DOI: 10.7910/dvn/9spt9n
title: Raw data for the Crop Health (Project 4) of ICON: Introducing non-flooded crops in rice-dominated landscapes: Impact on CarbOn, Nitrogen and water budgets

description: &lt;h1&gt;ICON&lt;/h1&gt;
&lt;h2&gt;Introducing non-flooded crops in rice-dominated landscapes: Impact on CarbOn, Nitrogen and water budgets&lt;/h2&gt;

Above ground foliar, stem and panicle injury observation and root nematode observation data collected for the ICON project.

The relevant excerpt from the proposal, also included in .doc format, follows.

&lt;h2&gt;Project 4 (Disease epidemics in rice-based systems affected by changes in water management;&lt;/h2&gt;
&lt;p&gt;IRRI, Savary – no funding requested) will monitor disease progress - in particular
sheath blight - in relation to the physical environment of the soil and of the canopy (microclimate), in both the rice and the maize crops (Project 3). The shift from flooded to non-flooded cropping systems directly affects the physical environment and occurrence of natural enemies of the soil-borne pathogens and this, indirectly, affects the physical environment of the canopy, where non soil-borne pathogens may develop (H.1). Rhizoctonia species are soil-borne fungi causing sheath blight in rice, a major disease in rice production, and there is indication that some of the R. solani sub-species can infect maize as well. In this project emphasis will be given to identify the responses of Rhizoctonia as well as other pathogens to (i) crop rotation and (ii) water management regime in order to develop functional relationships between cropping system and crop management and disease progress (H.3).&lt;/p&gt;

&lt;p&gt;Change in water management is a prerequisite for adaptation of rice-based agroecosystems in a context of climate change. While water-saving technologies, including supply of agricultural water (the largest user of water in tropical Asia), but also tillage and crop establishment is necessary, singignificant, and possibly considerable changes are to be expected with respect to the entire guild of yield-reducing organisms of rice, including pathogens (bacteria, fungi, and viruses), as well as insects (Savary et al., 2005).&lt;/p&gt;

&lt;p&gt;It is worth noting here that this work is congruent with large scale work IRRI has engaged in South Asia, under the umbrella of the Cereal System Initiative for South Asia. This project, among a series of objectives, aims at improving the performances of environmentally constrained – especially, water constrained – intensive cereal systems that must develop to feed South Asia for the decades to come; and this includes a series of heavily instrumented platforms where work similar to what is described below will be conducted.&lt;/p&gt;

&lt;p&gt;Over the years, IRRI has developed a set of methodologies – coupled standardized acquisition methods of injuries (IP) due to diseases and insects, as well as weeds; characterization of production situations (PS), including the physiological status of the crop; statistical multivariate, non-parametric methods to link IPs and PSs; and simulation modeling methods to analyze the effects of individual yield reducing organism of the guild within a community. A recent publication summarizes these methods and their applications (Savary et al, 2006).&lt;/p&gt;

&lt;p&gt;Project 4 of ICON will look at a series of attributes that will be changed with evolving water supply to rice crops:
&lt;ul&gt;
&lt;li&gt;meso-climate (which will be monitored in the overall experiment);&lt;/li&gt;
&lt;li&gt;micro-climate, and I particular, leaf temperature and leaf wetness duration.&lt;/li&gt;
&lt;/ul&gt;
&lt;/p&gt;
&lt;p&gt;We intend to implement the above methodology at successive development stages, including at least:
&lt;ul&gt;
&lt;li&gt;Maximum tillering&lt;/li&gt;
&lt;li&gt;Booting&lt;/li&gt;
&lt;li&gt;Early dough&lt;/li&gt;
&lt;/ul&gt;

where the levels of
&lt;ol&gt;
&lt;li&gt;
leaf diseases (esp., bacterial blight, sheath blight, blast, brown spot, narrow brown spot, bacterial leaf streak)&lt;/li&gt;
&lt;li&gt;tiller diseases (esp. sheath blight, sheath rot, stem rot)&lt;/li&gt;
&lt;li&gt;panicle diseases (esp. grain discoloration, false smut, bakanae)&lt;/li&gt;
&lt;li&gt;whole-plant diseases (esp. rice tungro)&lt;/li&gt;
&lt;li&gt;insect leaf injuries (esp. leaf folders, whorl maggots)&lt;/li&gt;
&lt;li&gt;insect tiller injuries (esp. stem borers – “dead hearts”)&lt;/li&gt;
&lt;li&gt;insect panicle injuries (esp. stem borers – “white heads”)&lt;/li&gt;
&lt;li&gt;sucking insect populations (brown plant hopper, white-back planthopper, and green leaf hopper)&lt;/li&gt;
&lt;/ol&gt;
will be monitored.&lt;/p&gt;

&lt;p&gt;Groups a and e – leaf injury; b and f – tiller injury; c and g – panicle injury; d – systemic injury; and h – sucking injury represent the framework of the “sub-guilds” developed in the above approach to characterize yield-reducing yields. These also are the basis of RICEPEST (Willocquet et al., a generic, mechanistic, crop physiology-based simulation model which enables to explore the individual impact of specific yield-reducer, and their combined effects on systems’ performances. RIRCEPEST has been parameterized, tested, and validated in China, India, and the Philippines during several cropping seasons.&lt;/p&gt;

&lt;p&gt;IRRI’s inputs in Project 4 should thus be seen twofold.

&lt;ol&gt;
&lt;li&gt;Quantification of the effects of varying levels of water management on the entire guild of yield-reducing organisms&lt;br /&gt;

This component will make use of field data acquisition procedure that have been heavily tested and validated in China, India, Vietnam, and the Philippines, as well as in Laos and Cambodia. The main approach to analyze the data will be
&lt;ul&gt;
&lt;li&gt;conventional-parametric statistics: relating macro-, micro-climate, and water with individual levels of injuries;&lt;/li&gt;
&lt;li&gt;non-parametric, including Bayesian, multivariate methods, to address the guild of yield-reducers in a given production situation as a whole.&lt;/li&gt;
&lt;li&gt;The purpose of this component would primarily be descriptive, hypothesis-forwarding, and analytical.&lt;/li&gt;
&lt;/ul&gt;
&lt;/li&gt;

&lt;li&gt;Modeling yield losses due to the guild of yield reducers at different levels of water management&lt;/li&gt;

&lt;p&gt;This component would involve RICEPEST, and therefore simulation modeling based on crop cuts at a series of development stages during the cropping season (a minimum of 4 crop cuts, especially at harvest, is necessary; however, 7-8 crop cuts would be desirable). These crop cuts would enable to (1) re-parameterize the model for different water regimes – keeping the flooded regime as a control (year 1), (2) test the model (year 2), and (3) conduct scenario and sensitivity analyses (year 3).
The purpose of this second component would enable generating an outlook of the consequences of water regime scenarios on yield-reduction due to diseases and insects, as well as to identify which of the yield-reducing components of the guild are the most import in what context. &lt;/p&gt;
&lt;p&gt;This second component would also enable linking Project 4 with other components of ICON in assessing the performances of water-constrained rice-based ecosystems in a holistic manner.&lt;/p&gt;

&lt;p&gt;Savary, S., Castilla, N.P., Elazegui, F.A. & Teng, P.S., 2005. Multiple effects of two drivers of agricultural change, labour shortage and water scarcity, on rice pest profiles in tropical Asia. Field Crops Research 91/2-3: 263-271.&lt;/p&gt;

&lt;p&gt;Savary, S., Teng, P.S., Willocquet, L. & Nutter, F.W., Jr., 2006. Quantification and modeling of crop losses: a review of purposes. Annual Review of Phytopathology 44: 89-112.&lt;/p&gt;

&lt;p&gt;Willocquet, L., Elazegui, F. A., Castilla, N., Fernandez, L., Fischer, K. S., Peng, S., Teng, P. S., Srivastava, R. K., Singh, H. M., Zhu, D., and Savary, S., 2004. Research priorities for rice disease and pest management in tropical Asia: a simulation analysis of yield losses and management efficiencies. Phytopathology 94(7):672-682.&lt;/p&gt;
description: Climate data for modelling data past 2012 can be obtained from the IRRI Climate Group