Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title><jats:p>The management of climate-resilient grassland systems is important for stable livestock fodder production. In the face of climate change, maintaining productivity while minimizing yield variance of grassland systems is increasingly challenging. To achieve climate-resilient and stable productivity of grasslands, a better understanding of the climatic drivers of long-term trends in yield variance and its dependence on agronomic inputs is required. Based on the Park Grass Experiment at Rothamsted (UK), we report for the first time the long-term trends in yield variance of grassland (1965–2018) in plots given different fertilizer and lime applications, with contrasting productivity and plant species diversity. We implemented a statistical model that allowed yield variance to be determined independently of yield level. Environmental abiotic covariates were included in a novel criss-cross regression approach to determine climatic drivers of yield variance and its dependence on agronomic management. Our findings highlight that sufficient liming and moderate fertilization can reduce yield variance while maintaining productivity and limiting loss of plant species diversity. Plots receiving the highest rate of nitrogen fertilizer or farmyard manure had the highest yield but were also more responsive to environmental variability and had less plant species diversity. We identified the days of water stress from March to October and temperature from July to August as the two main climatic drivers, explaining approximately one-third of the observed yield variance. These drivers helped explain consistent unimodal trends in yield variance—with a peak in approximately 1995, after which variance declined. Here, for the first time, we provide a novel statistical framework and a unique long-term dataset for understanding the trends in yield variance of managed grassland. The application of the criss-cross regression approach in other long-term agro-ecological trials could help identify climatic drivers of production risk and to derive agronomic strategies for improving the climate resilience of cropping systems.</jats:p>

<jats:title>Abstract</jats:title><jats:p>The intensification of crop production is widely recognized to negatively affect the agrobiodiversity in smallholder systems. This trend can also be observed in Quinoa production systems, where few varieties are commercialized while maintaining traditional varieties of quinoa remains a key agricultural activity in the high-Andes landscape. In recent decades, the “boom” of quinoa production has given rise to national projects intended to ensure that farmers benefit from their agricultural heritage, including the development of a collective trademark. However, little is known about the opinions of smallholder farmers regarding quinoa varieties cultivation, farming practices, market choices, or the development of a collective trademark as a tool to safeguard a position in the booming international quinoa market. To address these questions, we developed our research in three villages in the Puno region of Peru, quinoa’s center of origin. We applied a novel combination of participatory methods: the <jats:italic>Q</jats:italic> methodology to interpret the perceptions of smallholder quinoa farmers concerning the activities that are important on their farms, and the Four-Square Analysis workshops to explore quinoa biodiversity management. The results of our <jats:italic>Q</jats:italic>-analysis revealed three types of opinions emerging among farmers: (Type 1) <jats:italic>Conservationist</jats:italic>, (Type 2) <jats:italic>Intensification sustainer</jats:italic>, and (Type 3) <jats:italic>Collaboration seeker</jats:italic>. Type 1 assigns importance to maintaining and promoting quinoa biodiversity through collective practices and markets. Type 2 focuses on developing export-oriented production based on certified and improved varieties, combined with efficient ways of storing quinoa. Type 3 appears to value the collective aspects of organizations and cooperation among stakeholders. According to the results of the Four-Square Analysis, most landraces of quinoa are threatened by genetic erosion, as they are cultivated in situ in small plots and on few farms. Our results are an important baseline for further project development for biodiversity conservation in situ and market inclusion engaging local communities.</jats:p>

<jats:title>Abstract </jats:title><jats:p>Sustainable intensification aims to increase production and improve livelihoods of smallholder farmers in sub-Saharan Africa. Many farmers, however, are caught in a vicious cycle of low productivity and lack of incentives to invest in agricultural inputs. Moving towards sustainable intensification therefore requires support such as input subsidies and learning about new options through, for instance, co-learning approaches. Yet such support is not straightforward as agricultural developments often diverge from the envisaged pathways: extensification may occur instead of intensification and specialization instead of diversification. Understanding of farmers’ responses to incentives such as input subsidies and new knowledge is lacking. Our overarching aim was to improve this understanding, in order to better support future pathways for agricultural development in smallholder farming. Over five seasons, we compared the responses of farmers in western Kenya taking part in a novel co-learning program we developed, which included provision of an input voucher, with the responses of farmers who only received a voucher. We also assessed the differences before and during the program. We used diverse indicators that were related to the different agricultural development pathways. Farmer responses were mainly a result of the input voucher. Farmers increased maize yields (intensification) and maize area (specialization) for maize self-sufficiency. Increased farm and maize areas in combination with relatively low N application rates also pointed to extensification coupled with the risk of soil N mining. Diversification by increasing the soybean and groundnut area share was facilitated by the integrated co-learning approach, which thereby supported relatively complex farm management changes. Our results highlight the difficulty of enabling yield and production increases, while also meeting environmental and economic goals. The diversity of farmer responses and constraints beyond the farm level underlined the importance of wider socio-economic developments in addition to support of sustainable intensification at farm level.</jats:p>

<jats:title>Abstract</jats:title><jats:p>European farm households will face increasingly challenging conditions in the coming decades due to climate change, as the frequency and severity of extreme weather events rise. This study assesses the complex interrelations between external framework conditions such as climate change or adjustments in the agricultural price and subsidy schemes with farmers’ decision-making. As social aspects remain understudied drivers for agricultural decisions, we also consider value-based characteristics of farmers as internal factors relevant for decision-making. We integrate individual learning as response to extreme weather events into an agent-based model that simulates farmers’ decision-making. We applied the model to a region in Eastern Austria that already experiences water scarcity and increasing drought risk from climate change and simulated three future scenarios to compare the effects of changes in socio-economic and climatic conditions. In a cross-comparison, we then investigated how farmers can navigate these changes through individual adaptation. The agricultural trajectories project a decline of active farms between −27 and −37% accompanied by a reduction of agricultural area between −20 and −30% until 2053. The results show that regardless of the scenario conditions, adaptation through learning moderates the decline in the number of active farms and farmland compared to scenarios without adaptive learning. However, adaptation increases the workload of farmers. This highlights the need for labor support for farms.</jats:p>

<jats:title>Abstract</jats:title><jats:p>Phenological development is critical for crop adaptation. Phenology models are typically driven by temperature and photoperiod, but chickpea phenology is also modulated by soil water, which is not captured in these models. This study is aimed at evaluating the hypotheses that accounting for soil water improves (i) the prediction of flowering, pod-set, and flowering-to-pod-set interval in chickpea and (ii) the computation of yield-reducing frost and heat events after flowering. To test these hypotheses, we compared three variants of the Agricultural Production System Simulator (APSIM): (i) APSIMc, which models development with no temperature threshold for pod-set; (ii) APSIMx, which sets a threshold of 15 °C for pod-set; and (iii) APSIMw, derived from APSIMc with an algorithm to moderate the developmental rate as a function of soil water, in addition to temperature and photoperiod common to all three models. Comparison of modelled and actual flowering and pod-set of a common cheque cultivar PBA Boundary<jats:sup>A</jats:sup> in 54 diverse environments showed that accuracy and precision were superior for APSIMw. Because of improved prediction of flowering and pod-set timing, APSIMw improved the computation of the frequency of post-flowering frosts compared to APSIMc and APSIMx. The number of heat events was similar for all three models. We conclude that accounting for water effects on plant development can allow better matching between phenology and environment.</jats:p>