Intensification of rice-fallow cropping systems in the Eastern Plateau region of India: diversifying cropping systems and climate risk mitigation

AbstractIdentifying options for the sustainable intensification of cropping systems in southern Africa under prevailing high climate risk is needed. With this in mind, we tested an intercropping system that combined the staple crop maize with lablab, a local but underutilised legume. Grain and biomass productivity was determined for four variants (i) sole maize (sole-maize), (ii) sole lablab (sole-lablab), (iii) maize/lablab with both crops sown simultaneously (intercropped-SP) and (iv) maize/lablab with lablab sown 28 days after the maize crop (intercropped-DP). Soil water and weather data were monitored and evaluated. The trial was conducted for two seasons (2015/2016 and 2016/2017) at two sites in the Limpopo Province, South Africa: Univen (847 mm rainfall, 29.2 °C maximum and 18.9 °C minimum temperature average for the cropping season over the years 2008–2017) and Syferkuil (491 mm rainfall, with 27.0 °C maximum and 14.8 °C minimum temperature). Analysis revealed three key results: The treatment with intercropped-SP had significantly lower maize yields (2320 kg ha−1) compared with maize in intercropped-DP (2865 kg ha−1) or sole-maize (2623 kg ha−1). As expected, maize yields in the El Niño affected in season 2015/2016 were on average 1688 kg ha−1 lower than in 2016/2017. Maize yields were significantly lower (957 kg ha−1) at Univen, the warmer site with higher rainfall, than at Syferkuil. In 2015/2016, maximum temperature at Univen exceeded 40 °C around anthesis. Furthermore, soil water was close to the estimated permanent wilting point (PWP) for most of the cropping season, which indicates possible water limitations. In Syferkuil, the soil water was maintained well above PWP. Lablab yields were low, around 500 ha−1, but stable as they were not affected by treatment across season and site. Overall, the study demonstrated that intercropped-DP appears to use available soil water more efficiently than sole maize. Intercropped-DP could therefore be considered as an option for sustainable intensification under high climate risk and resource-limited conditions for smallholders in southern Africa.

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Learning adaptation to climate change from past climate extremes:

International Journal of Climate Change Strategies and Management ◽

10.1108/ijccsm-09-2018-0065 ◽

2020 ◽

Vol 12 (1) ◽

pp. 128-146

Author(s):

Jeetendra Prakash Aryal ◽

M.L. Jat ◽

Tek Bahadur Sapkota ◽

Dil Bahadur Rahut ◽

Munmum Rai ◽

...

Keyword(s):

Climate Change ◽

Risk Mitigation ◽

Climate Extremes ◽

Climate Risk ◽

Adaptation To Climate Change ◽

Past Climate ◽

Content Type ◽

Wheat Production ◽

Climate Risks ◽

Ex Ante

Purpose Conservation agriculture-based wheat production system (CAW) can serve as an ex ante measure to minimize loss due to climate risks, especially the extreme rainfall during the wheat production season in India. This study aims to examine whether farmers learn from their past experiences of exposure to climate extremes and use the knowledge to better adapt to future climate extremes. Design/methodology/approach The authors used data collected from 184 farmers from Haryana over three consecutive wheat seasons from 2013-2014 to 2015-2016 and multivariate logit model to analyse the driver of the adoption of CAW as an ex ante climate risk mitigating strategies based on their learning and censored Tobit model to analyse the intensity of adoption of CAW as an ex ante climate risk mitigation strategy. Farmer’s knowledge and key barriers to the adoption of CAW were determined through focus group discussions. Findings The analysis shows that the majority of farmers who had applied CAW in the year 2014-2015 (a year with untimely excess rainfall during the wheat season) have continued to practice CAW and have increased the proportion of land area allocated to it. Many farmers shifted from CTW to CAW in 2015-2016. Practical implications While farmers now consider CAW as an ex ante measure to climate risks, a technology knowledge gap exists, which limits its adoption. Therefore, designing appropriate methods to communicate scientific evidence is crucial. Originality/value This paper uses three years panel data from 184 farm households in Haryana, India, together with focus groups discussions with farmers and interviews with key informants to assess if farmers learn adaptation to climate change from past climate extremes.

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Measuring and Modeling the Water Balance in Low-Rainfall Cropping Systems

Transactions of the ASABE ◽

10.13031/trans.12581 ◽

2017 ◽

Vol 60 (6) ◽

pp. 2097-2110 ◽

Cited By ~ 5

Author(s):

Anthony M. Whitbread ◽

Munir P. Hoffmann ◽

C. William Davoren ◽

Damian Mowat ◽

Jeffrey A. Baldock

Keyword(s):

Risk Management ◽

Water Balance ◽

Soil Water ◽

Cropping Systems ◽

Management Strategies ◽

Climate Risk ◽

Management Options ◽

Crop Models ◽

Available Water ◽

Plant Available Water

Abstract. In low-rainfall cropping systems, understanding the water balance, and in particular the storage of soil water in the rooting zone for use by crops, is considered critical for devising risk management strategies for grain-based farming. Crop-soil modeling remains a cost-effective option for understanding the interactions between rainfall, soil, and crop growth, from which management options can be derived. The objective of this study was to assess the error in the prediction of soil water content at key decision points in the season against continuous, multi-layer soil water measurements made with frequency domain reflectometry (FDR) probes in long-term experiments in the Mallee region of South Australia and New South Wales. Field estimates of the crop lower limit or drained upper limit were found to be more reliable than laboratory-based estimates, despite the fact that plant-available water capacity (PAWC) did not substantially differ between the methods. Using the Agricultural Production Systems sIMulator (APSIM) to simulate plant-available water over three-year rotations, predicted soil water was within 7 mm (PAWC 64 to 99 mm) of the measured data across all sowing events and rotations. Simulated (n = 46) wheat grain production resulted in a root mean square error (RMSE) of 492 kg ha-1, which is only marginally smaller than that of other field studies that derived soil water limits with less detailed methods. This study shows that using field-derived data of soil water limits and soil-specific settings for parameterization of other properties that determine soil evaporation and water redistribution enables APSIM to be widely applied for managing climate risk in low-rainfall environments. Keywords: APSIM, Climate risk management, Crop models, Decision support, Soil moisture.

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