Farm/crop portfolio simulations under variable risk: a case study from Italy

Rosa, Franco; Taverna, Mario; Nassivera, Federico; Iseppi, Luca

doi:10.1186/s40100-019-0127-7

Cited by 13 publications

(7 citation statements)

References 31 publications

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“…This theoretical approach has previously been operationalized through modelling the (Target MOTAD) i.e. the Minimization of Total Absolute Deviation (Rosa et al. , 2019).…”

Section: Methodsmentioning

confidence: 99%

The empirical demand for farm insurance in Ireland: a quantile regression approach

Loughrey

Vidyaratne

2023

AFR

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PurposeThe purpose of this paper is to analyse the association between farm/farmer characteristics and unsubsidized farm insurance premium expenditure in Ireland. The distribution of farm insurance expenditures is wide, and it is important to understand the extent to which individual factors influence demand for different levels of insurance premium.Design/methodology/approachThe quantile regression approach and farm accountancy data from the Teagasc National Farm Survey are used to model the association between farm/farmer characteristics and farm insurance demand in Ireland.FindingsAsset values (livestock, buildings and machinery) are positively associated with total insurance expenditure. Both forestry area and crop area are significantly associated with farm insurance expenditure with a stronger influence on the middle and upper part of the distribution. The interaction between farm income and farmer age is positively associated with insurance expenditure pointing to the importance of farm income protection.Research limitations/implicationsThe research is mainly concerned with insuring against substantive risks, which are capable of threatening the asset base and continuation of the farm business. Future research can integrate questions in relation to farm safety and farmer health with research on the economic survival of the farm business.Practical implicationsFarmers in Ireland adopt unsubsidized farm insurance as a risk management tool. This situation is relevant to other EU member states including Belgium, Denmark, Germany and Sweden. The findings can be used to inform stakeholders and policymakers about the relative impact of different factors on insurance expenditure.Originality/valuePrevious research has typically focused on the linear relationship between farm/farmer characteristics and insurance demand without accounting for variability across the size distribution. This research is based on the quantile regression approach where the association between farm/farmer characteristics and farm insurance expenditure can be assessed at different points of the distribution.

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“…This theoretical approach has previously been operationalized through modelling the (Target MOTAD) i.e. the Minimization of Total Absolute Deviation (Rosa et al. , 2019).…”

Section: Methodsmentioning

confidence: 99%

The empirical demand for farm insurance in Ireland: a quantile regression approach

Loughrey

Vidyaratne

2023

AFR

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“…In addition, common strategies to deal with increasing uncertainty, such as overproduction (Fuss et al, 2015), land-use diversification (Rosa et al, 2019), or the diversion of the available labour to obtain off-farm income (Shannon & Motha, 2015) will influence land-use change processes and associated biodiversity. For instance, off-farm income may reduce crop diversification at the farm level (Ochoa et al, 2019).…”

Section: Directions To Modelling Developmentsmentioning

confidence: 99%

The road to integrate climate change effects on land-use change in regional biodiversity models

Cabral

Mendoza‐Ponce

Silva

et al. 2022

Preprint

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1. Current modelling approaches to predict spatially explicit biodiversity responses to climate change mainly focus on the direct effects of climate on species. Integration of spatiotemporal land-cover scenarios is still limited. Current approaches either regard land cover as constant boundary conditions, or rely on general, typically globally defined land-use scenarios. This is problematic as it disregards the complex synergistic effects of climate and land use on biodiversity at the regional scale, as biophysical, economic, and social issues important for regional land-use decisions are also affected by climate change. To realistically predict climate impacts on biodiversity, it is therefore necessary to consider both, the direct effect of climate change on biodiversity, and its indirect effect on biodiversity via land-use change. 2. In this review and perspective paper, we outline how biodiversity models could be better integrated with regional, climate-driven land-use models. We provide an overview of empirical and modelling approaches to both land-use (LU) and biodiversity (BD) change, focusing on how integration has been attempted. We then analyse how LU and BD model properties, such as scales, inputs, and outputs, can be matched and identify potential integration challenges and opportunities. 3. We found LU integration in BD models has been frequently attempted. By contrast, integrating the role of BD in models of LU decisions is largely lacking. As a result, bi-directional effects remain largely understudied. Only few integrated LU-BD socio-ecological models have assessed climate change effects on LU and no study has yet investigated the relative contribution of direct vs. indirect effects of climate change on BD. 4. There is a large potential for model integration given the overlap on spatial scales, although challenges remain with respect to spatial scale, temporal dynamics, investigation of indirect effects, and bi-directionality, including feeding back to climate models. Efforts to better understand human decisions, eco-evolutionary dynamics, connection between terrestrial and aquatic systems, and format standardization of modelling outputs and empirical data should improve future models. Integrating biodiversity feedbacks into land-use and climate models requires modelling innovations, but should be feasible.

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“…Agriculture is an activity with high level of risk, due to it is subjected to many unpredictable factors, both natural [1] such as climate, and market-based such as prices [2]. For this reason, farmers face the problem to decide about the agricultural process at the same time that something occurs, such as insect attack, long dry period, or price fall; however, when farmers have to react to these conditions, they still do not have an accurate prediction about yield and total production in their fields, therefore decisions do not necessarily will be the best.…”

Section: Short Communicationmentioning

confidence: 99%

Modelling Sunflower Yield Prediction at Field Scale

Gudiño

Rodríguez

Laurentín

2020

BJSTR

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Yield prediction at field scale based on biomass samples is an important issue for agriculture, because farmers may be able to change the development course of the agricultural process to get better final results. The aim of the present study was to stablish the basis for getting a sunflower yield prediction model, for that, this study investigated the partition of dry matter in sunflower plants, and also investigated if the dry mass of the second pair of leaves is a good predictor of the dry mass of the whole plant. Two commercial hybrids were planted for two years in two types of soils, and dry matter for the above ground biomass was determined for the second pair of leaves (top to down) and rest of the plant in the three first sampling each year, and also for capitulum in the other 5 samplings each year. Significant differences (P<0.05) were identified only for Soil Types for the variables Second Pair of Leaves Dry Mass and Capitulum Dry Mass in the sampling dates at 81, 92 and 103 days after planting. Dry mass of capitulum represented 30% of dry matter of the whole plant. Dry mass of Second Pair of Leaves was regressed on Dry Mass of the Whole plant and resulted for both types of soil in determination coefficients (R 2 ) higher than 0.95. By using of both results, an accurate model of yield prediction was obtained: at any age of the plant, dry mass of the second pair of leaves must be determined for predicting the whole biomass of the plant, and knowing that 30% of the dry mass of the whole plant is represented by capitulum, an accurate yield can be predicted. Short CommunicationAgriculture is an activity with high level of risk, due to it is subjected to many unpredictable factors, both natural [1] such as climate, and market-based such as prices [2]. For this reason, farmers face the problem to decide about the agricultural process at the same time that something occurs, such as insect attack, long dry period, or price fall; however, when farmers have to react to these conditions, they still do not have an accurate prediction about yield and total production in their fields, therefore decisions do not necessarily will be the best. Farmers could take the best decisions about the agricultural process, if they have good and reasonable predictions about the harvest. There are two approaches to help farmers to predict yield: without observation on the crop, and with observation on the crop. When prediction is carried out without observation on the crop, historical data of yield and weather are used to estimate future crop yield in agricultural processes that maybe have not started yet; there is no observation about the agricultural process, observation are about climatic conditions occurred in the past. This approach tries to find out climatic patterns to estimate crops yield, by means of several tools such as data mining [3,4], time series, panel, and cross-sectional models [5].On the contrary, yield prediction by means of observation on the crop is based on observations on the plant already established on the ...

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Farm/crop portfolio simulations under variable risk: a case study from Italy

Cited by 13 publications

References 31 publications

The empirical demand for farm insurance in Ireland: a quantile regression approach

The empirical demand for farm insurance in Ireland: a quantile regression approach

The road to integrate climate change effects on land-use change in regional biodiversity models

Modelling Sunflower Yield Prediction at Field Scale

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