Dayton M. Lambert

Conservation agriculture systems for Malawian smallholder farmers: long term effects on crop productivity, profitability and soil quality

Conservation agriculture (CA) systems are based upon minimal soil disturbance; crop residue retention and crop rotation and/or intercrop association are increasingly seen to recycle nutrients, increase yield and reduce production costs. This study examines the effects of CA practices on crop productivity, profitability and soil quality under the conditions encountered by smallholder farmers in two farming communities from 2005 to 2011 in Malawi, as part of the contribution to remedy a lack of supporting agronomic research for these relatively new systems. The drier agroenvironment of Lemu of Bazale Extension Planning Area (EPA) is characterized by sandy clay loam soils and lower rainfall. Here, CA showed positive benefits on maize yield after the first season of experimentation, with highest increases of 2.7 Mg ha −1 and 2.3 Mg ha −1 more yield in CA monocrop maize and CA maize–legume intercrop, respectively, than the conventional tillage in the driest season of 2009/10. In the high rainfall environment of Zidyana EPA (characterized by sandy loam soils), substantial maize yield benefits resulted in the fifth season of experimentation. Farmers spent at most 50 days ha −1 (US

An Application of Spatial Poisson Models to Manufacturing Investment Location Analysis

140) producing maize under CA systems compared with 62 days ha −1 (US

Ethanol Plant Location Determinants and County Comparative Advantage

176) spent under conventional tillage practices. In Lemu, both CA systems resulted in gross margins three times higher than that of the conventional control plot, while in Zidyana, CA monocrop maize and CA maize–legume intercrop resulted in 33 and 23% higher gross margins, respectively, than conventional tillage. In Zidyana, the earthworm population was highest (48 earthworms m −2 in the first 30 cm) in CA monocrop maize, followed by a CA maize–legume intercropping (40 earthworms) and lowest (nine earthworms) in conventionally tilled treatment. In both study locations CA monocrop maize and CA maize–legume intercrop gave higher water infiltration than the conventional treatment. Improvements in crop productivity, overall economic gain and soil quality have made CA an attractive system for farmers in Malawi and other areas with similar conditions. However, for extensive adoption of CA by smallholder farmers, cultural beliefs that crop production is possible without the ubiquitous ridge and furrow system and residue burning for mice hunting have to be overcome.

A Comparison of Four Spatial Regression Models for Yield Monitor Data: A Case Study from Argentina

The influence product markets, agglomeration, labor, infrastructure, and government fiscal attributes had on manufacturing investment flows in Indiana between 2000 and 2004 were estimated using Poisson regression, geographically weighted regression, and a spatial general linear model. Counties with access to urbanization economies, product markets, available labor, a high-quality workforce, and transport infrastructure were more likely to attract manufacturing investment. These effects were magnified to some extent when inter-county spatial effects were modeled. The distributional assumptions of the spatial models are different, but both methods are useful for understanding the spatial context of the factors influencing manufacturing investment flows.

Intensity of Precision Agriculture Technology Adoption by Cotton Producers

The location of ethanol plants is determined by infrastructure, product and input markets, fiscal attributes of local communities, and state and federal incentives. This empirical analysis uses probit regression along with spatial clustering methods to analyze investment activity of ethanol plants at the county level for the lower U.S. 48 states from 2000 to 2007. The availability of feedstock dominates the site selection decision. Other factors, such as access to navigable rivers or railroads, product markets, producer credit and excise tax exemptions, and methyl tertiary-butyl ether bans provided some counties with a comparative advantage in attracting ethanol plants.

Extreme Coefficients in Geographically Weighted Regression and Their Effects on Mapping

The gap between data analysis and site-specific recommendations has been identified as one of the key constraints on widespread adoption of precision agriculture technology. This disparity is in part due to the fact that analytical techniques available to understand crop GIS layers have lagged behind development of data gathering and storage technologies. Yield monitor, sensor and other spatially dense agronomic data is often autocorrelated, and this dependence among neighboring observations violates the assumptions of classical statistical analysis. Consequently, reliability of estimates may be compromised. Spatial regression analysis is one way to more fully exploit the information contained in spatially dense data. Spatial regression techniques can also adjust for bias and inefficiency caused by spatial autocorrelation. The objective of this paper is to compare four spatial regression methods that explicitly incorporate spatial correlation in the economic analysis of variable rate technology: (1) a regression approach adopted from the spatial econometric literature; (2) a polynomial trend regression approach; (3) a classical nearest neighbor analysis; and (4) a geostatistical approach. The data used in the analysis is from a variable rate nitrogen trial in the Córdoba Province, Argentina, 1999. The spatial regression approaches offered stronger statistical evidence of spatial heterogeneity of corn yield response to nitrogen than ordinary least squares. The spatial econometric analysis can be implemented on relatively small data sets that do not have enough observations for estimation of the semivariogram required by geostatistics. The nearest neighbor and polynomial trend analyses can be implemented with ordinary least squares routines that are available in GIS software. The main result of this study is that conclusions drawn from marginal analyses of this variable rate nitrogen trial were similar for each of the spatial regression models, although the assumptions about spatial process in each model are quite different.

RISK AND MAIZE-BASED CROPPING SYSTEMS FOR SMALLHOLDER MALAWI FARMERS USING CONSERVATION AGRICULTURE TECHNOLOGIES

Many studies on the adoption of precision technologies have generally used logit models to explain the adoption behavior of individuals. This study investigates factors affecting the intensity of precision agriculture technologies adopted by cotton farmers. Particular attention is given to the role of spatial yield variability on the number of precision farming technologies adopted, using a count data estimation procedure and farm-level data. Results indicate that farmers with more within-field yield variability adopted a higher number of precision agriculture technologies. Younger and better educated producers and the number of precision agriculture technologies used were significantly correlated. Finally, farmers using computers for management decisions also adopted a higher number of precision agriculture technologies.

Geographically weighted regression bandwidth selection and spatial autocorrelation: an empirical example using Chinese agriculture data

This study deals with the issue of extreme coefficients in geographically weighted regression (GWR) and their effects on mapping coefficients using three datasets with different spatial resolutions. We found that although GWR yields extreme coefficients regardless of the resolution of the dataset or types of kernel function: (1) GWR tends to generate extreme coefficients for less spatially dense datasets; (2) coefficient maps based on polygon data representing aggregated areal units are more sensitive to extreme coefficients; and (3) coefficient maps using bandwidths generated by a fixed calibration procedure are more vulnerable to the extreme coefficients than adaptive calibration.

Working Farm Participation and Acreage Enrollment in the Conservation Reserve Program

SUMMARY Agricultural production in southern Africa is constrained by numerous factors, including low soil fertility, frequent droughts and flooding, limited access to fertilizers and the use of unsustainable management techniques that increase soil erosion rates. Conservation agriculture (CA) is based on the principles of minimum soil disturbance, crop residue retention and crop rotations. CA systems have been proposed to alleviate the negative externalities associated with conventional crop management systems. This study was conducted to examine the riskiness of economic returns of CA technologies based on maize grain yield evaluated in 12 target communities in Malawi from 2005–2011. On average, maize grain yields on both CA treatments exceeded the conventional control treatment by 22.1–23.6%, with differences more distinct in low altitude areas with low rainfall and frequent seasonal dry spells. Stochastic dominance analysis suggest that CA technologies would be preferred by risk-averse farmers, with corresponding differences in risk premiums (compared to conventional maize production systems) ranging between US

Relationship between value of open space and distance from housing locations within a community

40 and US