Socio-ecological factors determine crop performance in agricultural systems

Libère Nkurunziza,Ingrid Öborn,Henrik G Smith,Christine A Watson,Jan Bengtsson,Göran Bergkvist

doi:10.1038/s41598-020-60927-1

Libère Nkurunziza, Ingrid Öborn + Show 4 more

Open Access

https://doi.org/10.1038/s41598-020-60927-1

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Abstract

Agricultural production systems are affected by complex interactions between social and ecological factors, which are often hard to integrate in a common analytical framework. We evaluated differences in crop production among farms by integrating components of several related research disciplines in a single socio-ecological analysis. Specifically, we evaluated spring barley (Hordeum vulgare, L.) performance on 34 farms (organic and conventional) in two agro-ecological zones to unravel the importance of ecological, crop and management factors in the performance of a standard crop. We used Projections to Latent Structures (PLS), a simple but robust analytical tool widely utilized in research disciplines dealing with complex systems (e.g. social sciences and chemometrics), but infrequently in agricultural sciences. We show that barley performance on organic farms was affected by previous management, landscape structure, and soil quality, in contrast to conventional farms where external inputs were the main factors affecting biomass and grain yield. This indicates that more complex management strategies are required in organic than in conventional farming systems. We conclude that the PLS method combining socio-ecological and biophysical factors provides improved understanding of the various interacting factors determining crop performance and can help identify where improvements in the agricultural system are most likely to be effective.

Highlights

Agricultural production systems are affected by complex interactions between social and ecological factors, which are often hard to integrate in a common analytical framework
We found that a combination of seven management practices, three soil parameters and landscape heterogeneity were the major sources of variation explaining barley performance on organic farms (OF), in contrast to conventional farms (CF) where external inputs in the year of study were most important for crop performance (Figs. 2 and 3)
Factors from all classes of variables (Table S1) – landscape, farm management, field management in the present and previous years, and soil factors– were important to explain variation among organic farms. When both farm types were analysed together, historical management practices and their indirect impacts were mostly observed in OF, as shown by the variables such as percentage weed cover, soil mineral nitrogen (SMN1) and total carbon (Tot-C%) that were higher in older farms under organic farming (Fig. 2)

Summary

Introduction

Agricultural production systems are affected by complex interactions between social and ecological factors, which are often hard to integrate in a common analytical framework. While it is well known that previous cropping strategies and land management history can influence yield via “rotation” and “memory” effects[5,6], it is difficult to quantify the impact of previous management on crop yield due to the complex interactions between management decisions and the biophysical environment Unravelling these relationships requires a methodology that can examine relationships between different types of socio-ecological and biophysical variables. In addition to the high costs associated with experiments where the approach is to keep as many variables as possible under control, the large difference between yields obtained in experimental studies and on-farm yields demonstrates the discrepancies between expected and real farm outcomes, which is the case for less intensive farming systems[12,13] This calls for alternative assessment methods that qualitatively and quantitatively integrate disparate causes of variation in crop performance, such as the temporal and spatial boundaries used in Uppsala County agro-ecological zone. These boundaries are dependent on farmer decisions, which are often guided by socio-economic conditions including finances, technologies and experience

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