Southeast (SE) Asian countries are the world’s largest rice producers and consumers. For decades, socio-economic and environmental changes have been altering agricultural practices, including rice cultivation (Felkner et al., 2009; Satterthwaite et al., 2010; Reynolds et al., 2018; Flor et al., 2018). These alterations require closer scrutiny to understand and ultimately maximize agricultural output both locally and globally, and to more effectively utilize land and water resources, as population growth and urbanization increase competition for their use in agriculture. In order to meet global rice demands for a rapid growing world population, rice yields need to increase by approximately 42% by 2050 (Ray et al., 2013). Rice farming is facing a dual challenge of delivering sufficient and nutritious food to meet the projected demands of population growth and markets, and overcoming issues such as climate change, soil fertility depletion and water scarcity through sustainable agricultural intensification. Thus, environmental-friendly strategies are needed to reduce the environmental burden associated with intensive rice cultivation without jeopardizing rice production, commoditization and global food security. In this context, rice diseases and pests are major biotic constraints reducing rice yields in Asia by about 37% (Gianessi, 2014). Among these pathogens,
Rice agricultural practices have been significantly modified in SE Asia during the last decades, notably due to the Green Revolution (Pingali, 2012; Reynolds et al., 2018) and the increasing scarcity of water and labor (Thrall et al., 2010). For hundreds of years, farmers traditionally transplanted rice seedlings. Nowadays, with the support of agricultural mechanization and to offset the scarcity of labor, seed broadcasting on non-flooded rice fields is the most common practice. Seed broadcasting means that rice seeds are being directly sown in the field rather than by transplanting seedlings from a nursery. Soil is tilled before seeding, and the rice seeds are sown in lines or broadcasted.
Conservation agriculture (CA) is seen as a promising alternative to soil and crop management practices to enhance soil functions and to improve farm sustainability (Hobbs et al., 2008; Verhulst et al., 2010; Palm et al., 2014). CA is an alternative to traditional farming methods in which soil disturbance is minimized and cropping systems diversified, by crop rotations and cover crops mixtures for instance, to improve soil fertility and conserve water while reducing operational costs including labor inputs. CA has gained popularity throughout the world because it alleviates the negative impacts and high production costs of the commonly used continuous plowing and crop monoculture. Although CA covered an estimated area of 180 million ha worldwide in 2016 (Kassam et al., 2019), few irrigated rice agroecosystems combine permanent no-till (NT) with cover crop management, a typical CA system. In the context of our study, we consider the terms CA and direct-seeding mulch-based cropping (DMC) systems as equivalent, the latter term having the advantage to be more explicit and refers to diversified cropping system managed under NT with a high biomass input. Biological soil processes are enhanced and maintained through the use of multifunctional cover crops and a higher degree of crop diversification (Séguy et al., 2006; Palm et al., 2014; Rabenarivo et al., 2014). The cover crops grown in between crop cycles provide a continuous flow of fresh organic matter increasing soil organic C and N concentrations (Hok et al., 2015; Sá et al., 2015; Patra et al., 2019) while they also enhance soil biodiversity (Lienhard et al., 2013a, b; Choudhary et al., 2018; Jiang et al., 2018). The effects of DMC on the abundance and diversity of soil microbial communities’ benefit, in turn the plant’s development, and thus yield (Hungria et al., 2009; Palm et al., 2014; Lienhard et al., 2014). Besides the micro-organisms, the relationships between DMC and the soil nematofauna have also been studied (Villenave et al., 2009; Ito, 2015; Ito et al., 2015). For instance, the examination of the long-term effects of contrasted DMC systems in Madagascar has shown that the soil food webs become more complex under DMC soils when compared with conventional plough-based tillage (CT), with an increasing abundance and diversity of soil microbial and nematode assemblages (Villenave et al., 2009).
The impact of DMC and rice RPN has not been studied so far. Some key practices in DMC could negatively affect the occurrence of RPN on rice. For instance, direct-seeded rice enhances the invasion and establishment of weeds (Farooq et al., 2011) such as
The experimental field was located in Stung Chinith, Santuk district, Kampong Thom province, Cambodia (12°32′55″N and 105°08′47″E). Since April 2011, a study comparing DMC vs CT cropping systems has been implemented. The experiment was managed by agronomists of the General Directorate of Agriculture of Cambodian, the Department of Agricultural Land Resources Management (DALRM), the Conservation Agriculture Service Center (DALRM/CASC), and the AIDA research unit of the French Agricultural Research for Development (CIRAD). The field soil is a sandy podzolic soil (≥70% sand at 0- to 40-cm deep) belonging to the “Prey Khmer group” in the Cambodian agronomic soil classification system (White et al., 1997), equivalent to red-yellow podzols (Crocker, 1962), or fluvisols/arenosols according to the FAO soil taxonomy (Anonymous, 2006).
Annual rainfall in the Kampong Thom province was 1,489 mm in 2014 and 1,154 mm in 2015 (Ministry of Water Resources and Meteorology, Stung Chinit Station). The experiment was conducted during the rainy seasons (from June to November) of 2014 and 2015. During the dry season (from late November to the end of May), the experimental field remained dry before being progressively flooded during the rainy season with most water from the end of August until October due to heavy rains.
The experimental field was about 2.6 ha large, where nine plots (250 m2 each) were randomly distributed and managed equitably either in the DMC (3 plots) or in the CT systems (2 × 3 plots) (Supplemental Material 1). In the DMC system, seeds of two leguminous cover crops (
The CT system consisted of a practice commonly used by the local rice farmers. After the harvest of the rice crop, rice stubbles were left in the field. One month before sowing rice (in May), the soil was ploughed to control weeds and incorporate the remaining rice stubbles into the soil. Then, soil plowing and harrowing were practiced again on the day of sowing (June). No cover crops were grown between the two rice crop cycles under CT management. Two rice planting/sowing methods were used: transplanting (three plots) and seed broadcasting (three plots). Before transplanting, seedlings were grown in a nursery on an adjacent field during two to three weeks. For seed-broadcasting plots, seeds were manually broadcasted directly onto prepared soil (plowing and harrowing). Then, a harrow was used to incorporate the seeds into the soil. Mineral fertilizer was applied (100 kg ha−1 of diammonium phosphate; 18% N, 46% P2O5 and 0% K2O) at sowing time under the DMC system, and 15 days after rice transplanting and at seed broadcasting under the CT system.
During the rice cycle, weed control was done three weeks after sowing under both treatments and Cyhalofop-butyl (2-4-4-cyano-2-fluorophenoxy phenoxy propanoic acid, butyl ester (R)) at 0.285 kg ha−1 and 2.4 D (2,4-dichlorophenoxyacetic acid) at 0.504 kg ha-1 were applied under unflooded conditions.
Roots were sampled four times per year in both 2014 and 2015, three to four years after establishment of the experiment. The 1st sampling was done when the rice plants were at the tillering stage (at the end of June in 2014 and mid-July in 2015), the 2nd sampling at the maximum tillering stage (late August), the 3rd sampling at the late reproductive phase or early milky stage (in October) and the 4th sampling at the ripening phase shortly before harvesting (November).
At each sampling date, 20 individual rice plants were randomly uprooted per plot. At the 1st and 2nd sampling dates, whole root systems were collected, whereas at the 3rd and 4th sampling dates, a composite sample of 100 g was collected for each plant due to the large size of the root systems. Composite samples were made after cutting the roots into small pieces and mixing the pieces thoroughly before taking a representative sub-sample of 100 g. The roots were carefully washed in running tap water and placed in labeled plastic bags before being transported in cool iceboxes to the laboratory.
Eggs were recovered from the root systems of 20, individually sampled, plants using a hypochlorite extraction method and a blender (McClure et al., 1973) with minor modifications (Bellafiore et al., 2015). After being filtered through 500 µm and 250 µm sieves, eggs and juveniles were recovered on a third 25 µm sieve. The nematode suspension obtained after extraction was homogenized in 40 ml tap water and 1 ml of this suspension placed in a counting cell chamber to count the number of eggs, second-stage juveniles (J2), and males using a stereomicroscope. Based on the total number of eggs, J2 and males, RPN population density per gram of fresh roots (RPN/g roots) was calculated.
From each plot, two infected rice plants (the infection determined by the presence of RPN after counting) were randomly selected. Using a stereomicroscope, 20 J2 were randomly and individually collected from each plant and put into 1 ml tubes with 0.001% of Tween-20 solution and stored at −80oC until DNA extraction (Bellafiore et al., 2015). Ten microliters of pre-mix 2X lysis buffer (20 mM Tris-HCl at pH 8.0, 100 mM KCl, 3 mM MgCl2, 2 mM DTT, 0.9% Tween 20) were added to each 1 ml tube containing a single J2, 10 µl of dH2O and 0.001% Tween 20. Zero-point five microliter of Proteinase K (100 µg/µl in stock solution, Thermo Scientific (Waltham, MA) and 0.025 µl of DTT (1 M) were added, and the tubes were incubated at 55oC for 3 hr before being transferred to 98oC for 15 min. One microliter of RNAse type A (10 mg/ml; Thermo Fisher Scientific, Waltham, MA) was then added to each tube and incubated at 37oC for 1 hr. The extracted DNA was immediately used for polymerase chain reaction (PCR) and then conserved at −20oC for future study.
PCR was performed on each J2 DNA extract using
Using standard nucleotide BLAST, the sequences were compared to a collection of ITS sequences from
In conjunction with the molecular identification of the RPN species, the population dynamics of the RPN were studied using the RPN population densities at four rice development stages: seedling, tillering, milky and harvest. Eggs extracted from infected roots were hatched under controlled conditions (Bellafiore et al., 2015). For the tillering stage in 2014 and the harvest stage in both 2014 and 2015, the hatching was compromised and we were not able to collect J2. The average number of J2 per 100 g of fresh roots was calculated for each plant stage. Based on the percentage of each RPN species from the molecular analyses and taking into account the RPN population density per gram of fresh roots previously formulated, the population density of each RPN species was calculated.
Each plot was subdivided into four sub-plots. Five soil samples were collected from each sub-plot at 0 to 5 cm depth in early December 2014 and these were mixed to create a composite sample. The samples were air-dried at room-temperature, passed through a 2-mm sieve and homogenized. Soil organic C and total N concentrations were determined with the dry combustion method (Nelson and Sommers, 1996) using a Sumigraph NC-80 Analyzer (Sumitomo Chemical Co, LTD, Osaka, Japan). Total organic carbon (TOC) and total nitrogen (TN) were estimated and computed on an equivalent soil mass-depth basis according to Ellert and Bettany (1995). The labile C (LC) fraction was determined using the potassium permanganate oxidizable C (POXC) procedure modified by Culman et al. (2012) at 0.2 M KMnO4. Briefly, 2.5 g of air-dried soil was transferred into a 50 ml centrifuge tube, to which 18 ml of deionized water and 2 ml of 0.2 M KMnO4 stock solution were added. The tube was shaken at 120 rpm with a horizontal shaker for 2 min, then left to settle for 10 min. After settling, 0.5 ml of the supernatant was transferred into another 50 ml centrifuge tube containing 49.5 ml of deionized water. The absorbance of the samples was read by a Spectrophotometer UV-1200 (Shimadzu Inc., Kyoto, Japan) at 550 nm.
Soil pH was determined using a 1:2.5 ratio of soil/distilled water and measured with a pH meter D-51 (Horiba Ltd., Kyoto, Japan). Phosphorus content in each sample was determined using the Olsen method to extract the available soil phosphorus (Olsen et al., 1954). The extracted soil phosphorus was measured by the Murphy and Riley (1962) method. Exchangeable base cations Ca2+ and Mg2+ were extracted with 1 mol/liter KCl and K+ with Mehlich-1 solution. Exchangeable Ca2+ and Mg2+ were determined by titration using 0.025 mol/liter EDTA. K+ was determined by flame photometry.
In addition, for each sub-plot the central point was sampled using a core sampler topsoil (0–5 cm depth) to measure soil bulk density (
All statistical analyses were done using the software R (R Core Team, 2015; version 3.3.3). The RPN population density per g of fresh roots did not follow a normal distribution due to the high number of non-infected plants (zero value), and the data was therefore transformed into a binary distribution (0 for non-infected and 1 for infected plants) before performing a generalized linear model (GLM) following a binomial distribution. We tested the effect of the sampling by year (2014 vs 2015), cropping system (DMC vs CT), plot and rice developmental stage on RPN population densities by considering both non-infected and infected plants. The same tests were also performed with only infected plants. Then, the relationship between yield and number of RPN per gram of fresh roots was evaluated using Kendall’s test.
Plot parameters were further investigated using a principal component analysis (PCA) to summarize the information conveyed by all the variables to a reduced number of dimensions. The analysis was carried out with the library ade4 (Dray and Dufour, 2007). A multivariate analysis was conducted to further analyze the contribution of the different plot parameters to the
The PCA showed an impact of the DMC and CT cropping systems on soil nutrient concentrations, TOC, TN, pH, labile soil organic C and bulk density (Fig. 1). The two axes explained 63.6% of the variance (first: 42.7%; second: 20.9%). The significance of this grouping (DMC vs CT) was significant (Monte–Carlo test,
Principal component analysis (PCA) between plots belonging to conventional plough-based tillage (CT) and direct-seeding mulch-based cropping (DMC) systems and other variables in Stung Chinith field experiment. The first two dimension captures 43.3 and 21.9% of the total variance, respectively. Each dot is a plot; plots are regrouped by an ellipse considering the cultivation mode (DMC vs CT, see text for details). The variables were measured from samples at 10 cm soil layer/depth during each nematode survey: Cond is soil conductivity, Redox is the redaction-oxidation reaction, and ppm is the amount of salt in parts per million. Other variables were calculated from 0 to 5 cm soil layer in the laboratory: pH, BD (soil bulk density (
The DMC cropping system resulted in higher TOC concentration, LC, TN, Mg and K concentrations (Table 1). In contrast, the pH was lower in the DMC cropping system compared with the CT cropping system.
Comparison of soil minerals in DMC
Systems | Plots ID | TOC | TN | CN | LC | pH | PO | BD | Ca2+ | Mg2+ | K+ |
---|---|---|---|---|---|---|---|---|---|---|---|
DMC | 42 L | 8.78 | 0.91 | 9.62 | 370.33 | 4.57 | 27.64 | 1.72 | 0.89 | 0.22 | 0.13 |
DMC | 52 L | 8.38 | 0.91 | 9.16 | 254.94 | 4.50 | 15.21 | 1.78 | 1.20 | 0.22 | 0.14 |
DMC | 62 L | 13.14 | 1.43 | 9.19 | 312.46 | 4.19 | 17.79 | 1.64 | 0.92 | 0.20 | 0.14 |
CT | 41 LB | 4.62 | 0.46 | 9.94 | 139.35 | 4.91 | 11.53 | 1.68 | 0.53 | 0.14 | 0.10 |
CT | 41 LT | 4.43 | 0.45 | 9.81 | 139.91 | 4.96 | 9.53 | 1.62 | 0.51 | 0.14 | 0.10 |
CT | 51 LB | 5.53 | 0.59 | 9.32 | 110.56 | 4.97 | 12.86 | 1.62 | 0.90 | 0.15 | 0.10 |
CT | 51 LT | 4.69 | 0.52 | 9.04 | 107.12 | 5.05 | 9.51 | 1.62 | 0.70 | 0.18 | 0.10 |
CT | 61 LB | 8.30 | 0.75 | 10.99 | 174.19 | 4.45 | 13.39 | 1.49 | 0.56 | 0.15 | 0.10 |
CT | 61 LT | 8.17 | 0.78 | 10.54 | 169.73 | 4.57 | 15.41 | 1.72 | 0.51 | 0.14 | 0.07 |
DMC | Plot mean | 10.10 | 1.09 | 9.33 | 312.58 | 4.42 | 20.21 | 1.72 | 1.00 | 0.21 | 0.14 |
CT | Plot mean | 5.71 | 0.57 | 9.92 | 139.57 | 4.84 | 11.86 | 1.63 | 0.60 | 0.15 | 0.09 |
DMC, direct-seeded mulch-based cropping system; CT, conventional tillage; TOC, total organic carbon; TN, total nitrogen; CN, ratio between carbon and nitrogen; LC, labile carbon; pH, potential of hydrogen; PO, diphosphorus dioxide (P2O2); BD, soil bulk density (ρb); Ca2+, calcium irons; Mg2+, available magnesium (Mg2+); and K+, available potassium (K+). Soil properties at 0–5-cm soil depth.
For all cropping systems, the yield was significantly higher in 2014 (2,572 kg ha-1) compared to 2015 (2,299 kg ha-1,
Comparison of yield in direct mulch-based cropping systems (DMC) vs conventional plough-based tillage (CT) plots. Pairwise comparison of rice production in 2014 and 2015 in the DMC system vs two CT systems using the Kruskal–Wallis non-parametric test. DMC: direct-seeding mulch-based cropping system; CT-B: conventional tillage-seed broadcasting method; CT-T: conventional tillage transplanting method. Values are in kg ha−1 and in the form: mean ± standard error.
GLM analysis showed the effects of cropping system, rice developmental stage, year of sampling, and plot on RPN. Interactions were revealed between all variables measured (Table 2). When only the nematode-infected plants were considered, GLM analysis showed no interaction between the variables measured. All variables had an effect on RPN, except the sampling year (Table 2). The average number of RPN per gram of roots was significantly higher in 2014 than in 2015 for all cropping systems (
Generalized Linear Model (GLM) results with the
Variables | Log-likelihood ratio test ( |
df | Pr (> |
---|---|---|---|
Cropping system | 32.039 | 1 |
|
Plot | 85.259 | 13 |
|
Year | 12.900 | 1 |
|
Rice developmental stage | 72.845 | 2 |
|
Year × Plot | 132.994 | 13 |
|
Year × Rice developmental stage | 11.432 | 2 |
|
Plot × Rice developmental stage | 227.402 | 26 |
|
Plot × Cropping system | 68.299 | 26 |
|
Year × Cropping system | 3.514 | 1 |
|
Rice Developmental stage × Cropping system | 2.366 | 2 |
|
Cropping system | 52.274 | 1 |
|
Plot | 99.796 | 13 |
|
Year | 3.024 | 1 |
|
Rice developmental stage | 32.079 | 2 |
|
GLM results using both non-infested and infested plants (coded 0 for non-infected and 1 for infected plants) were highlight in gray. The model tested the effects of the sampling by year (2014 and 2015), cropping system (DMC and CT), plot and rice developmental stage on RPN population densities and their interactions. The results obtained from the analysis of Deviance Table (Type II tests), the significance was considered at
Comparison of the average number of root-parasitic nematodes between DMC vs conventional tillage plots in both year sequences. RPN stands for root-parasitic nematodes extracted from rice roots. The number of nematodes indicated is the average of RPN per gram of root. Different letter on histogram (a, b) indicates significant difference using Kruskal–Wallis non-parametric test. Values are the mean ± standard error. DMC: direct-seeding mulch-based cropping system; CT-B: conventional tillage-seed broadcasting method; CT-T: conventional tillage transplanting method.
Comparison of the percentage of
Comparison of the dynamic of
In our study, the abundance of RPN was higher under the DMC system compared to the CT system. Several causes could be emphasized to explain this observation. First, the two cover crops used in the DMC system,
Second, prevalence of some weeds species was observed both under DMC and CT and those could have a positive impact on RPN abundance. Specifically,
Our third hypothesis is that the DMC system may have changed the microbial soil community in such a way that this benefitted the RPN. This cropping system may have resulted in more complex soil food webs compared to the CT system (Villenave et al., 2009; Ito et al., 2015). Higher root development in DMC systems (F. Tivet, pers. comm.) could also increase RPN population densities, as more roots offer more infection sites. Higher rice yields in the DMC system suggest that the increase in soil nutrients and organic matter observed may have allowed the rice plants to compensate for damage by the increased RPN numbers, also observed by Asif et al. (2015).
The molecular identification revealed that two RPN species were associated with rice roots.
We can conclude that the DMC system applied in our study improved, at least in the short term, the soil fertility and soil quality in such a way that also promoted the occurrence of RPN. In this short-term transition, rice yields were significantly higher (
In order to enhance the efficiency of such DMC system, attention should be paid to weed management during the dry season that can grow jointly with cover crops, since the DMC system has been shown to be more favorable to the occurrence of some weed species (i.e.