The influence of GM crop adoption on the profitability of farms operated by young and beginning farmers

Abstract

Purpose -The purpose of this paper is to assess the impacts of GM crop adoption on the profitability of farms operated by young and/or beginning farmers and ranchers (YBFR). Design/methodology/approach -This research uses weighted quantile regression analysis in conjunction with 2004-2006 Agricultural Resource Management Survey to evaluate the impact of GM crop adoption on financial performance of farms operated by YBFR. The methodology employed in this study corrects for the simultaneity of technology adoption and farm financial performance. Findings -As expected, the impact of GM crop adoption on profitability is positively affected by the scale of operation and leverage. On the other hand, off-farm employment by "beginning" farmers has a negative impact on farm's profitability if they choose to adopt GM crops. Finally, quantile regression results from a farm household study shows that the model performs better at the higher quantile of the distribution.Research limitations/implications -This study helps to determine whether the adoption of GM crops increases the profitability of farms operated by "beginning" farmers. In addition, it explores the impact of other factors (such as farm, operator, demographic, and financial characteristics) on the profitability of farms operated by "beginning" farmers. Practical implications -Computing the profitability of adoption decisions for YBFR will provide significant information to YBFR that they can use in constructing their farm operations strategic business plan and future decisions regarding farming operations. Originality/value -Existing research does not examine the impact of GM crops adoption on farm profitability of YBFR. Furthermore, YBFR operators face significant challenges in making their operations financially viable, owing to lack of access to capital and land.

Exploring the adoption of technologies among beginning farmers in the specialty crops industry

“…Another contribution of this article is the empirical evidence that beginning farmers were more likely to adopt VA technologies when compared to experienced operators (p<0.1). This result is consistent with the beginning farmer literature that showed that beginning farmers tend to be motivated to replace traditional agricultural processes with new technologies (Detre et al. , 2011).…”

“…Another contribution of this article is the empirical evidence that beginning farmers were more likely to adopt VA technologies when compared to experienced operators ðp < 0:1Þ. This result is consistent with the beginning farmer literature that showed that beginning farmers tend to be motivated to replace traditional agricultural processes with new technologies (Detre et al, 2011). One explanation may be that the adoption of VA technologies among beginning farmers may be seen as an optimization strategy to address limited access to land, while experienced farmers may have already reached optimized economies of scale and land access (Katchova and Dinterman, 2018).…”

“…Despite the fact that VA technologiessuch as drying and cutting produce into customerready portionsare considered costly or risky among produce growers (Fujisaka, 1994), beginning farmers in the sample seemed to be taking advantage of these technologies that may help them differentiate their products with VA features and extend the market season. One explanation may be that beginning farmers are more likely to replace traditional agricultural process with technologies that help differentiate their products than experienced farmers (Detre et al, 2011). Another explanation may be that since beginning farmers are more likely to farm part-time than experienced counterparts (Jablonski et al, 2017), these technologies can help beginning farmers overcoming issues such as access to farmland and labor.…”

“…high tunnels, genetically modified crops, etc.) (Detre et al, 2011;Gilmour et al, 2019) to VA technologies (e.g. solar dryers) (Cucagna and Goldsmith, 2018).…”

Labor-use efficiency and New York dairy farm financial performance

“…Both NFI and net returns per unit (NRU) of milk sold were used as measures of financial performance in El-Osta and Johnson (1998), and performed as dependent variables in regression models. Similarly, Detre et al (2011) used NFI and ROA as measures of financial performance and fitted them independently in regression. A derivative work is Mishra et al (2012) which applied the idea of DuPont expansion and used net profit margins, ATR and total assets to net worth ratios in SUR models to identify factors influencing agricultural profitability.…”