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Gene Regulatory Networks are models of genes and gene interactions at the expression level. The advent of microarray technology has challenged computer scientists to develop better algorithms for modeling the underlying regulatory relationship in between the genes. Fuzzy system has an ability to search microarray datasets for activator/repressor regulatory relationship. In this paper, we present a fuzzy reasoning model based on the Fuzzy Petri Net. The model considers the regulatory triplets by means of predicting changes in expression level of the target based on input expression level. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. Through formalization of fuzzy reasoning, we propose an approach to construct a rule-based reasoning system. The experimental results show the proposed approach is feasible and acceptable to predict changes in expression level of the target gene.

Around 80 % of global soybean supply is produced in southeast South America (SESA), central Brazil (CB) and the United States (US) alone. This concentration of production in few regions makes global soybean supply sensitive to spatially compounding harvest failures. Weather variability is a key driver of soybean variability, with soybeans being especially vulnerable to hot and dry conditions during the reproductive growth stage in summer. El Niño–Southern Oscillation (ENSO) teleconnections can influence summer weather conditions across the Americas, presenting potential risks for spatially compounding harvest failures. Here, we develop causal structural models to quantify the influence of ENSO on soybean yields via mediating variables like local weather conditions and extratropical sea surface temperatures (SSTs). We show that soybean yields are predominately driven by soil moisture conditions in summer, explaining ∼50 %, 18 % and 40 % of yield variability in SESA, CB and the US respectively. Summer soil moisture is strongly driven by spring soil moisture, as well as by remote extratropical SST patterns in both hemispheres. Both of these soil moisture drivers are again influenced by ENSO. Our causal models show that persistent negative ENSO anomalies of -1.5 standard deviation (SD) lead to a -0.4 SD soybean reduction in the US and SESA. When spring soil moisture and extratropical SST precursors are pronouncedly negative (-1.5 SD), then estimated soybean losses increase to -0.9 SD for the US and SESA. Thus, by influencing extratropical SSTs and spring soil moisture, persistent La Niñas can trigger substantial soybean losses in both the US and SESA, with only minor potential gains in CB. Our findings highlight the physical pathways by which ENSO conditions can drive spatially compounding events. Such information may increase preparedness against climate-related global soybean supply shocks. [ABSTRACT FROM AUTHOR]

Around 80% of global soybean supply is produced in southeast South America (SESA), central Brazil (CB) and the United States (US) alone. This concentration of production in few regions makes global soybean supply sensitive to spatially compounding harvest failures. Weather variability is a key driver of soybean yield variability, with soybean especially vulnerable to hot and dry conditions during the reproductive growth stage in summer. El Niño Southern Oscillation (ENSO) teleconnections can influence summer weather conditions across the Americas presenting potential risks for spatially compounding harvest failures. Here, we develop causal structural models to quantify the influence of ENSO on crop yields via mediating variables like local weather conditions and extratropical sea-surface temperatures (SST). We show that soybean yields are predominately driven by soil moisture conditions in summer explaining ~50%, 18% and 40% of yield variability in SESA, CB and US respectively. Summer soil moisture is strongly driven by spring soil moisture as well as remote extra tropical SST patterns in both hemispheres. Both of these soil moisture drivers are again influenced by ENSO. Our causal models show that persistent negative ENSO anomalies of -1.5 standard deviation (SD) lead to a -0.4 SD soybean reductions in the US and SESA. When spring soil moisture and extratropical SST precursors are pronouncedly negative (-1.5 SD), then estimated soybean losses increase to -0.9 SD for US and SESA. Thus, by influencing extratropical SSTs and spring soil moisture, persistent La Niña's can trigger substantial soybean losses in both the US and SESA, with only minor potential gains in CB. Our findings highlight the physical pathways by which ENSO conditions can drive spatially compounding events. Such information may increase preparedness against climate related global soybean supply shocks. [ABSTRACT FROM AUTHOR]

The US agriculture system supplies more than one-third of globally-traded soybean and with 90 % of US soybean produced under rainfed agriculture, soybean trade is particularly sensitive to weather and climate variability. Average growing season climate conditions can explain about one-third of US soybean yield variability. Additionally, crops can be sensitive to specific short-term weather extremes, occurring in isolation or compounding at key moments throughout crop development. Here, we identify the dominant within-season climate drivers that can explain soybean yield variability in the US, and explore synergistic effects between drivers that can lead to severe impacts. The study combines weather data from reanalysis, satellite-based evapotranspiration and root-zone soil moisture with sub-national crop yields using statistical methods that account for interaction effects. Our model can explain on average about half of the year-to-year yield variability (60 % on all years and 40 % on out-of-sample predictions). The largest negative influence on soybean yields is driven by high temperature and low soil moisture during the summer crop reproductive period. Moreover, due to synergistic effects, heat is considerably more damaging to soybean crops during dry conditions, and less so during wet conditions. Compound and interacting hot and dry August conditions (defined by the 95th and 5th percentiles of temperature and soil moisture, respectively) reduce yields by 1.25 standard deviation. This sensitivity is, respectively, 6 and 3 times larger than the sensitivity to hot or dry conditions alone. Other important drivers of negative yield responses are lower evapotranspiration early in the season and lower minimum temperature late in the season, both likely reflecting an increased risk of frost. The sensitivity to the identified drivers varies across the spatial domain with higher latitudes, and thus colder regions, being less sensitive to hot-dry August months. Historic trends in identified drivers indicates that US soybean has generally benefited from recent shifts in weather. Overall warming conditions have reduced the risk of frost in early and late-season and potentially allowed for earlier sowing dates. More importantly, summers have been getting cooler and wetter over eastern US. Still, despite these positive changes, we show that the frequency of compound hot-dry August month has remained unchanged over 1946-2016. Moreover, in the longer term, climate models project substantially warmer summers for the continental US which likely creates risks for soybean production. [ABSTRACT FROM AUTHOR]