23 regions with data available [11].

Fig 1 — Spatial distribution of the study area (left) and inter-district alignment (middle to the right) with the agroecological zone (AEZ) of Bangladesh. GIS map prepared by the authors by using the administrative shapefile of Bangladesh. Shapefile republished from the Bangladesh Agricultural Research Council (BARC) database (http://maps.barcapps.gov.bd/index.php) under a CC BY license, with permission from Computer and GIS unit, BARC, original copyright 2014.

2.2. Data validation and growth analysis

To test the stationary of time series data, autocorrelation and partial autocorrelation [27, 28] were calculated using R programming. We also used the tau-statistic for testing stationary under the null hypotheses of Augmented Dickey-Fuller (ADF) to determine whether the cultivation area, production, and yield variables were stationary [29]. Shapiro-Wilk normality test was used to identify the distribution of the datasets [30]. The following exponential growth model [31] was performed to estimate the growth rate of cultivation area, production, and yield of Aus, Aman, and Boro rice in Bangladesh.

Y_{t} = {a e}^{b t}

o r, l n Y = l n a + b t

(1)

Where Y is the cultivation area, production, and yield, t is the time, b is the growth parameter to be estimated, and ln stands for natural logarithm. The estimated parameter of the exponential growth model depicts the nature and magnitude of the trend with significant tests using the student t-test [32]. All our statistical analyses in this study were done at the 5% probability level.

2.3. Statistical analysis for a multivariate approach

We analyzed the time series data using multivariate statistical techniques, specifically applying principal component analysis (PCA) and hierarchical cluster analysis (HCA). Because of the existence of different factors, cultivation area, yield, and production may be correlated. In this study, we employed multivariate analysis to account for the spatiotemporal variability present across the 64 districts. Specifically, we utilized a 42-time series attributes matrix (64×42) of cultivation area, production, and yield datasets, which may exhibit significant internal, spatial, and temporal correlations. Following the application of PCA, we obtained eigenvalues (42×1) and eigenvectors (42×42) (referred to as loadings). These eigenvalues indicate the variance explained by the principal components (PCs), and, corresponding to each eigenvalue, the loadings assign weights to the indices, enabling their transformation into PCs. The selection of the number of components is based on a point at which the remaining eigenvalues become relatively small and similar in size [33, 34]. The PCA features the capability to unveil such spatiotemporal complexities among the determinants. In addressing the issue, the R-mode operational input matrix [35], i.e., districts vs. attributes (time series observed values), is formed such that the rows represent individual districts (64 nos.). The columns represent the long-term observed values for each year of cultivation area, production, and yield data (42 no.) [36]. Another method to determine the number of principal components involved examining a Scree Plot, which displays the ordered eigenvalues from largest to smallest.

Hierarchical cluster analyses were applied to the time series variables to similar group districts (spatial variability) by using Ward’s method [37] and dynamic time warping (DTW) [38] for the estimation of the distance matrix. For the cluster analysis, variables such as cultivation area (ha), production (mt), and yield (t/ha) of each rice-growing season (Aus, Aman, and Boro) were utilized, ensuring their independence from one another. Thus, the independent PCs were used as input for the cluster analysis [36]. DTW is a family of algorithms that calculate the optimal local stretch or compression to align two-time series’ time axes for the most efficient mapping of one onto the other. DTW generates the cumulative distance between the two series and, if necessary, the mapping or warping function itself. DTW has broad applications in econometrics, chemometrics, and general time series mining for classification and clustering purposes [38]. More precisely, the number of optimal clusters has been identified using the elbow method, which looks at the percentage of explained variance as a function of the number of clusters.

2.4. Relationship between adoption rate and rice production using regression analysis

A time series regression model was employed to examine the impact of high-yielding varieties (HYVs) adoption on rice production in Bangladesh. The empirical form of the regression model is presented below:

{l n Y}_{t} = β_{0} + β_{1} X_{1 t} + β_{2} X_{2 t} + β_{3} X_{3 t} + \dots + β_{64} X_{64 t} + ε_{0}

(2)

Where, lnY_t represents the rice production (in logarithmic form) in period t; X_1t, X_2t, X_3t,…,X_64t are the adoption rate (% of area coverage) of HYVs of rice in different districts during period t; β₀ is the intercept term; β₁, β₂, β₃,…,β₆₄ are the regression coefficients corresponding to each independent variable; and, ε represents the error term or residual. We employed robust standard errors to address the issue of heteroscedasticity in the data.

2.5. Statistical tools

This study used MS Excel to arrange and compile the secondary datasets. We performed all statistical analyses by using programming R software. The spatial data were organized in a district shape file to better understand the results, and all the outcomes were mapped using the programming R and ArcGIS10.3 software.

3. Results

3.1. Descriptive statistics of rice cultivation area, yield, and production

The data analysis reveals that, for most districts and across all seasons, the rice cultivation area, production, and yield data demonstrate adherence to the normality assumption. In the case of the cultivation area, data showed a normal distribution for 47 districts in the Aman area, followed by Boro (45) and Aus (38). The production data for the Boro season revealed a normal distribution for the majority of districts (53), followed by the Aman and Aus seasons. The time series data for Fifty-eight districts showed a normal distribution for the Boro, Aus, and Aman yields.

Fig 2 illustrates the fourteen years average of Aus, Aman, and Boro rice cultivation areas, productions, and yields for the 64 districts. The cultivation area of Aus, Aman, and Boro rice showed significant differences among the districts for the average 14 years period (2006–2007 to 2019–2020) (Fig 2A). Bhola (69,122 ha) and Joypurhat (89 ha) had the highest and lowest average Aus cultivation area, while Mymensingh (2,62,827 ha) and Bandarban (9,007 ha) had the highest and lowest Aman cultivation area, respectively. In the Boro season, the highest and lowest average cultivation areas were reported in Mymensingh (2,54,813 ha) and Barguna (491 ha), respectively. The average production of Aus, Aman, and Boro rice differed significantly (p-value ≤ 0.01) among the districts of Bangladesh (Fig 2B). The average production of the Aus season was highest in Cumilla (1,48,105 mt) and lowest in Joypurhat (208 mt). Dinajpur (6,36,040 mt) had the highest Aman rice production, while Narayanganj (14,981 mt) had the lowest. In the Boro season, the highest and lowest average rice production was in Mymensingh (9,60,248 mt) and Barguna (1416 mt), respectively. Mymensingh (16,18,040 mt) produced the most rice collectively, while Bandarban (53,535 mt) had the least.

Over the last fourteen years, the average milled rice yield for Aus, Aman, and Boro have significantly differed both spatially and temporarily across the 64 districts of Bangladesh (Fig 2C). The highest average yield was found in Cox’s Bazar (2.62 tons/ha), while the lowest was in Faridpur (1.12 tons/ha) in the Aus season. In the Aman season, Khagrachari had the highest average yield (2.75 tons/ha), while Munsiganj had the lowest (1.04 tons/ha). Boro rice yield was highest in Gopalganj (4.60 tons/ha) and the lowest was in Patuakhali (2.26 tons/ha). Gopalganj and Dhaka (3.63 tons/ha) had the highest overall rice yield, while Patuakhali (1.73 tons/ha) had the lowest.

We calculated the standard error of the mean (SEM) (in percentage) to compare seasonal fluctuations in rice cultivation area, production, and yield in both temporal and spatial aspects (Figs 2 and 3). The results suggest that the Aus season had the largest SEM for the cultivation area, production, and yield at 7.09%, 8.56%, and 4.99%, while the Boro season had the lowest at 2.07%, 2.93%, and 1.76% for the cultivation area, production, and yield, respectively. The Aman season exhibited SEM values of 2.17% for cultivation area, 3.87% for production, and 2.91% for yield.

3.2. District-wise trend and growth rates for rice cultivation area, production, and yield

We have estimated the exponential growth rates of all districts in Bangladesh to identify the actual nature of rice cultivation areas, production and yield pattern based on both temporal and spatial aspects.

3.2.1. Trend and growth rate of rice cultivation area

The spatiotemporal variations in rice cultivation areas during the Aus, Aman, and Boro seasons in Bangladesh are illustrated in Fig 4. The exponential growth rate with its significance at a 5% level is also presented in Table 1. During the last 14 years (2006–2007 to 2019–2020), the annual growth rate of rice cultivation area in different districts of Bangladesh ranged from -24.91 to 87.01% for the Aus season, -3.47 to 5.06% for the Aman season and -11.22 to 7.67% for Boro season. Aus rice cultivation area decreased significantly (p ≤ 0.05 and negative correlation) over the year in Barishal, Chattogram, Dhaka, Feni, Gazipur, Gopalganj, Jashore, Jhalokathi, Khulna, Madaripur, Munsiganj, Mymensingh, Norail, Natore, Netrokona, Pabna, Patuakhali, Rajbari, and Sherpur districts. Aman rice cultivation area showed a significant negative correlation (p ≤ 0.05) with time for Bagerhat, Chattogram, Chuadanga, Cumilla, Jashore, Madaripur, Narayanganj, Narsingdi, Pabna, Pirojpur, and Satkhira districts. In the Boro season, rice cultivation in Barishal, Chattogram, Cumilla, Dhaka, Faridpur, Lalmonirhat, Madaripur, Meherpur, Narayanganj, Natore, Pabna, Patuakhali, Rajbari, Rajshahi, and Shariatpur decreased significantly (p ≤ 0.05) over time.

Table 1. District-wise growth rate (%) of rice cultivation area, production, and yield in Bangladesh from 2006–07 to 2019–20.

Sl.	Districts	Cultivation area				Production				Yield
Sl.	Districts	Aus	Aman	Boro	Total	Aus	Aman	Boro	Total	Aus	Aman	Boro	Total
1.	Bagerhat	-1.90	-3.47^*	3.82^*	-1.05^*	1.79	-1.82	6.81^*	2.62^*	3.70^*	1.65	2.99^*	3.68^*
2.	Bandarban	4.04^*	2.51^*	5.03^*	3.64^*	5.15^*	2.87^*	6.59^*	4.54^*	1.10	0.37	1.56^*	0.91^*
3.	Barguna	0.55	-0.97	7.67^*	-0.42	3.57^*	1.20	10.79^*	2.07^*	3.02^*	2.17^*	3.12^*	2.49^*
4.	Barishal	-5.35^*	0.39	-1.51^*	-0.82^*	-4.45^*	1.53	-0.67	-0.06	0.90	1.14	0.83^*	0.76
5.	Bhola	2.91^*	1.48^*	-1.25	1.38^*	6.47^*	5.17^*	-0.35	3.88^*	3.56^*	3.69^*	0.90	2.49^*
6.	Bogura	2.44	0.12	-0.09	0.10	5.60^*	2.43^*	-0.17	0.93^*	3.16^*	2.31^*	-0.09	0.84^*
7.	Brahmanbaria	7.30^*	1.10	0.60	0.94	12.30^*	4.14^*	1.85^*	2.50^*	5.00^*	3.03^*	1.25^*	1.56^*
8.	Chandpur	-2.40^*	-1.34	0.30	-0.50	1.09	0.27	0.66	0.58	3.49^*	1.62^*	0.36	1.07^*
9.	Chapai Nawabgonj	1.13	0.73	-0.47	0.43	4.59^*	0.82	-0.41	1.16^*	3.46^*	0.10	0.06	0.73^*
10.	Chattogram	-1.75^*	-0.89^*	-1.45^*	-1.14^*	-0.68	-0.41	-0.10	-0.35	1.07^*	0.48	1.35^*	0.78^*
11.	Chuadanga	12.31^*	-1.43^*	-0.81	1.82^*	16.17^*	0.31	-0.89	2.47^*	3.85^*	1.74^*	-0.08	0.65^*
12.	Cox’s Bazar	7.78	0.89^*	0.80	0.91^*	7.24	0.28	2.79^*	1.47^*	-0.54	-0.61	2.00^*	0.56
13.	Cumilla	3.88^*	-1.39^*	-0.58^*	-0.20	6.92^*	-0.46	0.15	0.79	3.04^*	0.93	0.73	0.99^*
14.	Dhaka	-4.68^*	-1.59	-0.77^*	-1.05^*	2.78	0.62	0.04	0.05	7.47^*	2.20^*	0.80	1.11
15.	Dinajpur	-0.86	0.98^*	0.25	0.65^*	1.79	2.80^*	0.89^*	1.81^*	2.65^*	1.81^*	0.64^*	1.16^*
16.	Faridpur	-4.54	5.06^*	-3.68^*	1.41	1.01	9.23^*	-3.17^*	2.83^*	5.54^*	4.17^*	0.51	1.42^*
17.	Feni	-2.99^*	1.84^*	-0.80	0.48	-0.31	2.21	-0.50	0.91	2.68^*	0.36	0.30	0.43
18.	Gaibandha	39.75^*	0.97^*	1.53^*	1.38^*	41.13^*	3.57^*	1.22^*	2.17^*	1.37	2.59^*	-0.31	0.80^*
19.	Gazipur	-5.21^*	0.42	0.02	0.08	0.14	0.81	1.08^*	0.99	5.35^*	0.40	1.06^*	0.90^*
20.	Gopalgonj	-5.94^*	-0.86	0.13	-0.38	-0.40	4.08	1.27^*	1.50^*	5.54^*	4.93^*	1.13^*	1.88^*
21.	Hobigonj	3.91^*	1.18^*	0.96	1.49^*	6.16^*	2.30^*	1.56	2.32^*	2.26^*	1.12	0.60	0.84
22.	Jamalpur	8.63^*	0.66	1.17^*	0.99^*	13.28^*	2.33^*	1.29^*	1.63^*	4.65^*	1.66^*	0.12	0.63^*
23.	Jashore	-5.80^*	-0.78^*	0.65^*	-0.51	-4.25^*	0.85	1.55^*	0.96^*	1.55^*	1.63^*	0.90^*	1.46^*
24.	Jhalakathi	-5.36^*	0.66^*	0.88	-0.72^*	-2.11	1.77	2.13^*	0.92	3.25^*	1.11	1.25^*	1.64^*
25.	Jhenaidah	3.92^*	1.03^*	-0.07	0.87^*	7.69^*	3.19^*	1.96^*	2.88^*	3.77^*	2.16^*	2.03^*	2.01^*
26.	Joypurhat	6.43	0.77^*	0.06	0.42^*	10.48	2.77^*	-0.12	1.02^*	4.06^*	2.00^*	-0.17	0.60^*
27.	Khagrachari	2.54^*	-0.33	0.69	0.14	3.77^*	0.83	1.57^*	1.21	1.23	1.16^*	0.88^*	1.08^*
28.	Khulna	-6.12^*	-0.52	3.23^*	0.43	-5.62^*	1.38	4.83^*	2.78^*	0.50	1.90^*	1.60^*	2.34^*
29.	Kishoregonj	1.76	0.99^*	-0.30	0.26	4.01^*	3.98^*	0.75	1.52^*	2.25^*	2.98^*	1.05^*	1.27^*
30.	Kurigram	11.69	1.04^*	2.34^*	1.72^*	17.90^*	4.05^*	2.73^*	3.24^*	6.21^*	3.00^*	0.40^*	1.52^*
31.	Kushtia	-1.37	1.88^*	0.56	0.89	2.60	3.66^*	0.94	2.58^*	3.97^*	1.77^*	0.38	1.70^*
32.	Lakshmipur	-1.96	0.87	-0.31	0.05	1.37	4.94^*	0.79	2.98^*	3.33^*	4.07^*	1.11^*	2.93^*
33.	Lalmonirhat	43.56^*	0.88^*	-0.93^*	0.64^*	46.07^*	3.44^*	-1.11	1.55^*	2.51	2.56^*	-0.18	0.91^*
34.	Madaripur	-10.27^*	-2.08^*	-1.71^*	-2.24^*	-5.27^*	1.16	-0.68	-0.44	5.00^*	3.25*	1.03^*	1.80^*
35.	Magura	-5.26	2.36^*	0.36	1.17^*	-2.92	4.07^*	0.81	2.18^*	2.35^*	1.71^*	0.45	1.01^*
36.	Manikgonj	5.32	4.01	-1.04	0.41	18.05^*	7.25^*	0.44	1.24	12.73^*	3.25^*	1.48^*	0.82
37.	Maulovibazar	8.32^*	0.27	2.47^*	2.32^*	11.60^*	1.02	4.38^*	3.72^*	3.28^*	0.75^*	1.90^*	1.40^*
38.	Meherpur	10.45^*	0.03	-2.43^*	0.65	15.59^*	2.06^*	-2.50^*	1.38^*	5.14^*	2.04^*	-0.08	0.73^*
39.	Munsigonj	-24.91^*	1.46	-0.28	0.04	-16.04^*	1.88	0.59	0.64	8.87^*	0.43	0.87^*	0.60
40.	Mymensingh	-12.06^*	0.01	1.08^*	-0.22	-7.77^*	2.47^*	2.90^*	2.27^*	4.29^*	2.46^*	1.82^*	2.49^*
41.	Naogaon	6.56^*	-0.88	0.53^*	0.51	9.49^*	0.10	1.08^*	1.39^*	2.93^*	0.97^*	0.56	0.88^*
42.	Narail	-2.76^*	-2.00	3.21^*	0.29	0.77	0.63	3.75^*	2.57^*	3.53^*	2.63^*	0.54	2.28^*
43.	Narayangonj	-2.81	-3.15^*	-2.59^*	-2.61^*	-0.29	-1.81	-1.79^*	-1.71^*	2.51	1.34	0.79	0.91
44.	Narsingdi	-1.73	-1.35^*	-0.67	-0.96^*	5.84	-0.18	-0.27	-0.23	7.57^*	1.17^*	0.39	0.73
45.	Natore	-3.31^*	1.53^*	-1.51^*	-0.08	2.54	4.15^*	-1.09^*	0.86^*	5.85^*	2.62^*	0.42	0.94^*
46.	Netrokona	-22.82^*	0.39	0.88	0.52	-20.14^*	1.71^*	2.00^*	1.81^*	2.67^*	1.32^*	1.12^*	1.29^*
47.	Nilphamari	35.75	0.14	1.43^*	0.73^*	50.36	2.50^*	2.40^*	2.45^*	14.62	2.36^*	0.96^*	1.72^*
48.	Noakhali	-0.44	2.50^*	2.15^*	1.87^*	3.60^*	5.66^*	4.27^*	4.69^*	4.05^*	3.16^*	2.11^*	2.82^*
49.	Pabna	-2.18^*	-1.38^*	-1.77^*	-1.59^*	5.59^*	0.83	-2.21^*	-0.55	7.77^*	2.21^*	-0.44	1.04^*
50.	Panchagar	87.01^*	1.46^*	-1.77	0.56^*	99.47^*	4.66^*	-0.93	2.41^*	12.46^*	3.20^*	0.83^*	1.86^*
51.	Patuakhali	-7.49^*	-0.56	-11.22^*	-1.94^*	-3.83^*	2.76^*	-7.49^*	1.30	3.67^*	3.31^*	3.73^*	3.25^*
52.	Pirojpur	-2.87	-1.52^*	2.30^*	-1.07^*	0.42	0.41	4.49^*	1.77^*	3.29^*	1.93^*	2.19^*	2.84^*
53.	Rajbari	-18.99^*	0.58	-3.89^*	-1.59^*	-13.93^*	2.92^*	-3.46^*	0.09	5.07^*	2.34^*	0.43	1.68^*
54.	Rajshahi	1.56	0.19	-1.04^*	0.00	4.45^*	1.53^*	-1.36^*	0.58	2.89^*	1.34^*	-0.32	0.58^*
55.	Rangamati	0.72	0.74	0.35	0.58	0.50	2.13^*	2.18	1.87^*	-0.22	1.39^*	1.83^*	1.29^*
56.	Rangpur	53.59^*	1.54^*	1.05^*	1.84^*	57.80^*	4.09^*	2.47^*	3.55^*	4.20^*	2.55^*	1.42^*	1.71^*
57.	Satkhira	10.81^*	-1.16^*	1.37^*	0.13	12.28^*	0.36	2.17^*	1.43^*	1.47^*	1.52^*	0.80^*	1.30^*
58.	Shariatpur	-1.69	-0.08	-2.63^*	-1.79^*	4.18^*	3.07	-1.25	-0.33	5.87^*	3.15^*	1.38^*	1.46^*
59.	Sherpur	-10.86^*	0.13	1.89^*	0.67^*	-6.53^*	2.55^*	2.49^*	2.29^*	4.33^*	2.42^*	0.61	1.61^*
60.	Sirajgonj	10.28^*	4.55^*	0.57^*	1.82^*	14.41^*	8.30^*	1.07^*	2.39^*	4.13^*	3.75^*	0.50^*	0.57
61.	Sunamgonj	13.01^*	2.80^*	0.17	1.32	19.06^*	4.48^*	2.60	3.29	6.04^*	1.68^*	2.43^*	1.97^*
62.	Sylhet	4.80^*	0.99	2.11^*	1.97^*	8.31^*	1.83	4.27^*	3.65^*	3.51^*	0.84	2.17^*	1.68^*
63.	Tangail	-2.60	-1.26	0.74^*	-0.12	2.84	0.49	1.49^*	1.19^*	5.44^*	1.75^*	0.76^*	1.31^*
64.	Thakurgaon	47.71^*	2.33^*	0.24	2.29^*	50.55^*	3.78^*	-0.25	2.60^*	2.84^*	1.45^*	-0.49	0.32

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Note: * indicate the significance at a 5% probability level

3.2.2. Trend and growth rate of rice production

From 2006–2007 to 2019–2020, the annual growth rate of rice production in different districts of Bangladesh (Fig 5) ranged from -20.14 to 99.47% for the Aus season, -1.82 to 9.23% for the Aman season and -7.49 to 10.79% for Boro season (Table 1). The results showed that rice production in the Aus season decreased significantly (p ≤ 0.05) in Barishal, Jashore, Khulna, Madaripur, Munsiganj, Mymensingh, Netrokona, Patuakhali, Rajbari, and Sherpur. In Table 1, the Aman rice production showed no significant negative correlation with time among 64 districts. Aman rice production in the majority of districts has increased significantly. In the Boro season, the production growth rates of 22 districts decreased by between 0.1 and 7.49%, with Patuakhali, Rajbari, Faridpur, Meherpur, Pabna, Narayanganj, Rajshahi, and Natore exhibiting the most substantial declines. Out of the 64 districts in Bangladesh, about 45 districts showed a significant positive growth rate in rice production.

3.2.3. Trend and growth rate of rice yield

The spatiotemporal changing patterns of Aus, Aman, and Boro rice yield of the last 14 years of Bangladesh are presented in Fig 6. The annual growth rate of rice yield in different districts of Bangladesh ranged from -0.54 to 14.62% in the Aus season, -0.61 to 4.93% in the Aman season, and -0.49 to 3.73% in the Boro season during 2006–2007 to 2019–2020 (Table 1). Except for Rangamati and Cox’s Bazar, the majority of the districts have found a positive association between Aus rice yield and time change. In the Aman season, except for Cox’s Bazar, most districts have shown significant positive yield growth rates. Excluding the districts, Thakurgaon, Pabna, Rajshahi, Gaibandha, Lalmonirhat, Joypurhat, Bogura, Chuadanga, and Meherpur, the annual growth rate of rice yield has been increasing throughout the country during the Boro season.

3.3. Multivariate analysis of time series

3.3.1. PCA interpretations

The analysis of multivariate statistical techniques PCA is obtained to discover sets of attributes whose trends covariate similarly across districts. As all obtained principal components (PCs) are not statistically significant in the scree plot, we may consider stopping at the fifth principal component. The result found that the first five PCs have been explained about 93.21%, 97.14%, and 96.02% of the total variances for the Aus, Aman, and Boro seasons, respectively (Fig 7A–7C). Those mentioned first two PCs explain the maximum variation of the total variance of input attributes.

Fig 7 — Percentage of explained variance for ten principal components of different seasons (a) Aus, (b) Aman, and (c) Boro rice.

3.3.2. Spatial distribution and clustering

This section described the spatial distribution and cluster analysis of the district-level rice cultivation area, production, and yield in Bangladesh. The characterization of the cluster according to the performance of yield, production, and cultivated area in different seasons in different cluster groups is presented in the boxplot (Figs 8–10). The cluster analysis was carried out to identify spatial groups of different districts based on their similarities of long-term rice cultivation scenarios, i.e., cultivation area, production, and yield performance trends. The number of clusters was identified at this point, so the "elbow criterion" (The elbow criterion is a method used in cluster analysis to determine the optimal number of clusters in a dataset. It involves plotting the variance explained by the clusters against the number of clusters. The plot typically resembles an arm, and the "elbow" or bend in the plot represents the point where the addition of more clusters does not significantly reduce the variance. This point is considered the optimal number of clusters for the given dataset. The elbow criterion helps in selecting a reasonable number of clusters that balance capturing meaningful patterns in the data while avoiding overfitting.) in the scree plot depicted the number of cluster groups: five for Aus, seven for Aman, and six for Boro season (Fig 7A–7C).

Fig 10 — (a) dendrograms based on their spatial similarity; (b) spatial representation of the clusters on a GIS map; and (c) distribution of clusters. GIS map prepared by the authors by using the administrative shapefile of Bangladesh. Shapefile republished from the Bangladesh Agricultural Research Council (BARC) database (http://maps.barcapps.gov.bd/index.php) under a CC BY license, with permission from Computer and GIS unit, BARC, original copyright 2014.

Based on the historical data of the Aus season, five cluster groups are depicted in Fig 8(A): cluster 1 (C1), cluster 2 (C2), cluster 3 (C3), cluster 4 (C4), and cluster 5 (C5). The analysis indicates that the highly populated cluster 1 comprises a significant number of districts from the Rangpur, Mymensingh, and Dhaka divisions (Fig 8B). Clusters 1 and 2 have the smallest rice cultivation area and produce the lowest rice production compared to all other cluster groups. Cluster groups 3 and 5 also captured the districts with the highest land-use patterns and production contribution during the Aus season in Bangladesh. It was observed that most of the districts within all cluster groups had similar average yield performance except cluster 2 (Fig 8C).

Spatial groups corresponding to the Aman season were observed to be different compared to the historical rice cultivation area, production, and yield data. Seven clusters, i.e., C1, C2, C3, C4, C5, C6, and C7 (Fig 9A), were found in the Aman season based on the historical data of rice cultivation area, production, and yield. Clusters 2 and 5 had the minimum Aman area coverage and minimum production. The majority of the districts of C2 were located in the Northern and Eastern hills regions. Cluster 5 is mainly covered by the districts of Dhaka division (Fig 9B). Cluster 7 was the least populated cluster covering the maximum cultivated area and the highest rice production in the Aman season. Among the seven clusters, C3 had the second-highest average rice production and area cultivation in the Aman season (Fig 9C).

Fig 9 — (a) dendrograms based on their spatial similarity; (b) spatial representation of the clusters on a GIS map; and (c) distribution of clusters. GIS map prepared by the authors by using the administrative shapefile of Bangladesh. Shapefile republished from the Bangladesh Agricultural Research Council (BARC) database (http://maps.barcapps.gov.bd/index.php) under a CC BY license, with permission from Computer and GIS unit, BARC, original copyright 2014.

The spatial groups for the Boro season were observed in six clusters based on the historical data on rice cultivation area, production, and yield (Fig 10). Cluster 1 consisted of the highest number (18) of districts spatially distributed in the different divisions of Bangladesh. The second highest number (14) of districts was captured by cluster 6 (Fig 10A). Most districts are spatially distributed in Khulna and Dhaka divisions and covered by active, high, and low Ganges River floodplain regions (Fig 10B). Cluster 4 had the highest average Boro rice cultivated area and production among the cluster group, followed by C5, C1, C3, C6, and C2 (Fig 10C). Four spatial groups, C1, C4, C5, and C6 clusters, showed comparatively similar average yield performance, while C2 and C3 had the lowest average yield.

3.4. Spatiotemporal variation of high-yielding variety adoption in Bangladesh

The adoption rate (percentage of area covered) of high-yielding variety (HYV) by season at the country level is illustrated in Fig 11. Over the past 14 years (2006–2007 to 2019–2020), Bangladesh’s annual HYV adoption of rice has gradually increased. Among the total cultivated areas, significant (p ≤ 0.05) increasing trends of HYV adoption were observed in Aus (1.97%), Aman (1.06%), and Boro (0.29%) seasons, with standard error (SE) representing the range of regional variability.

The spatial distribution and temporal variation of the HYV adoption rate of Aus, Aman, and Boro rice is shown in Fig 12. For the spatial distribution, we have employed the mean value of the 14-year data for each district, which is represented in the figure as ‘HYV adoption (%)’. The temporal variability of HYV adoption was measured by a coefficient of variation, i.e., CV (%). The result showed that a low CV (%) value in the districts with a high HYV adoption rate increased yield trend, while a high CV in the districts with a low HYV adoption rate showed mild increasing yield trends for all seasons. In Aus season (Fig 12A), except Shariatpur, Madaripur, Faridpur, Rajbari, Gopalganj, Rangamati, Tangail, Khulna, and Narail, the majority of the districts have the highest increasing HYV adoption rate and the lowest adoption variability. In Aman season (Fig 12B), the districts of Munsiganj, Manikganj, Gopalganj, Narayanganj, Shariatpur, Madaripur, Pirojpur and Jhalakathi in the Dhaka division and all districts of Barishal divisions had the lowest HYV adoption rate (less than 25%) and the highest adoption variability (CV greater than 35%). Most districts throughout the Boro season (Fig 12C) reported high adoption of high-yielding varieties and the lowest adoption variability. Only Patuakhali, Barguna, and Sylhet had the lowest HYV adoption rate and the highest adoption variations.

3.5. Impact of high-yielding variety adoption on rice production

This section shows the impact of technological advancement, more specifically focusing on examining the impact of HYV adoption on rice production in various districts of Bangladesh, assuming all other factors remained constant. In this analysis, HYV adoption served as a proxy for technological advancement. To the best of our knowledge, the extent to which this technological advancement (HYVs) has influenced rice production in specific seasons and districts of Bangladesh has not been previously revealed. This study provides novel evidence in this regard, shedding light on the relationship between HYV adoption and rice production at the district level (Fig 13). Our findings revealed that a 10% increase in HYV adoption led to a varying range of rice production increase, ranging from 0.04% to 5.8% across different seasons, with a few exceptions. In the Aus season, a significant increasing trend (0.007% to 0.521%) was observed in 28 districts, with an increase of 1% adoption of HYVs, while a significant decrease (0.005% to 0.070%) in production was seen in five districts. In Aman season, the trend was significant and positive (0.004% to 0.039%) for 34 districts, whereas a significant negative trend (0.006% to 0.021%) was found in four districts. Boro season also followed the same trend, where 36 districts exhibited a significant increasing trend (0.013% to 0.584%), and six districts showed significant negative trends (0.090% to 0.426%). Overall, the production advantage due to HYV adoption was higher in Aus and Boro compared to the Aman season.

Fig 13 — Impact of high-yielding variety adoption on (a) Aus, (b) Aman, and (c) Boro rice production in Bangladesh. Here, the positive coefficient indicates the increasing trend, the negative coefficient indicates the decreasing trend, and the coefficient value indicates the percentage change.

3.6. Future prediction of rice cultivation area and production change in Bangladesh (2020–2030)

Here, our aim was to identify the future challenges related to rice cultivation area and production changes in Bangladesh. We accomplished this by analyzing the existing trends from 2006–2007 to 2019–2020, and subsequently predicting and presenting the rice production and cultivation area in Fig 14. In this study, our prediction time was restricted to align with the global development agenda timeframe, which extends until 2030. It is worth noting that shorter prediction periods may yield higher accuracy and precision in forecasting outcomes. In the Aus season, the highest decreasing production rate (>5%) will be found in Mymensingh, Patuakhali, Pirujpur, Rangamati, Barishal, Jessore, Madaripur and Sherpur, whereas 1–5% will be found in Faridpur, Gopalganj, Jhalokathi, Khulna, Narail and Tangial. On the other hand, >5% and 1–5% increasing production trends will be found in 22 and 28 districts, respectively. Aus cultivation area will be increased and decreased in 44 and 20 districts, respectively. During Aman, a more than 5% increase in production will be found only in Narail, where 1–5% will be noticed in 54 districts. Conversely, a more than 5% decrease in production has been projected in Manikganj and 1–5% in Bagerhat, Chattagram, Cumilla, Cox’s Bazar, Madaripur, Narayanganj, Narsingdi, and Tangail. In the case of the rice cultivation area, the amount will be increased and decreased in 43 and 21 districts, respectively. For the Boro season, a more than 5% increase in production has been predicted in Borguna, whereas 1–5% in 41 districts. Oppositely, a 1–5% decreasing trend will be found in 22 districts, and no districts will show a decreasing trend of more than 5%. For area, it was forecasted that area would increase in 34 districts while it will be decreased in 30 districts.

Fig 14 — Prediction (2020–2030) of rice cultivation area and production change in (a) Aus, (b) Aman, and (c) Boro season of Bangladesh. GIS map prepared by the authors by using the administrative shapefile of Bangladesh. Shapefile republished from the Bangladesh Agricultural Research Council (BARC) database (http://maps.barcapps.gov.bd/index.php) under a CC BY license, with permission from Computer and GIS unit, BARC, original copyright 2014.

4. Discussion

The rice-growing ecosystem in Bangladesh exhibits significant diversity, and several factors profoundly impact rice cultivation practices and productivity growth. The following are the main points of discussion.

Land changing nature and geographical diversity: Urbanization and human settlements reduce the thousands of agricultural lands in worldwide [39, 40] which consequences a threat to the environment and food security. Agricultural lands have been declining in Bangladesh for the past 30 to 40 years at a pace of 1% per year, while the percentage of urban areas has dramatically expanded [41]. The total amount of agricultural land decreased from 57.27% in 1992 to 42.82% in 2018, indicating an annual loss of 1.8% [42]. In the last 26 years, 25.6% of agricultural lands have been changed to other land cover types while there is a high conversion between agriculture and vegetation with rural settlements [42]. However, significant increase of per unit production of rice playing a vital role to ensure the food security of Bangladesh [5, 8]. The land type of the ecosystem mostly determines the rice cultivation area, cropping pattern, and cropping intensity [43]. Also, the favorable and unfavorable environment, farmers’ selection of crops, and crop profitability influence the rice cultivation area [44].

According to our findings, Mymensingh district’s dominance in rice production and area coverage can be attributed to a favorable environment for rice cultivation, including suitable topography, stagnant water in 80% of its area, low water flow recession, and high clay soil content [5]. Additionally, Dinajpur is the second largest rice cultivation area in the Aman season because of favorable environment, market potential for aromatic rice, and well-structured rice processing and distribution network. The bulk of the aromatic rice, along with other high-yielding varieties grown during the Aman season may lead to the second largest rice producing season in the country [9, 45]. Sunamgonj, Kishorgonj, Sylhet, Brahmanbaria, Habiganj, Moulvibazar, and Netrokona districts belong to the haor area (a wetland ecosystem). The Haor region is a significant contributor to the overall rice production in the country, especially Boro rice; however, there exist various challenges and limitations that impede the production of rice in this region, such as inadequate transportation facilities, improper usage of fertilizers, a shortage of quality seeds, deficiency of potassium nutrients, various rice diseases and insects such as blast, bakanae, and brown plant hopper, flash floods, and cold injury [46–50]. Developing short-duration with high-yielding rice varieties, improving transportation facilities, and implementing farmers’ training programs on nutrient and disease management are essential steps to overcome the existing problems and increase rice production in the region. On the contrary, Bandarban, Khagrachari, and Rangamati districts have the lowest rice cultivation area and production. The location of these districts in a hilly region, coupled with the absence of flat terrain, agricultural inputs, new technologies, and adequate infrastructure, accounts for the challenges encountered in rice cultivation [5, 51]. In districts such as Jhalokati, Meherpur, Munsigonj, Shariatpur, and Moulvibazar, the cultivation of local rice varieties has resulted in a relatively lower production contribution to the national food basket. However, from an economic perspective, these local varieties exhibit higher profitability compared to HYVs, making a significant contribution to increasing farmers’ income [45] and bolstering the national economy.

Technological advancements and adoption: Sustainable agriculture heavily relies on technology. In the 1970s in Bangladesh, the introduction of irrigation systems, chemical fertilizers, and the modern rice cultivars IR5, IR8, and IR20, collectively referred to as "green revolution technologies," have long played major roles in rice production [52]. After that, gradual improvement occurs in domestic rice productivity by developing and adopting new technologies for rice cultivation. In 1990, the government’s substantial subsidies on irrigation facilities and the release of two high-yield varieties of rice, BRRI dhan28 and BRRI dhan29 in 1994, paved the way for a second green revolution in Bangladesh [5]. These facilities and the high demand for rice act as a catalyst to show the positive association of 45 districts and the trend of rice production over the last 14 years. Our study revealed that Bangladesh’s bulk of rice growing land is devoted to high-yielding varieties. As evidence, High Yielding Varieties (HYVs) adoption has expanded dramatically throughout the country over the past 36 years, with observed variations in adoption rates of 72.0% for Aus, 73.5% for Aman, and 98.5% for Boro season in 2020–21 [5]. Our result stated that the coverage is almost saturated in the majority of districts in Boro season compared to Aus and Aman. In several regions, adoption of improved crop technology was low (less than 50%) for Aus and Aman seasons. Perceptible differences exist in the spread of HYVs across districts for different seasons. For example, in the central and southern districts of Bangladesh, the adoption rate of HYVs is very low (less than 25%). Availability and accessibility frequently influence farmers’ selection of rice varieties, hence increasing the adoption heterogeneity of HYV in some regions. Moreover, a new variety might not be suitable for every district and may result in low yield due to environmental and edaphic factors and poor management. This situation sometimes convinces farmers to cultivate their old and traditional varieties instead of modern ones, leading to significant variation in adoption in some districts. However, the widespread cultivation of HYVs has the potential to displace many traditional rice varieties from cultivation, posing a threat to their preservation. Likewise, this is of grave concern to the cultural heritage of Bangladesh.

The adoption of advanced technology by farmers plays a crucial role in increasing rice production. Regarding yield, the Gopalganj district has the highest rice yield in Bangladesh, attributed to the high organic matter content in the soil, the dominance of the high-yielding Boro cultivation area [5], and the single cropping pattern of Boro-Fallow-Fallow, which makes Boro season the highest yielding rice season [43]. Afterward, technological advancements and the dissemination of modern high-yielding rice varieties in Dhaka district have resulted in its potential for higher rice yields compared to other districts. The Bangladesh Rice Research Institute (BRRI) and the Bangladesh Agricultural Research Institute (BARI) are located in the Gazipur district, closer to Dhaka. So, the dissemination of new agricultural inventions and the adoption of modern high-yielding crop varieties from institutions are facilitated for the surrounding regions of Gazipur. High yield is a significant challenge in Patuakhali and other coastal regions due to the prevalent issue of salinity in the soil. In response to this, BRRI has developed high-yield salt-tolerant rice varieties such as BRRI dhan47, BRRI dhan67, BRRI dhan76, and BRRI dhan77. However, farmers still prefer cultivating traditional low-yield salt-tolerant and tidal submergence varieties [53, 54]. Therefore, extensive campaigns and input provisions are required to encourage farmers to adopt these varieties to increase yield.

Changes in production practices: The overall rice production trend in Bangladesh is increasing. Unfortunately, rice production in some regions fell short of expectations and declined over time. In the coastal districts of Bagerhat and Barishal, and the hilly district Chattogram, rice cultivation areas and production are falling for irrigation constraints. Shrimp farming (known as gher) is more profitable than rice in coastal regions, so farmers switch rice fields for gher farms [55]. In the northwestern and hilly regions of Bangladesh, farmers have embraced a transformation in agricultural patterns by adopting diverse fruit-dominated farming systems instead of relying solely on rice monoculture. This shift is driven by factors such as erratic rainfall, fluctuations in temperature, a shortage of labourers, limited economic benefits, and difficulties in accessing agricultural inputs [56]. Relative profitability, labour shortages, and low land topography are the key determinants of converting rice fields into aquaculture farming in the districts of Mymensingh and Khulna [57–59]. Shortage of labour and low price of rice also discourage farmers from growing rice instead of other crops. Mechanization tools like introducing the combined harvester, reaper machine, and processing unit could be an alternate option for labours. Policymakers should develop marketing strategies so farmers can get profitable prices by cultivating rice. However, the rising male outmigration left women to manage the farm with limited access to resources. Participatory water governance and capacity building of women through various training programs can be beneficial in reviving rice production [60].

Shifts in crop and varietal preferences: Location-wise crop selection and varietal adoption depends on the farmers’ choice, market value, and environmental factors. People of south western part prefer to eat bold grain rice (like BRRI dhan76, BRRI dhan77, and local varieties), where slender grain rice (BRRI dhan28, BRRI dhan34, BRRI dhan49, BRRI dhan50, and BRRI dhan63) are preferred by the people of northern and central regions of Bangladesh [61]. BRRI dhan51 and BRRI dhan52 are popular in submergence prone areas like Lalmonirhat, Kurigram, Sylhet, Cumilla, Faridpur, Rangpur, etc., while drought tolerant varieties (BRRI dhan33, BRRI dhan56, BRRI dhan57, and BRRI dhan71) are preferable in north western, central, and part of southern regions [12]. Recently BRRI dhan89, BRRI dhan92 and Bangabandhu dhan100 are promising Boro varieties of Bangladesh and gaining popularity because of their high yielding capacity. BR11, BR21, BR22, BR23, BRRI dhan49, BRRI dhan71, and BRRI dhan87 are also very popular Aman rice varieties throughout the country. BRRI dhan48 is one of the top choice varieties to the farmers in Kharif-I season compared to local Aus varieties. BRRI developed hybrid varieties (BRRI hybrid dhan3, BRRI hybrid dhan5, and BRRI hybrid dhan7) gaining popularities throughout the country because of high yield and profitable price. In sum, the popularity of different varieties, reflects the diverse agricultural landscape and the importance of selecting suitable rice varieties for different agro-ecological conditions in the country. Additionally varietals adoption also depends on the farmers taste, grain structure, economic value, and even in gender who lead the households [62].

Farmers in Narsingdi, Pabna, Pirojpur, Rajbari, Madaripur, and Shariatpur districts have preferred cultivating vegetables and spices over rice production. This can be attributed to the higher profitability and demand for these crops, as well as the insufficient supply of rice seeds. To increase rice production in these regions, developing short-duration Aman varieties and the seed supply chain can enable farmers to cultivate winter vegetables after Aman rice (Experts from the Department of Agricultural Extension, personal communication, March 10, 2023). Urban areas, such as Dhaka, Chattogram, Narayonganj, and Narsingdi, which are centers of industrialization, have experienced a decline in rice production and cultivation areas. Industrialization reduces crop farming and induces people to engage in high-paying industrial work instead of laborious rice cultivation.

Environmental challenges and adaptation: Bangladesh is one of the climate vulnerable countries of the world [63]. Salinity, drought, flood and cold are becoming major challenges for rice cultivation in the country. Around two million ha of land in the south and southwestern regions of Bangladesh are already affected by salinity. For the adaptation, BRRI-developed saline-tolerant varieties like BRRI dhan47 and BRRI dhan67 are gaining popularity in the saline regions, but to cover more area, we need to develop higher saline (>16 dsm^-1) tolerant varieties at vegetative and reproductive stages. The northern regions of Bangladesh are prone to drought stress due to factors such as reduced rainfall, groundwater depletion, and inadequate water drainage [64]. Many cultivable lands are remained fallow due to lack of water and drought conditions. BRRI dhan33, BRRI dhan56, BRRI dhan57, and BRRI dhan71 are drought-tolerant varieties gaining popularity in northern drought-prone areas of Bangladesh. But to increase cultivation area coverage of rice in the drought-prone region, high drought-tolerant varieties with precision management systems like drip or sprinkler irrigation, direct seeded planting, and rainwater harvesting could be possible solutions. In the Boro season, most of the land of southern and northeastern regions remain fallow due to lack of irrigation water. The establishment of deep water and shallow water tubewell, and irrigation channels to use natural resources like rivers and canals could increase the rice cultivation area in Bangladesh. Flood is not a regular phenomenon in Bangladesh, but heavy rainfall and flash flood causes significant crop losses in the low-lying and flood-prone areas. Forecasts of heavy rainfall, dam making, and river digging could solve the flood problem in the country. Moreover, flood-tolerant varieties like quick regeneration capacity after the recession of water, sustaining ability under flood water for 21–25 days, stronger culm having lodging tolerant could be another way to withstand the challenge of flood problems.

Rice crop zoning: Effective rice crop zoning encourages the preservation of agricultural land, can stimulate locally grown crops, and aims to assist the overall agrarian economy. According to the spatial clustering of our analysis, districts in clusters 1 and 2 in the Aus season have low cultivable areas, which means area expansion is limited. However, expanding high-yielding varieties and other management practices can improve the production of these areas. Districts in clusters 3, 4, and 5 also can enhance their rice production by expanding cultivable land, using high-yielding varieties, and adopting new technologies. In the case of Aman season, the focus should be given to clusters 1, 2, and 3 to increase rice cultivation area and production. For the Boro season, regions belonging to clusters 1, 4, and 5 have the potential to expand rice cultivation area and production.

4.1. Practical implications

The research findings have significant practical implications for informing and guiding future agricultural practices and policies in rice-intensive areas of Bangladesh. The study provides valuable insights into the dynamic changes in the area and production of rice cultivation at a more disaggregated level, representing diverse ecosystems and management constraints. Policymakers can utilize this information to develop targeted strategies and interventions aimed at enhancing rice production in specific regions. For instance, regions with favorable environmental conditions and fertile soil content can be encouraged to prioritize expanding rice cultivation. Efforts can be directed towards providing farmers in these regions with access to modern high-yielding varieties and advanced agricultural technologies to improve productivity. Conversely, areas facing challenges such as salinity or hilly terrain require tailored approaches to overcome these limitations, such as the development of stress-tolerant rice varieties and the implementation of appropriate irrigation systems. Additionally, identifying regions where rice production is declining, or farmers are shifting to alternative crops can guide initiatives aimed at revitalizing rice cultivation through improved irrigation facilities, training programs, and the development of short-duration varieties. The study highlights the areas that require closer attention to overcome challenges and enhance productivity, ultimately contributing to achieving Sustainable Development Goal 2.3 of doubling agricultural productivity and income for smallholder farmers by 2030. In summary, these practical implications can contribute to sustainable agricultural practices, enhance food security, and improve the livelihoods of farmers in Bangladesh.

4.2. Limitations of this study and future research scope

Despite the valuable insights provided by this study, there are certain limitations that should be acknowledged. First, this research focused primarily on the quantitative analysis of rice cultivation area and production trends, and did not delve into the underlying socio-economic factors that may influence these trends. Additionally, this study relied on secondary data sources, which may be subject to limitations such as data accuracy and availability. Conducting primary data collection through field surveys and interviews could enhance the accuracy and reliability of the findings. Furthermore, the study’s scope was limited to a specific timeframe (2006–2019), and it is important to recognize that future changes in climate, technology, and agricultural practices could impact rice cultivation in unforeseen ways.

Building on the findings and limitations of this study, several areas for future research can further contribute to the understanding of rice cultivation in Bangladesh. Firstly, investigating the socio-economic factors that influence farmers’ decision-making processes regarding rice cultivation, including factors such as market conditions, government policies, and farmers’ preferences, would provide valuable insights into the drivers of rice production. Secondly, exploring the impact of climate change on rice cultivation, including the effects of rising temperatures, changing rainfall patterns, and increased occurrences of extreme weather events, is crucial for developing climate-resilient agricultural strategies. Additionally, examining the adoption and effectiveness of specific interventions aimed at improving rice productivity, such as the dissemination of high-yielding varieties, the implementation of irrigation systems, and the provision of training and extension services, would help in identifying best practices and areas for improvement. Finally, integrating remote sensing and geospatial analysis techniques can enhance the accuracy and timeliness of monitoring rice cultivation dynamics, allowing for more precise and up-to-date assessments of cultivation area and production trends.

5. Conclusions and policy recommendations

To ensure the sustainability of rice production in Bangladesh, it is imperative to have a comprehensive understanding of the spatiotemporal distribution of rice cultivation area, production, and yield. This knowledge will enable the formulation of region-specific policies tailored to the specific needs of different areas. The study has introduced novel methodological approaches for trend analysis and spatial clustering. Our findings showed that 14 years averages of rice cultivation area, production, and yield for three major seasons, Aus, Aman, and Boro, differ significantly among the study districts in Bangladesh. The Aus season has the highest temporal variability of rice production determinants, followed by the Aman and Boro seasons. Regional disparities in production were revealed in five cluster groups for the Aus season, seven for the Aman, and six for the Boro season. The share of HYV adoption significantly increased for most of the season. A significant increasing trend in Aus (0.007–0.521%), Aman (0.004–0.039%), and Boro (0.013–0.584%) were observed in 28, 34, and 36 districts, respectively, with an increase of 1% adoption of HYV. Predictions revealed that more than 5% of rice production would be increased in 28 districts in the Aus season, and for Aman and Boro, more than 5% would be increased in Narail and Bogura, respectively. Moreover, a 1–5% increase will be found in 50, 54, and 41 districts in Aus, Aman, and Boro seasons, respectively. These findings underscore the importance of formulating tailored and targeted policies at the regional level to effectively enhance rice productivity in Bangladesh.

The following policy recommendations are crucial for addressing the challenges and maximizing the potential of rice production in the country.

Measures should be implemented to safeguard existing rice cultivation areas as the availability of arable land decreases. This includes preserving fertile lands, preventing land conversions, and implementing land-use planning strategies that prioritize agricultural purposes.
In regions where there is an increasing trend in rice cultivation area, the adoption of high-yielding varieties should be promoted.
Areas with low production can benefit from the adoption of precision management systems. These systems utilize advanced technologies to optimize resource utilization, enhance efficiency, and improve crop yields.
To overcome the challenges faced in certain regions, such as salinity, drought, or flood-prone areas, tactical management approaches should be implemented.
In regions with high rice production, there should be a focus on building adequate storage facilities to prevent post-harvest losses. This will ensure food security and stabilize prices in the market.
Providing farmers with access to quality inputs such as seeds, fertilizers, and pesticides, along with financial support in the form of subsidies and crop insurance, will help mitigate risks and encourage increased rice production.
Strengthening water management systems and promoting the adoption of modern farming technologies are essential steps toward achieving sustainable increases in rice production.
Policies should be implemented to ensure fair and remunerative prices for rice farmers.
Last but not least, the government should prioritize investment in agricultural infrastructure, research and development, and extension services.

By implementing these policy recommendations, the government can create an enabling environment for sustainable rice production, ensure food security, and enhance the income and livelihoods of rice farmers in Bangladesh.

Supporting information

S1 Data

(XLSX)

pone.0300648.s001.xlsx^{(17.4KB, xlsx)}

Acknowledgments

The authors express their sincere thanks to the Bangladesh Bureau of Statistics (BBS) for making available the relevant rice data. The authors also acknowledge several scientists of the Bangladesh Rice Research Institute for participating in the discussion at various stages of preparing the manuscript.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0300648.r001

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PONE-D-23-09867Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh

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Reviewer #1: In this paper, the authors performed a spatio-temporal mapping of the area, production, and yield of rice from 2006-2007 to 2019-2020 using secondary data for disaggregating 64 districts in Bangladesh. They also looked at the adoption rate of high-yielding varieties of rice and did cluster analysis. The authors of this paper has also published a similar paper (same lead author) in PLOS One with the title “Growth and trend analysis of area, production and yield of rice: A scenario of rice security in Bangladesh (Al Mamun M.A, Nihad SAI, Sarkar M.AR, Aziz M.A, Qayum M.A, Ahmed R, et al. 2021. Growth and trend analysis of area, production and yield of rice: A scenario of rice security in Bangladesh. PLoS ONE 16(12): e0261128. https://doi.org/10.1371/journal.pone.0261128”). The current manuscript is a subset of the other published paper. In the published paper, they used data for the period of 1969–70 to 2019–20 at the region level (old district level, there were 20 old districts which are currently named as region, the statistical data until early 2005 were available only at that level). In this paper they are using the current 64 districts (20 regions were divided into 64 districts) for which data are available for the period of 2006 to 2020 which they used in this manuscript). Many things are common between these two papers (such as maps of area, production, yield and adoption rate, statistical parameters, cluster analysis, etc.). There is no new message in this manuscript. So, what is the novel aspect of this paper? The paper is mostly the presentation of the secondary data in maps and charts though they are hardly readable. The quality of the figures and graphs are very poor as I mentioned in detail in my specific comments below. The discussion section of the paper is mostly not much relevant. Many significant factors such as rapid urbanization and industrialization on the agricultural lands, varieties of rice grown in different districts, problems of flood, drought and salinity in the coastal region are significant factors in future rice cultivation which should have been discussed. Please see also my specific comments given below.

Abstract and introduction and in other places in the manuscript: Spatial – temporal should be spatio-temporal

Lines 59-68: The word rice is used and the statistics for milled-rice is given for the world (787 tons) in lines 59-65. However, in line 66 the authors mentioned about the average paddy yield. Please clarify the average paddy yield. Is this milled-rice or yield at the farm after the harvest, un-milled rice? This always create confusion and it is not clear in the Bangladesh statistics whether the yield reported in for milled rice or un-milled paddy rice.

Line 74: Projected population growth to 189.9 – provide reference for that.

Line 76: Reference 9 given in the list does not have journal name.

Fig.1 What does inter-district alignment mean? The text in the figure particularly in the right on is not readable.

Line 193: Showing area in has up to 2 decimals is unnecessary. Please remove decimal places in all.

Lines 205 to 212: Please explain whether this is the yield of milled rice or unmilled rice.

Lines 217-218: Among the three seasons Aman season …., respectively – not clear. Please rephrase.

Line 229-230: “We examined season-wise assessments and their aggregated aspects to determine the impact of the leading season on national rice security in Bangladesh” - Not clear.

Fig.7: What does this % numbers mean? It is not clear in the text. What are the 10 principal components as mentioned in the fig caption?

Figs 8-10: What additional information do they provide? The information presented here can be easily presented in the Fig.2 district wise and then arranging them in ascending or descending order to show the cluster. In addition, there is spatial maps which shows the variations. So new information do they provide? Just another figures. The text in the figures are not readable at all.

Lines 314: What are the characteristics of different clusters? In Fig. 7, C1 to C5 were used as clusters? What C1 to C5 indicates in terms of area, yield, and production?

Lines 319: Elbow criterion – not readable so difficult to understand.

Lines: 321- 329: Please see my comments above.

Line 325: Poorest rice production – what does it mean? I think you wanted to mean lowest total production

Lines 337 – 365: Comments related to cluster analysis mentioned above applies here as well.

Lines 366 – 370: Increasing trend of what? I understand this could be the area but there is no mention of it in the text or in the figure 11. This should be clear that of the total cultivated area, xx% were on HYV cultivation. This is also country level data. Up to this point, the data was presented at the district level. Here, there is no mention of whether the data and the figure is at country level or district level.

Fig 11: Adoption (%) - % of what? Should be mentioned. Same applies to Fig. 12.

Fig 12: Spatial distribution of adoption rate presented here is very confusing. Adoption rate for each district for 14 years were classified into different groups. But this data should have two dimensions on is temporal and the other is spatial. How the authors presented these in these maps? What is the point of presenting the previous years? I think the authors can take the adoption rate of the last year and then classify them in different group (64 district into different group) which then can be presented in a map.

Line 395 -396: Districtwide adaptation scenario – is it adaptation or adoption? In the whole section of 3.5, adaptation was used in the text whether in the title it is adoption.

Fig 13: Again it is hard to understand the message from the figure caption or the axis headings. What does influence mean here? Increase or decrease of yield or production?

Lines 432-441: Mostly already described in the introduction (lines 78 to 86).

Lines 448-452: Repetition of results

Line 452-453: “The bulk of the aromatic rice is grown during the Aman season which may lead to high Aman rice production in Dinajpur” what about area. Aromatic rice nothing to do with the high production it is the area or the yield. There is no discussion on the yield differences and on varieties. In some areas, local varieties or HYV with lower yield are grown which has impact on overall production.

Lines 454- 462: Any references for this?

Lines 491 – 496: Any data or references?

Lines 499-502: Vertical and hydroponic farming for rice? Any references?

Lines 529-532: Repetitive.

Reviewer #2: The research topic “Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh” is substantive and is within the scope of the journal. In general, the paper is thoroughly researched and well-written. By the way, I have some minor comments for the authors that would help to improve the quality of the manuscript.

Abstract:

1. Line # 35: Authors wrote “… performed a spatial-temporal mapping of the area, production, and yield….”. Is the word “area” sufficient or it is “cultivated/cultivation area”?

2. Line # 37 – 38: Replace “Results show that …” with “Results showed that….”.

3. Line # 41: “…the rice area in 19 districts, 11 districts, and 13 districts declined significantly”. The word “rice area” is not a suitable wording. Please replace it with “rice-cultivation-area” or “cultivation area”.

Introduction:

1. Line # 60-63: Please add the information “over 3.5 billion people are solely dependent upon rice for at least 20% of their daily required calories” from Introduction section of “Alam, M. J., Alamin, M., Sultana, M. H., Ahsan, M. A., Hossain, M. R., Islam, S. S., & Mollah, M. N. H. (2020). Bioinformatics studies on structures, functions and diversifications of rolling leaf related genes in rice (Oryza sativa L.). Plant Genetic Resources, 18(5), 382-395” with the sentence “With over half of the world's population depending on rice for their daily energy, and it supplies approximately 62% carbohydrate, 46% protein, 8% fat, 7% calcium, and 44% phosphorus of the recommended dietary allowance” and cite Alam et al. 2020.

2. Line # 65-66: Please add citation for the sentence “China is the leading rice-producing country, followed by India, Bangladesh, and Vietnam”.

3. Line #103-104: “This paper analyzed spatiotemporal” is not a standard wording. Please reshape the sentence as “In this study, we analyzed spatiotemporal data on cultivation area, and production and yield of rice to examine/investigate the trends and growth patterns from 2006-2007 to 2019-2020 in Bangladesh. Do not mention the data sources under Introduction section, rather write it under Materials and Methods section.

Materials and Methods:

1. Line # 120: Replace the word “area” with “cultivation area”. Also, this is applicable for whole body of this manuscript.

2. Please add a sentence to mention the level of significance considered for different statistical tests (e.g., normality test, t-test, etc.) under “Statistical analysis” subsection.

Results:

1. Line # 233: “… seasons in Bangladesh is illustrated in Fig 4”. Replace “is” with “are”.

2. Line # 293: Add unit of measurement for area, production and yield within brackets.

3. Line # 425: Replace “will” with “would” in line # 425.

Discussion:

1. Line # 484: Add an “and” before the phrase “declined over time”.

2. Line # 485: Replace “rice area” with “rice cultivation areas”.

3. Line # 555: Add “were” verb before the phrase “revealed in five ….” in line no. 555.

4. Line # 561: Replace “will” with “would” in line no. 561.

5. I would suggest to add practical implications of this research under the Discussion section.

6. In the Discussion section limitation of this study and future scope could be added.

Conclusion:

1. Line 545-550: First four sentences of Conclusion section are repetition that are already told in objective and method section. Rewrite these into one introductory sentence of Conclusion and write the conclusion with more focus on main finding and recommendation and policy making for government.

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Reviewer #1: No

Reviewer #2: Yes: Dr. Md. Jahangir Alam

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PLoS One. 2024 Mar 15;19(3):e0300648. doi: 10.1371/journal.pone.0300648.r002

Author response to Decision Letter 0

21 Jul 2023

Manuscript number: PONE-D-23-09867

Title: Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh

Journal: PLOS ONE

Dear Academic Editor,

Thank you for the comments concerning our manuscript. We deeply appreciate your positive evaluation of our work. Those comments are valuable and very helpful. We have read through comments carefully and have made corrections. Please see below; all tasks and revisions taken are shown point-by-point.

Response to Academic Editor comments

Comments #1: Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Response to comment #1: We tried to meet the PLOS ONE’s style requirements.

Comments #2: Thank you for submitting the above manuscript to PLOS ONE. During our internal evaluation of the manuscript, we found significant text overlap between your submission and previous work in the [introduction, conclusion, etc.]. Please revise the manuscript to rephrase the duplicated text, cite your sources, and provide details as to how the current manuscript advances on previous work.

Response to comment #2: Thank you for this comment. We made an attempt to rephrase the manuscript.

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Response to comment #3: We have uploaded the minimal anonymized dataset and subsequently adjusted the data availability statement accordingly.

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Response to comment #4: We obtained the necessary permission from the authority to use the shape file, and a copy of the permission letter has been attached.

Thank you once again for your precious comments and advice. Those comments are all valuable and very helpful for revising and improving our manuscript. We have revised the manuscript accordingly, and our point-by-point responses are presented above. We hope you are satisfied with our answers and the new data we have provided. Our deepest gratitude goes to you for your careful work and thoughtful suggestions that have helped improve this paper substantially.

Sincerely yours

All Authors’

Response to Reviewer’s comments

Reviewer #1:

Manuscript number: PONE-D-23-09867

Title: Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh

Journal: PLOS ONE

Dear Reviewer,

Thank you for the comments concerning our manuscript. We deeply appreciate your posi-tive evaluation of our work. Those comments are valuable and very helpful. We have read through comments carefully and have made corrections. Please see below; all tasks and re-visions taken are shown point-by-point.

Comment #1: In this paper, the authors performed a spatio-temporal mapping of the area, production, and yield of rice from 2006-2007 to 2019-2020 using secondary data for dis-aggregating 64 districts in Bangladesh. They also looked at the adoption rate of high-yielding varieties of rice and did cluster analysis. The authors of this paper has also pub-lished a similar paper (same lead author) in PLOS One with the title “Growth and trend analysis of area, production and yield of rice: A scenario of rice security in Bangladesh (Al Mamun M.A, Nihad SAI, Sarkar M.AR, Aziz M.A, Qayum M.A, Ahmed R, et al. 2021. Growth and trend analysis of area, production and yield of rice: A scenario of rice security in Bangladesh. PLoS ONE 16(12): e0261128. https://doi.org/10.1371/journal.pone.0261128”). The current manuscript is a sub-set of the other published paper. In the published paper, they used data for the period of 1969–70 to 2019–20 at the region level (old district level, there were 20 old districts which are currently named as region, the statistical data until early 2005 were available only at that level). In this paper they are using the current 64 districts (20 regions were divided into 64 districts) for which data are available for the period of 2006 to 2020 which they used in this manuscript). Many things are common between these two papers (such as maps of area, production, yield and adoption rate, statistical parameters, cluster analysis, etc.). There is no new message in this manuscript. So, what is the novel aspect of this paper?

Response to comment #1: Thank you for your comment. Yes, our research paper on the growth and trend analysis of rice area, production, and yield has been published in PLOS ONE. In the aforementioned publication, our primary focus was on the regional context, aiming to derive meaningful findings within the framework of 14 specific locations. How-ever, it is important to recognize that the rice-growing ecosystem in Bangladesh is charac-terized by a high degree of diversity, driven by factors such as geographical position, socio-economic conditions, and environmental variations. So, the findings of the previous study have limitations in terms of their statistical robustness and generalizability for the formula-tion of region-specific policies. To address these limitations, our current study takes a more disaggregated approach, analyzing data from 64 locations, in order to provide a comprehen-sive understanding of the dynamics of rice cultivation in Bangladesh. We believe that this shift in focus not only enhances the statistical validity of our research but also facilitates the formulation of more effective policies tailored to specific locations. However, we greatly appreciate your insightful comments, and as a result, we have made efforts to incorporate the research novelty into the introduction section of our manuscript. For a detailed elabora-tion of the novelty and distinctiveness of our current research, please refer to the table pro-vided below.

Table 1. Comparison of two manuscripts

Particulars Previous pa-per Current paper Remarks

Study area 14 agricultural regions 64 districts The rice growing ecosystem in Bangladesh exhibits significant diversity, making it essen-tial to initiate strategies and policy formula-tion at the grassroots level. In our previous study, we focused on 14 agricultural regions to gather information at the regional level. However, in our current study, we analyzed data at a disaggregated level, specifically ex-amining the 64 districts of Bangladesh. Ex-panding the study from 14 locations to 64 locations in Bangladesh was justified for sev-eral reasons. Firstly, by including a larger number of locations, we were able to capture a more comprehensive and representative pic-ture of rice cultivation practices and produc-tion across the country. This broader scope allowed us to identify regional variations, un-derstand diverse agricultural practices, and uncover unique challenges specific to each location. More specifically, by studying 64 districts, we gained a deeper understanding of the spatial distribution and dynamics of rice cultivation, which would have been limited in a study confined to only 14 regions. Second-ly, the inclusion of additional locations en-hanced the statistical robustness and generali-zability of our findings. With a larger sample study location, we were able to obtain more reliable estimates and draw more meaningful conclusions about rice cultivation patterns in Bangladesh. The increased geographical cov-erage provided a more accurate representation of the overall situation, minimizing potential biases that could arise from studying a small-er subset of regions. Furthermore, the expan-sion to 64 districts enabled us to better tailor our recommendations and policy implications to the specific needs and challenges faced by a wider range of regions, ensuring that the findings have practical relevance and applica-bility at a national level.

Data period 1969-70 to 2019-20 2006-07 to 2019-20 In order to align with the research objectives of our present study, we utilized 14 years of data (from 2006-07 to 2019-20) pertaining to rice area, production, and yield at the district level. For this purpose, we relied on the Bang-ladesh Bureau of Statistics (BBS) as the sole national statistical data repository in the country. The availability of data at the district level spans from 2006 onwards, while data prior to 2006 was only available at the region-al level. Therefore, in our previous study, we conducted a regional-level analysis using data ranging from 1969-70 to 2019-20. In that study, we utilized national-level data from 1969-70 to 2019-20 and regional-level data from 1984-85 to 2019-20, taking into consid-eration the availability of relevant data for each level of analysis. Thus, the choice of data period in our studies is primarily guided by the study objectives and the availability of data.

Statistical method Durbin-Watson test, Exponential growth

model, Cochrane-Orcutt itera-tion method, and k-means cluster analy-sis Augmented Dickey-Fuller test, Shapiro-Wilk normality test, Exponential growth model, Principal compo-nent analysis, and Hierarchical clus-ter analysis This study nature is a partial continuation of the previous one, employing a similar growth model to estimate growth and conduct trend analysis in both cases. However, there are notable differences in the methodology. In the previous study, we utilized the k-means cluster method, where only average data points were considered for clustering. In con-trast, for the present study, we applied robust multivariate time series clustering techniques, specifically dynamic time warping, which enabled us to use time series data points for more precise cluster identification. Addition-ally, we employed principal component analy-sis and optimal clustering methods to identify the most suitable clusters in our current study.

Study ob-jective Region map-ping via growth, trend and cluster analysis District mapping via growth, trend and cluster analy-sis

Nexus between adoption and pro-duction growth

District-level area and production forecasting The present study aims to provide a compre-hensive understanding of rice cultivation at the district level through growth, trend, and cluster analysis. Specifically, we focus on mapping the growth patterns and trends in rice production within each district, while also exploring the relationship between adop-tion rates of new technologies and the corre-sponding production growth. Additionally, we aim to forecast rice cultivation area and pro-duction at the district level. In contrast, our previous study focused on regional mapping, analyzing growth, trends, and clusters at a broader regional level rather than the district level. By shifting our focus to the district lev-el in the present study, we can provide more granular insights into the spatial dynamics of rice cultivation and better inform policy and decision-making processes.

Study con-text-1 - District-wise de-scriptive statistics of area, produc-tion and yield In the current paper, we employed descriptive statistics to offer a comprehensive overview of rice cultivation area, production, and yield at the district level over a 14-year period. This analysis enables us to summarize data, ex-plore patterns, facilitate comparisons, and support arguments. Moreover, it provides a crucial foundation for subsequent analysis and interpretation while enhancing the clarity and communicability of our research findings.

Study con-text-2 Production contribution by seasons and regions - While the current paper includes comprehen-sive descriptive statistics, it does not present the results regarding production contribution.

Study con-text-3 Periodic trend and growth assessment by regions and seasons Yearly trend and growth by dis-tricts and seasons In the previous study, our focus was on con-ducting trend and growth analyses within a regional context, limited by specific periods, which hindered our ability to uncover region-al heterogeneity. In contrast, in the present study, we expanded our examination to the district level, enabling us to conduct trend and growth analyses that capture a greater degree of regional heterogeneity.

Study con-text-4 Periodic adop-tion of modern varieties by regions and seasons The average adoption rate (%) of modern varie-ties and its tem-poral variation via GIS map In the earlier study, the adoption rate (%) of high-yielding variety (HYV) of rice was pre-sented across three distinct periods; however, the spatial distribution of this adoption was not analyzed. In contrast, our current paper offers a more comprehensive analysis by ex-amining the spatiotemporal variation of HYV adoption at the district level and representing it through GIS mapping. This approach pro-vides a valuable understanding of the geo-graphical patterns and temporal changes in HYV adoption, enhancing our knowledge of this important factor (technological adoption) in rice cultivation.

Study con-text-5 Clustering rice growing re-gions based on the production growth and HYV adoption Spatial clustering and classification of rice-growing districts based on cultivation area, production, and yield In our previous paper, we employed the k-means clustering technique to cluster 14 re-gions based on the growth rates of rice pro-duction and high-yielding variety adoption. However, this approach had limitations in identifying regional heterogeneity as it fo-cused on mean values and had a limited scope. Considering the highly diverse and regionally specific factors influencing the rice growing ecosystem in Bangladesh, we sought to address this limitation in our current study. To achieve more comprehensive and precise results, we utilized time series data of rice cultivation area, production, and yield at the district level as input parameters for cluster-ing. We employed robust multivariate cluster-ing techniques, specifically dynamic time warping (DTW), which takes into account technological advancements, farmers' prefer-ences, government initiatives, and environ-mental interactions associated with rice culti-vation. The cluster analysis resulted in group-ing similar districts, facilitating the creation of a rice zoning map and providing valuable insights for policy implications.

Study con-text-6 - Effect of modern varieties adoption on rice produc-tion by districts and seasons This section represents a new addition to our current paper, focusing on examining the im-pact of high-yielding variety (HYV) adoption on rice production in various districts of Bangladesh, assuming all other factors re-mained constant. Our findings revealed that a 10% increase in HYV adoption led to a vary-ing range of rice production increase, ranging from 0.04% to 5.8% across different seasons, with a few exceptions. In this analysis, HYV adoption served as a proxy for technological advancement. To the best of our knowledge, the extent to which this technological ad-vancement has influenced rice production in specific seasons and districts of Bangladesh has not been previously revealed. This study provides novel evidence in this regard, shed-ding light on the relationship between HYV adoption and rice production at the district level.

Study con-text-7 - District-level pro-jections of rice area and produc-tion changes an-ticipated by the year 2030 This section introduces a new addition to our current paper, with a focus on identifying future challenges that may hinder the achievement of doubling agricultural produc-tivity by 2030, specifically in sustaining rice production. While previous studies have of-fered forecasts for national-level rice area and production, our study takes a step further by providing projections for changes in district-level area and production from 2020 to 2030, visualized through GIS mapping. This ap-proach enables a comprehensive situational analysis, offering valuable insights into the required actions and strategies to effectively implement the 2030 global development agenda.

Comment #2: The paper is mostly the presentation of the secondary data in maps and charts though they are hardly readable. The quality of the figures and graphs are very poor as I mentioned in detail in my specific comments below.

Response to comment #2: We appreciate your feedback regarding the presentation of the secondary data in maps and charts. We acknowledge that the figures and graphs may have been difficult to read due to their low resolution in the PDF copy. However, we would like to inform you that we have uploaded the figures in TIFF format, ensuring higher resolution and larger file sizes. We kindly request you to refer to the original figures for better clarity and readability.

Comment #3: The discussion section of the paper is mostly not much relevant. Many sig-nificant factors such as rapid urbanization and industrialization on the agricultural lands, varieties of rice grown in different districts, problems of flood, drought and salinity in the coastal region are significant factors in future rice cultivation which should have been dis-cussed.

Response to comment #3: Thank you for your comment and valuable suggestions. We have taken your feedback into consideration and made revisions to the discussion section accord-ingly. We have included a more comprehensive discussion on significant factors such as rapid urbanization and industrialization affecting agricultural lands, the diversity of rice varieties grown in different districts, and the challenges posed by issues like flood, drought, and salinity in the coastal region. We believe these additions have enhanced the relevance and completeness of our discussion. We appreciate your input and the opportunity to im-prove the paper.

Comment #4: Abstract and introduction and in other places in the manuscript: Spatial – temporal should be spatio-temporal

Response to comment #4: Thank you for your comment. We have made the necessary changes throughout the manuscript by replacing the term "Spatial - temporal" with "Spatio-temporal" to ensure consistency and accuracy.

Comment #5: Lines 59-68: The word rice is used and the statistics for milled-rice is given for the world (787 tons) in lines 59-65. However, in line 66 the authors mentioned about the average paddy yield. Please clarify the average paddy yield. Is this milled-rice or yield at the farm after the harvest, un-milled rice? This always create confusion and it is not clear in the Bangladesh statistics whether the yield reported in for milled rice or un-milled paddy rice.

Response to comment #5: Thank you for bringing this to our attention. Here, paddy means un-milled rice. We have made the necessary clarification by adding the definition of "pad-dy" in the brackets.

Comment #6: Line 74: Projected population growth to 189.9 – provide reference for that.

Response to comment #6: Thank you for bringing this to our attention. We have added a reference to support this projection.

Comment #7: Line 76: Reference 9 given in the list does not have journal name.

Response to comment #7: Thank you for bringing this to our attention. We have made the necessary correction to the reference.

Comment #8: Fig.1 What does inter-district alignment mean? The text in the figure partic-ularly in the right on is not readable.

Response to comment #8: Thank you for your comment. In this context, inter-district alignment refers to the occurrence of agro-ecological zones (AEZs) overlapping in different districts. In Bangladesh, there are 30 AEZs distributed across 64 districts with overlapping zones. We acknowledge that the figures in the PDF copy may have had low resolution, mak-ing them difficult to read. However, we would like to inform you that we have uploaded the figures in TIFF format, ensuring higher resolution and larger file sizes. We kindly request you to refer to the original figures for better clarity and readability.

Comment #9: Line 193: Showing area in has up to 2 decimals is unnecessary. Please re-move decimal places in all.

Response to comment #9: Thank you for your suggestion. We have made the necessary re-visions to this section accordingly.

Comment #10: Lines 205 to 212: Please explain whether this is the yield of milled rice or unmilled rice.

Response to comment #10: Thank you for bringing this to our attention. In fact, this refers to milled rice, and we have already mentioned milled rice in the first line of the paragraph.

Comment #11: Lines 217-218: Among the three seasons Aman season …., respectively – not clear. Please rephrase.

Response to comment #11: Thank you for your suggestion. We have revised the sentence as per your recommendation. Please see the text, “The Aman season exhibited standard error of the mean (SEM) values of 2.17% for cultivation area, 3.87% for production, and 2.91% for yield”.

Comment #12: Line 229-230: “We examined season-wise assessments and their aggregated aspects to determine the impact of the leading season on national rice security in Bangla-desh” - Not clear.

Response to comment #12: Thank you for bringing this to our attention. We have revised the sentence as per your recommendation.

Comment #13: Fig.7: What does this % numbers mean? It is not clear in the text. What are the 10 principal components as mentioned in the fig caption?

Response to comment #13: Thank you for your comment. Regarding the principal compo-nent analysis (PCA), the percentages represent the amount of explained variation by each individual principal component (PC). The PCA technique allows us to specify the number of components to consider. In our analysis, we utilized 10 PCs to capture the variation in the multivariate data. These components serve as indicators of the explanatory power of the da-ta, with the first few components explaining the majority of the variation. Specifically, we observed that the first five PCs accounted for approximately 93.21%, 97.14%, and 96.02% of the total variances in the Aus, Aman, and Boro seasons, respectively.

Comment #14: Figs 8-10: What additional information do they provide? The information presented here can be easily presented in the Fig.2 district wise and then arranging them in ascending or descending order to show the cluster. In addition, there is spatial maps which shows the variations. So new information do they provide? Just another figures. The text in the figures are not readable at all.

Response to comment #14: Thank you for your comment. In our analysis, Figure 2 illus-trates the average performance (mean and standard error) of area, production, and yield for different seasons spanning from 2006-2007 to 2019-2020. It provides an overview of the trends without considering the clustering aspect. Our research focuses on three parameters: area, production, and yield. When dealing with a single parameter, clustering is relatively straightforward. However, when multiple parameters are involved, clustering becomes more challenging, requiring the application of statistical techniques.

For example, if we were to cluster based on ascending order of area using the mean value, the time variation effect would be disregarded, resulting in a regional area cluster. On the other hand, clustering based on production or yield could potentially result a different re-gional cluster. With multiple parameters, it becomes challenging to establish fixed regional clusters as the cluster of area may not be similar to the production cluster or yield cluster. To address this issue, we rely on multivariate statistical techniques to identify unique clus-ters based on the three parameters: area, production, and yield.

To achieve more comprehensive and precise results, we employed robust multivariate time series clustering techniques, specifically dynamic time warping, which enables us to utilize time series data points for more accurate cluster identification. Furthermore, we utilized principal component analysis and optimal clustering methods to identify the most suitable clusters for our study. By incorporating time series data of rice cultivation area, production, and yield at the district level as input parameters, we captured the heterogeneity in patterns across districts, attributable to geographical positions, technological dissemination, farmers' responsiveness, and climatic and edaphic factors. So, the cluster analysis resulted in the grouping of similar districts, facilitating the creation of a rice zoning map and providing valuable insights for policy implications.

We acknowledge that the text in the figures may have been difficult to read in the PDF copy due to their low resolution. However, we have uploaded the figures in TIFF format, ensuring higher resolution and larger file sizes. We kindly request you to refer to the original figures for better clarity and readability.

Comment #15: Lines 314: What are the characteristics of different clusters? In Fig. 8, C1 to C5 were used as clusters? What C1 to C5 indicates in terms of area, yield, and production?

Response to comment #15: Thank you for your comments. In our study, we utilized long-term data of area, production, and yield as input parameters for identifying clusters using a multivariate clustering technique. The logic behind cluster analysis is to uncover hidden structures or patterns within a dataset and group similar observations together. More specif-ically it helps to identify groups or clusters within a dataset based on similarities or dissimi-larities between observations. In the case of the Aus season (Fig. 8), we identified five clus-ters out of the 64 districts. Each cluster represents a group of districts with similar charac-teristics in terms of cultivation area, production, and yield. Additionally, these clusters cap-ture regional heterogeneity and agro-ecological conditions, enabling effective policy formu-lation and implementation.

For example, the characteristics of Cluster 5 in Fig. 8 includes three districts (Bhola, My-mensingh, and Patuakhali) with similar dynamics in Aus rice cultivation. These districts are located in two coastal and one central region of Bangladesh. The rice cultivation area (high-est among the clusters) in this cluster ranges from 15,000 to 88,000 hectares, with more sta-ble rice production ranging from 40,000 to 170,000 tons and the highest mean rice produc-tion. The average yield is 1.8 tons per hectare, with a minimum of 1.2 tons per hectare and a maximum of 2.8 tons per hectare. Furthermore, six agro-ecological zones (AEZs) namely 8, 9, 13, 18, 28, and 29 have been identified in terms of Aus rice cultivation area, production, and yield within this cluster, indicating similar agro-ecological conditions in those regions for Aus cultivation.

Similarly, each cluster exhibits unique characteristics that provide valuable insights for pol-icymakers and researchers in formulating effective policies and taking immediate initia-tives to sustain rice production in Bangladesh.

Comment #16: Lines 319: Elbow criterion – not readable so difficult to understand.

Response to comment #16: Thank you for your comments. We have included a footnote to provide a clear explanation of the 'elbow criterion'. Please refer to the following text: “The elbow criterion is a method used in cluster analysis to determine the optimal number of clusters in a dataset. It involves plotting the variance explained by the clusters against the number of clusters. The plot typically resembles an arm, and the "elbow" or bend in the plot represents the point where the addition of more clusters does not significantly reduce the variance. This point is considered the optimal number of clusters for the given dataset. The elbow criterion helps in selecting a reasonable number of clusters that balance capturing meaningful patterns in the data while avoiding overfitting.”

Comment #17: Lines: 321- 329: Please see my comments above.

Response to comment #17: Thank you for your comments. We have addressed the im-portance of clustering in response to comment #14 and provided an explanation of the key characteristics of each cluster in response to comment #15. Please refer to the aforemen-tioned responses for more details.

Comment #18: Line 325: Poorest rice production – what does it mean? I think you wanted to mean lowest total production.

Response to comment #18: Thank you for bringing this to our attention. We have revised the sentence as per your recommendation.

Comment #19: Lines 337 – 365: Comments related to cluster analysis mentioned above applies here as well.

Response to comment #19: Thank you for your comments. We have addressed the im-portance of clustering in response to comment #14 and provided an explanation of the key characteristics of each cluster in response to comment #15. Please refer to the aforemen-tioned responses for more details.

Comment #20: Lines 366 – 370: Increasing trend of what? I understand this could be the area but there is no mention of it in the text or in the figure 11. This should be clear that of the total cultivated area, xx% were on HYV cultivation. This is also country level data. Up to this point, the data was presented at the district level. Here, there is no mention of wheth-er the data and the figure are at country level or district level.

Response to comment #20: Thank you for your comments. We have made the necessary re-visions to this section accordingly.

Comment #21: Fig 11: Adoption (%) - % of what? Should be mentioned. Same applies to Fig. 12.

Response to comment #21: Thank you for your comments. We have made the necessary re-visions to these figures accordingly.

Comment #22: Fig 12: Spatial distribution of adoption rate presented here is very confus-ing. Adoption rate for each district for 14 years were classified into different groups. But this data should have two dimensions on is temporal and the other is spatial. How the au-thors presented these in these maps? What is the point of presenting the previous years? I think the authors can take the adoption rate of the last year and then classify them in differ-ent group (64 district into different group) which then can be presented in a map.

Response to comment #22: Thank you for your comments. We sincerely apologize for any confusion caused by Fig 12 and appreciate the opportunity to clarify it. In Fig 12, we have utilized 14 years of high-yielding variety (HYV) adoption data from 64 districts. This data encompasses two dimensions: temporal and spatial. Our aim is to illustrate the spatial dis-tribution and temporal variations in the adoption rate of HYVs in rice cultivation across Bangladesh.

If we were solely interested in the spatial distribution, it would be sufficient to present the adoption rate of the current year. However, this approach would not capture the dynamic changes and fluctuations in HYV adoption over time. As varietal adoption is subject to dy-namic shifts, relying solely on the current year's data might overlook important temporal variations. Moreover, cultivating a particular variety in a single year does not guarantee its cultivation in the following year.

Given the two-dimensional nature of the data and our objective to track both spatiotemporal variations, a statistical technique is required. For the spatial distribution, we have employed the mean value of the 14-year data for each district, which is represented in the map as one of the legends "HYV adoption (%)." Additionally, we have included another legend in the form of "coefficient of variation (%)" to depict the temporal variability of HYV adoption in each district. The coefficient of variation (CV) was calculated using the 14-year data points for each district and visualized as circles on the map.

Therefore, to fulfill our research objective and ensure clarity, it is essential to consider the multi-year data. We have revised the title of Fig 12 accordingly. We hope that our explana-tion satisfactorily addresses your concerns.

Comment #23: Line 395 -396: Districtwide adaptation scenario – is it adaptation or adop-tion? In the whole section of 3.5, adaptation was used in the text whether in the title it is adoption.

Response to comment #23: Thank you for bringing this to our attention. We sincerely apol-ogize for the typo mistake. The correct term should be "adoption" instead of "adaptation" in section 3.5. We appreciate your feedback, and we have made the necessary revisions to en-sure consistency throughout the section.

Comment #24: Fig 13: Again it is hard to understand the message from the figure caption or the axis headings. What does influence mean here? Increase or decrease of yield or pro-duction?

Response to comment #24: Thank you for your comments. We deeply regret any confusion caused by Fig 13 and we are grateful for the chance to provide clarification. We have re-vised Section 3.5 and included the methodology in Section 2.4 to ensure a clear explanation and improve understanding. We kindly request you to review these sections, and we hope that the revisions will address your concerns adequately.

Comment #25: Lines 432-441: Mostly already described in the introduction (lines 78 to 86).

Response to comment #25: Thank you for your comments. We have taken note of your sug-gestion and have removed the redundant information as per your recommendation. We ap-preciate your feedback in streamlining the content of the manuscript.

Comment #26: Lines 448-452: Repetition of results

Response to comment #26: Thank you for your continued feedback. We have carefully con-sidered your comment and have made the appropriate changes to ensure that the content is concise and avoids repetition. We appreciate your diligence in reviewing our manuscript and helping us enhance its quality.

Comment #27: Line 452-453: “The bulk of the aromatic rice is grown during the Aman season which may lead to high Aman rice production in Dinajpur” what about area. Aro-matic rice nothing to do with the high production it is the area or the yield. There is no dis-cussion on the yield differences and on varieties. In some areas, local varieties or HYV with lower yield are grown which has impact on overall production.

Response to comment #27: Thank you for your comments. We have made the necessary re-visions to this section accordingly.

Comment #28: Lines 454- 462: Any references for this?

Response to comment #28: Thank you for pointing out the need for references in lines 454-462. We apologize for the oversight and have now included the relevant citations as per your suggestion.

Comment #29: Lines 491 – 496: Any data or references?

Response to comment #29: Thank you for pointing out the need for references in lines 491-496. We apologize for the oversight and have now included the relevant citations as per your suggestion.

Comment #30: Lines 499-502: Vertical and hydroponic farming for rice? Any references?

Response to comment #30: Thank you for bringing this to our attention. We appreciate your comment and have removed the reference line to vertical and hydroponic farming for rice from the manuscript.

Comment #31: Lines 529-532: Repetitive.

Response to comment #31: Thank you for your comments. We have taken them into consid-eration and have removed the mentioned lines from the manuscript.

Thank you once again for your precious comments and advice. Those comments are all val-uable and very helpful for revising and improving our manuscript. We have revised the manuscript accordingly, and our point-by-point responses are presented above. We hope you are satisfied with our answers and the new data we have provided. Our deepest gratitude goes to you for your careful work and thoughtful suggestions that have helped improve this paper substantially.

Sincerely yours

All Authors’

Response to Reviewer’s comments

Reviewer #2:

Manuscript number: PONE-D-23-09867

Title: Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh

Journal: PLOS ONE

Dear Reviewer,

Comment #1: The research topic “Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh” is substantive and is within the scope of the journal. In general, the paper is thoroughly researched and well-written. By the way, I have some minor comments for the authors that would help to improve the quality of the manuscript.

Response to comment #1: We would like to express our gratitude for your valuable and in-sightful suggestions and comments regarding the improvement of our manuscript. We firm-ly believe that they have significantly enhanced the scientific value of the manuscript, and we sincerely appreciate your contributions.

Abstract

Comment #2: Line # 35: Authors wrote “… performed a spatial-temporal mapping of the area, production, and yield….”. Is the word “area” sufficient or it is “cultivated/cultivation area”?

Response to comment #2: Thank you for the suggestion. The word “cultivation” has been added.

Comment #3: Line # 37 – 38: Replace “Results show that …” with “Results showed that….”.

Response to comment #3: The word 'show' has been replaced with 'showed'.

Comment #4: Line # 41: “…the rice area in 19 districts, 11 districts, and 13 districts de-clined significantly”. The word “rice area” is not a suitable wording. Please replace it with “rice-cultivation-area” or “cultivation area”.

Response to comment #4: The word "cultivation" has been added before the word "area".

Introduction

Comment #5: Line # 60-63: Please add the information “over 3.5 billion people are solely dependent upon rice for at least 20% of their daily required calories” from Introduction sec-tion of “Alam, M. J., Alamin, M., Sultana, M. H., Ahsan, M. A., Hossain, M. R., Islam, S. S., & Mollah, M. N. H. (2020). Bioinformatics studies on structures, functions and diversifica-tions of rolling leaf related genes in rice (Oryza sativa L.). Plant Genetic Resources, 18(5), 382-395” with the sentence “With over half of the world's population depending on rice for their daily energy, and it supplies approximately 62% carbohydrate, 46% protein, 8% fat, 7% calcium, and 44% phosphorus of the recommended dietary allowance” and cite Alam et al. 2020.

Response to comment #5: We appreciate your suggestion, and we have included the provid-ed information and incorporated the reference you suggested. Thank you for your valuable input.

Comment #6: Line # 65-66: Please add citation for the sentence “China is the leading rice-producing country, followed by India, Bangladesh, and Vietnam”.

Response to comment #6: We appreciate your observation. The citation for the sentence "China is the leading rice-producing country, followed by India, Bangladesh, and Vietnam" has been duly included.

Comment #7: Line #103-104: “This paper analyzed spatiotemporal” is not a standard word-ing. Please reshape the sentence as “In this study, we analyzed spatiotemporal data on culti-vation area, and production and yield of rice to examine/investigate the trends and growth patterns from 2006-2007 to 2019-2020 in Bangladesh. Do not mention the data sources un-der Introduction section, rather write it under Materials and Methods section.

Response to comment #7: Thank you for the suggestion. We have revised the sentence ac-cordingly.

Materials and Methods

Comment #8: Line # 120: Replace the word “area” with “cultivation area”. Also, this is ap-plicable for whole body of this manuscript.

Response to comment #8: We have made the necessary adjustment by replacing the term "area" with "cultivation area" throughout the entire manuscript, as suggested.

Comment #9: Please add a sentence to mention the level of significance considered for dif-ferent statistical tests (e.g., normality test, t-test, etc.) under “Statistical analysis” subsec-tion.

Response to comment #9: As per your recommendation, we have included the significance level of 5% in the analysis. Thank you for the suggestion.

Results

Comment #10: 1. Line # 233: “… seasons in Bangladesh is illustrated in Fig 4”. Replace “is” with “are”.

Response to comment #10: The word 'is' has been replaced with 'are'.

Comment #11: 2. Line # 293: Add unit of measurement for area, production and yield with-in brackets.

Response to comment #11: We have incorporated the percentage as a unit of measurement for the growth rate.

Comment #12: 3. Line # 425: Replace “will” with “would” in line # 425.

Response to comment #12: The word 'will' have been replaced with 'would'.

Discussion

Comment #13: Line # 484: Add an “and” before the phrase “declined over time”.

Response to comment #13: We have corrected it accordingly.

Comment #14: Line # 485: Replace “rice area” with “rice cultivation areas”.

Response to comment #14: We have corrected it accordingly.

Comment #15: Line # 555: Add “were” verb before the phrase “revealed in five ….” in line no. 555.

Response to comment #15: We have added ‘were’ verb before the phrase “revealed in five ….” accordingly.

Comment #16: Line # 561: Replace “will” with “would” in line no. 561.

Response to comment #16: The word 'will' have been replaced with 'would'.

Comment #17: I would suggest to add practical implications of this research under the Dis-cussion section.

Response to comment #17: Thank you for the suggestion. We have made an effort to incor-porate the practical implications of this research into the discussion section. Please see the text, “The research findings have significant practical implications for informing and guid-ing future agricultural practices and policies in rice-intensive areas of Bangladesh. The study provides valuable insights into the dynamic changes in the area and production of rice cultivation at a more disaggregated level, representing diverse ecosystems and man-agement constraints. Policymakers can utilize this information to develop targeted strate-gies and interventions aimed at enhancing rice production in specific regions. For instance, regions with favorable environmental conditions and fertile soil content can be encouraged to prioritize expanding rice cultivation. Efforts can be directed towards providing farmers in these regions with access to modern high-yielding varieties and advanced agricultural technologies to improve productivity. Conversely, areas facing challenges such as salinity or hilly terrain require tailored approaches to overcome these limitations, such as the de-velopment of stress-tolerant rice varieties and the implementation of appropriate irrigation systems. Additionally, identifying regions where rice production is declining, or farmers are shifting to alternative crops can guide initiatives aimed at revitalizing rice cultivation through improved irrigation facilities, training programs, and the development of short-duration varieties. The study highlights the areas that require closer attention to overcome challenges and enhance productivity, ultimately contributing to achieving Sustainable De-velopment Goal 2.3 of doubling agricultural productivity and income for smallholder farm-ers by 2030. In summary, these practical implications can contribute to sustainable agricul-tural practices, enhance food security, and improve the livelihoods of farmers in Bangla-desh.”

Comment #18: In the Discussion section limitation of this study and future scope could be added.

Response to comment #18: Thank you for the suggestion. We have made an effort to incor-porate the limitation and future research scope into the discussion section. Please see the text, “Despite the valuable insights provided by this study, there are certain limitations that should be acknowledged. First, this research focused primarily on the quantitative analysis of rice cultivation area and production trends, and did not delve into the underlying socio-economic factors that may influence these trends. Additionally, this study relied on second-ary data sources, which may be subject to limitations such as data accuracy and availabil-ity. Conducting primary data collection through field surveys and interviews could enhance the accuracy and reliability of the findings. Furthermore, the study's scope was limited to a specific timeframe (2006-2019), and it is important to recognize that future changes in cli-mate, technology, and agricultural practices could impact rice cultivation in unforeseen ways.

Building on the findings and limitations of this study, there are several areas for future re-search that can further contribute to the understanding of rice cultivation in Bangladesh. Firstly, investigating the socio-economic factors that influence farmers' decision-making processes regarding rice cultivation, including factors such as market conditions, govern-ment policies, and farmers' preferences, would provide valuable insights into the drivers of rice production. Secondly, exploring the impact of climate change on rice cultivation, in-cluding the effects of rising temperatures, changing rainfall patterns, and increased occur-rences of extreme weather events, is crucial for developing climate-resilient agricultural strategies. Additionally, examining the adoption and effectiveness of specific interventions aimed at improving rice productivity, such as the dissemination of high-yielding varieties, the implementation of irrigation systems, and the provision of training and extension ser-vices, would help in identifying best practices and areas for improvement. Finally, integrat-ing remote sensing and geospatial analysis techniques can enhance the accuracy and time-liness of monitoring rice cultivation dynamics, allowing for more precise and up-to-date assessments of cultivation area and production trends.”

Conclusion

Comment #19: Line 545-550: First four sentences of Conclusion section are repetition that are already told in objective and method section. Rewrite these into one introductory sen-tence of Conclusion and write the conclusion with more focus on main finding and recom-mendation and policy making for government.

Response to comment #19: We greatly appreciate your valuable suggestion, which has prompted us to thoroughly revise the conclusion section and incorporate essential policy recommendations.

Sincerely yours

All Authors’

Attachment

Submitted filename: Response to Reviewer 2.docx

pone.0300648.s003.docx^{(22.1KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0300648.r003

Decision Letter 1

Abul Khayer Mohammad Golam Sarwar

9 Jan 2024

PONE-D-23-09867R1

Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh

PLOS ONE

Dear Dr. Al Mamun,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we have decided that your manuscript does not meet our criteria for publication and must therefore be rejected.

I am sorry that we cannot be more positive on this occasion, but hope that you appreciate the reasons for this decision.

Kind regards,

Abul Khayer Mohammad Golam Sarwar

Academic Editor

PLOS ONE

Additional Editor Comments:

fails to show any novelty even after the major revision. Please see the reviewer's comment.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #3: All comments have been addressed

Reviewer #4: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #3: Yes

Reviewer #4: No

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #3: Yes

Reviewer #4: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #3: Yes

Reviewer #4: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #3: Yes

Reviewer #4: No

**********

6. Review Comments to the Author

Reviewer #3: The authors have fairly addressed all the comments. In this regard, this article can be published in this journal. Good luck!

Reviewer #4: Sorry to say that there is nothing new in the manuscript. A lot of similar studies have been performed. These cited studies are missing in this paper. I suggest rejecting this paper due to lack of novelty.

**********

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PLoS One. 2024 Mar 15;19(3):e0300648. doi: 10.1371/journal.pone.0300648.r004

Author response to Decision Letter 1

30 Jan 2024

Manuscript number: PONE-D-23-09867

Title: Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh

Journal: PLOS ONE

Dear Academic Editor,

Response to Academic Editor comments

Comments #1: Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

Response to comment #1: We tried to meet the PLOS ONE’s style requirements.

Response to comment #2: Thank you for this comment. We made an attempt to rephrase the manuscript.

Response to comment #3: We have uploaded the minimal anonymized dataset and subsequently adjusted the data availability statement accordingly.

Response to comment #4: We obtained the necessary permission from the authority to use the shape file, and a copy of the permission letter has been attached.

Sincerely yours

All Authors’

Manuscript number: PONE-D-23-09867R1

Title: Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh

Journal: PLOS ONE

Dear Academic Editor,

Response to Academic Editor comments

Comments #1: Fails to show any novelty even after the major revision. Please see the reviewer's comment.

Response to comment #1: Thank you for your thorough review and valuable feedback. While we respect your perspective, we assert that our manuscript offers unique contributions not fully covered in previous studies. Recognizing the significance of novelty in research, we have carefully revised the introduction section to highlight the distinctive aspects of our work. Additionally, we have incorporated relevant citations to address updated references, aiming to enhance the overall value of our manuscript. We hope that upon reevaluation, you will find our revised work to be a meaningful addition to the existing literature.

Regarding your comment on our research paper's publication in PLOS ONE, we appreciate your acknowledgment. In our previous study, we focused on the regional context within 14 specific locations, but we recognize the inherent diversity in Bangladesh's rice-growing ecosystem. To address this, our current study takes a more detailed approach, analyzing data from 64 locations for a comprehensive understanding of rice cultivation dynamics. This shift enhances the statistical validity of our research and facilitates the formulation of more effective, location-specific policies. We have incorporated these refinements, including the research novelty, into the introduction section based on your insightful comments. For detailed insights into the novelty of our research, kindly refer to response of Comment #1 from Reviewer #1.

Our research findings have significant practical implications for informing and guiding future agricultural practices and policies in rice-intensive areas of Bangladesh. The study provides valuable insights into the dynamic changes in the area and production of rice cultivation at a more disaggregated level, representing diverse ecosystems and management constraints. Policymakers can utilize this information to develop targeted strategies and interventions aimed at enhancing rice production in specific regions. For instance, regions with favorable environmental conditions and fertile soil content can be encouraged to prioritize expanding rice cultivation. Efforts can be directed towards providing farmers in these regions with access to modern high-yielding varieties and advanced agricultural technologies to improve productivity. Conversely, areas facing challenges such as salinity or hilly terrain require tailored approaches to overcome these limitations, such as the development of stress-tolerant rice varieties and the implementation of appropriate irrigation systems. Additionally, identifying regions where rice production is declining, or farmers are shifting to alternative crops can guide initiatives aimed at revitalizing rice cultivation through improved irrigation facilities, training programs, and the development of short-duration varieties. The study highlights the areas that require closer attention to overcome challenges and enhance productivity, ultimately contributing to achieving Sustainable Development Goal 2.3 of doubling agricultural productivity and income for smallholder farmers by 2030. In summary, these practical implications can contribute to sustainable agricultural practices, enhance food security, and improve the livelihoods of farmers in Bangladesh.

We express our sincere gratitude for your valuable comments and advice, which have significantly contributed to the improvement of our manuscript. Your thorough review has been instrumental in shaping our revisions, and we trust that our responses and the additional data provided meet your expectations. Once again, thank you for your careful work and thoughtful suggestions.