Crowdsourcing systems accomplish large tasks with scale and speed by breaking work down into independent parts. However, many types of complex creative work, such as fiction writing, have remained out of reach for crowds because work is tightly interdependent: changing one part of a story may trigger changes to the overall plot and vice versa. Taking inspiration from how expert authors write, we propose a technique for achieving interdependent complex goals with crowds. With this technique, the crowd loops between reflection, to select a high-level goal, and revision, to decompose that goal into low-level, actionable tasks. We embody this approach in Mechanical Novel, a system that crowdsources short fiction stories on Amazon Mechanical Turk. In a field experiment, Mechanical Novel resulted in higher-quality stories than an iterative crowdsourcing workflow. Our findings suggest that orienting crowd work around high-level goals may enable workers to coordinate their effort to accomplish complex work.

As demands on agriculture increase, food producers will need to employ management strategies that not only increase yields but reduce environmental impacts. Modeling is a powerful tool for informing decision-making about current and future practices. We present a model to evaluate the effects of crop diversification on the robustness of simulated farms under labor shocks. We use an example inspired by the Florida production system of high-value, labor-intensive fruits. We find that crop diversification to high-value crops is a robust strategy when labor shocks are mild, and that crop diversification becomes less valuable as more simulated farms practice it. Based on our results, we suggest that crop diversification is a useful management strategy under specific conditions, but that policies designed to encourage crop diversification must consider broad effects as well as farm-level benefits.

Crowdsourcing systems accomplish large tasks with scale and speed by breaking work down into independent parts. However, many types of complex creative work, such as fiction writing, have remained out of reach for crowds because work is tightly interdependent: changing one part of a story may trigger changes to the overall plot and vice versa. Taking inspiration from how expert authors write, we propose a technique for achieving interdependent complex goals with crowds. With this technique, the crowd loops between reflection, to select a high-level goal, and revision, to decompose that goal into low-level, actionable tasks. We embody this approach in Mechanical Novel, a system that crowdsources short fiction stories on Amazon Mechanical Turk. In a field experiment, Mechanical Novel resulted in higher-quality stories than an iterative crowdsourcing workflow. Our findings suggest that orienting crowd work around high-level goals may enable workers to coordinate their effort to accomplish complex work.

Remote sensing provides a way of studying agricultural systems when proprietary data and field studies are unavailable. As satellite data become more plentiful and accurate, they have been used to analyze crop yields and, when paired with field data, the effects of management practices. However, satellite data have rarely been used alone to quantify the effects of management practices. This study was conducted to determine whether satellite data can replicate field data findings on the yield benefits of rotation practices. We investigated the yield benefits of crop rotation on corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] using estimates generated from satellite imagery in Indiana, Iowa, and Illinois and compared these to yield benefits from field‐level data between 2007 and 2012. After adjusting for environmental differences between fields with and without rotations, satellite data show yield benefits of 1.0% with a range of −8.9 to 9.7% for corn rotations and 10.8% with a range of 4.6 to 17.6% for soybean rotations, within the range of effects found in the literature and not significantly different from estimates of 4.3% for rainfed corn and 10.3% for rainfed soybean found using the field‐level data. Based on our findings, we conclude that satellite data can be used to evaluate rotation practices without ground data. Core Ideas Satellite data showed yield benefits of 1.0 and 10.8% for rotated corn and soybean from 2007 to 2012. These results are within the range of effects found in the literature. These results are not significantly different from those found with field‐level data. Satellite data are a promising source with which to answer rotation questions.

Many agent-based models (ABMs) try to explain large-scale phenomena by reducing them to behaviors at lower scales. At these scales in social systems are functional groups such as households, religious congregations, coops and local governments. The intra-group dynamics of functional groups often generate inefficient or unexpected behavior that cannot be predicted by modeling groups as basic units. We introduce a framework for modeling intra-group decision-making and its interaction with social norms, using the household as our focus. We select phenomena related to women’s empowerment in agriculture as examples influenced by both intra-household dynamics and gender norms. Our framework proves more capable of replicating these phenomena than two common types of ABMs. We conclude that it is not enough to build multi-scale models; explaining social behaviors entails modeling intra-scale dynamics.