Weed Management In Direct-seeded Rice Research Articles

Tillage and weed management in direct-seeded rice (Oryza sativa)wheat (Triticum aestivum) cropping system

A field experiment was conducted at Ranchi, Jharkhand during 201011 and 201112, to find out the effect of tillage and weed-control methods on weed dynamics and productivity of rice ( Or yza sativa L.)wheat [ Tr iticum aestivum (L.) emend. Fiori & Paol.] cropping system. The experiment comprised of 4 tillage methods, viz. zero zero (ZT-ZT), zeroconventional (CT-ZT), conventionalzero (CT-ZT) and conventionalconventional tillage (CT- CT), performed during rainy and winter seasons, respectively laid out under main plots, while 3 weed control meth- ods viz., recommended herbicides in rice (butachlor 1.0 kg/ha pre-emergence + 2,4-D 0.5 kg/ha post-emergence) and wheat (isoproturon 0.75 kg/ha + 2,4-D 0.5 kg/ha post-emergence), weed free and weedy check under sub- plots. Continuous conventional tillage being similar to rotational tillage systems recorded the maximum rice grain (1.96 t/ha) and straw (2.86 t/ha) yield. Three hand-weedings resulted in 2.25% and 92.38% higher grain yield than recommended herbicidal use and weedy check respectively. The CT-CT tillage being similar to ZT-CT recorded 14.16% higher wheat grain yield (2.85 t/ha) than ZT-ZT tillage. Application of isoproturon @ 0.75 kg/ha + 2,4-D @ 0.5 kg/ha as post-emergence gave 22.59% and 58.75% higher grain and straw yield than hand-weeding and weedy check respectively. Continuous conventional tillage in rice and wheat recorded 22.18% higher rice-equiva- lent yield (5.76 t/ha) and maximum net returns ( 28,446/ha). Application of recommended herbicide in rice and wheat gave 11.8% higher rice equivalent yield (6.34 t/ha) than hand-weeding and also provided the maximum net returns ( 37,296/ha) and benefit: cost ratio (1.42).

A plant growth model for integrated weed management in direct-seeded rice. III. Interspecific competition for light

The model DSRICE1 was developed for analyzing integrated weed management strategies for direct-seeded rice. We have shown that DSRICE1 predicts monoculture rice growth well and accounts for water-depth effects on growth. Here, the model is used to simulate competition for light between rice and two weeds, Echinochloa oryzoides (early watergrass) and Ammannia spp. (redstem). Except for minor differences in phenology, weed growth was simulated as described for rice. Direct competition for light depended on the species' vertical distributions of leaf and stem areas (live and dead) and their extinction coefficients. Water also attenuates light, so species' early height growth rates were important because they determined when plants emerged into full light. Structural sensitivity analyses of rice in competition with the two weeds revealed that water-depth effects and leaf area distributions strongly affected competition, and shading by dead leaf and stem dry mass reduced total production. Validation was based on independent data sets for redstem and watergrass competition using several statistical tests and indices. For rice–redstem competition, DSRICE1 simulated rice growth well because redstem competitive effects were small, but predictions of redstem growth were good only when observed heights were matched in simulations. Redstem competitiveness depended on height growth rate, perhaps due to its small seed size. For rice–watergrass competition, the growth of both species was predicted well, except that watergrass growth in plots with early-season drainage was underpredicted. Watergrass parameters were similar to those for rice except for faster height growth and higher photosynthesis rates. In a model application, simulations in which rice seeding was delayed for a time after flooding led to greater yield losses from redstem than from watergrass because delays reduced the advantage of rice over redstem. The usefulness of DSRICE1 for drained fields will be improved by better simulation of plant growth responses to drainage, but rice competition with redstem and watergrass in continuously-flooded fields was simulated well.

A plant growth model for integrated weed management in direct-seeded rice: II. Validation testing of water-depth effects and monoculture growth

Manipulating water depths and timing is a key management practice in rice cropping systems, but rice models have not simulated water-depth effects on plant growth. A new plant growth model, DSRICE1, simulates most cultural and weed management practices except fertility. Water-depth effects on plant light capture are mechanistically simulated as reflection and attenuation of light by water. Light attenuation by water in the model depended on water depth and the light extinction coefficient of water, k H 2O (m −1). DSRICE1 was validation tested for prediction of monoculture growth, and specifically for early-season water-depth effects. Analyses revealed that attenuation by water limited irradiance and reduced seedling growth, and indicated which plant traits contributed to growth during submergence. Reflection did not affect rice growth and may be ignored. In empirical validation tests, DSRICE1 predictions were compared with data from 14 independent data sets. For growth up to 40 days after seeding (DAS) (11 experiments), simulations without water-depth effects failed validation tests by overpredicting rice shoot dry mass (DM). In contrast, DSRICE1 simulations with water-depth effects included were accurate with k H 2O from 1 to 4, k H 2O =3 was best overall, and predictions improved with experiment-specific k H 2O values. DSRICE1 also accurately predicted differences in shoot DM per plant in a 1985 water-depth study, while again simulations without water-depth effects did not. Thus, water-depth effects were required to simulate early rice growth accurately. In whole-season empirical validation tests (14 experiments), DSRICE1 simulated shoot, stem, leaf, live leaf, and live stem DM accurately, especially considering the range of data used and the fact that no calibration was needed. DSRICE1 was also corroborated by the fact that it accounts for many physical factors and plant traits that affect submergence tolerance. The model or the techniques it uses may be useful in analyses of cultivar tolerance to submergence. In some respects, DSRICE1 was subjected to more rigorous validation testing than previous rice models, and potentially explains more interactions between rice and weed growth and management. This approach may broaden and improve simulation analyses of integrated weed management in direct-seeded rice systems.

A plant growth model for integrated weed management in direct-seeded rice: I. Development and sensitivity analyses of monoculture growth

A new model, DSRICE1, was developed to analyze weed management strategies in direct-seeded rice ( Oryza sativa) systems. Previous rice models have not accounted for important cultural and weed management factors in direct-seeded systems, such as growth from seeds and water-depth effects on plant growth. Here we describe the development and sensitivity analysis of DSRICE1 for monoculture rice growth under water-seeded conditions. DSRICE1 is largely process-based and includes all standard weed management practices except fertility. Simulation inputs include latitude, daily solar radiation, daily maximum and minimum temperatures, water depth, and seed rate. Phenology depends on thermal units. Growth begins with seed storage mobilization to seedlings, and photosynthesis starts when the first leaf is extended. Canopy light dynamics depend on leaf and stem area distributions for both live and dead dry mass, and on water depth when submerged. Water-depth effects were explicitly simulated as reflection and attenuation of light. Model analyses revealed that parameter sensitivities varied over time. Some parameters were always important, while the effects of others were limited to particular parts of the season. Judged over the whole season, the most important parameters were for photosynthesis and light capture. Unlike in most monoculture simulations, early height gain rate was important in DSRICE1 because it determined when plants emerged from the water into full light. Analyses of model structure and specifications revealed that predictions were significantly affected by the use of skewed live leaf area distributions and the non-rectangular hyperbola for the light response curve, and the inclusion of water-depth and dead canopy dry mass effects on canopy light dynamics. The cropping system and management processes simulated in DSRICE1 had important effects on model predictions of rice growth. Explicit consideration of these factors distinguishes DSRICE1 from other rice growth models, and may lead to better simulation analyses of system interactions with plant growth and weed management strategies.